[{"text": "If you already have a recent version of `gradio`, then the `gradio_client` is\nincluded as a dependency. But note that this documentation reflects the latest\nversion of the `gradio_client`, so upgrade if you\u2019re not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3)\nand is tested to work with **Python versions 3.9 or higher** :\n\n \n \n $ pip install --upgrade gradio_client\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Spaces\n\nStart by connecting instantiating a `Client` object and connecting it to a\nGradio app that is running on Hugging Face Spaces.\n\n \n \n from gradio_client import Client\n \n client = Client(\"abidlabs/en2fr\") a Space that translates from English to French\n\nYou can also connect to private Spaces by passing in your HF token with the\n`hf_token` parameter. You can get your HF token here:\n\n\n \n \n from gradio_client import Client\n \n client = Client(\"abidlabs/my-private-space\", hf_token=\"...\")\n\n", "heading1": "Connecting to a Gradio App on Hugging Face", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "use\n\nWhile you can use any public Space as an API, you may get rate limited by\nHugging Face if you make too many requests. For unlimited usage of a Space,\nsimply duplicate the Space to create a private Space, and then use it to make\nas many requests as you\u2019d like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this\nprocess simple (you\u2019ll need to pass in your [Hugging Face\ntoken](https://huggingface.co/settings/tokens) or be logged in using the\nHugging Face CLI):\n\n \n \n import os\n from gradio_client import Client, file\n \n HF_TOKEN = os.environ.get(\"HF_TOKEN\")\n \n client = Client.duplicate(\"abidlabs/whisper\", hf_token=HF_TOKEN)\n client.predict(file(\"audio_sample.wav\"))\n \n >> \"This is a test of the whisper speech recognition model.\"\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_\ncreate a new Space. Instead, the Client will attach to the previously-created\nSpace. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well,\nand your Hugging Face account will get billed based on the price of the GPU.\nTo minimize charges, your Space will automatically go to sleep after 1 hour of\ninactivity. You can also set the hardware using the `hardware` parameter of\n`duplicate()`.\n\n", "heading1": "Duplicating a Space for private", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "app\n\nIf your app is running somewhere else, just provide the full URL instead,\nincluding the \u201chttp://\u201d or \u201chttps://\u201c. Here\u2019s an example of making predictions\nto a Gradio app that is running on a share URL:\n\n \n \n from gradio_client import Client\n \n client = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n\n", "heading1": "Connecting a general Gradio", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are\navailable to you by calling the `Client.view_api()` method. For the Whisper\nSpace, we see the following:\n\n \n \n Client.predict() Usage Info\n ---------------------------\n Named API endpoints: 1\n \n - predict(audio, api_name=\"/predict\") -> output\n Parameters:\n - [Audio] audio: filepath (required) \n Returns:\n - [Textbox] output: str \n\nWe see that we have 1 API endpoint in this space, and shows us how to use the\nAPI endpoint to make a prediction: we should call the `.predict()` method\n(which we will explore below), providing a parameter `input_audio` of type\n`str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()`\nmethod. Although this isn\u2019t necessary if a Gradio app has only 1 named\nendpoint, it does allow us to call different endpoints in a single app if they\nare available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the\n\u201cUse via API\u201d link in the footer of the Gradio app, which shows us the same\ninformation, along with example usage.\n\n![](https://huggingface.co/datasets/huggingface/documentation-\nimages/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \u201cAPI Recorder\u201d that lets you interact with\nthe Gradio UI normally and converts your interactions into the corresponding\ncode to run with the Python Client.\n\n", "heading1": "The \u201cView API\u201d Page", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()`\nfunction with the appropriate arguments:\n\n \n \n from gradio_client import Client\n \n client = Client(\"abidlabs/en2fr\", api_name='/predict')\n client.predict(\"Hello\")\n \n >> Bonjour\n\nIf there are multiple parameters, then you should pass them as separate\narguments to `.predict()`, like this:\n\n \n \n from gradio_client import Client\n \n client = Client(\"gradio/calculator\")\n client.predict(4, \"add\", 5)\n \n >> 9.0\n\nIt is recommended to provide key-word arguments instead of positional\narguments:\n\n \n \n from gradio_client import Client\n \n client = Client(\"gradio/calculator\")\n client.predict(num1=4, operation=\"add\", num2=5)\n \n >> 9.0\n\nThis allows you to take advantage of default arguments. For example, this\nSpace includes the default value for the Slider component so you do not need\nto provide it when accessing it with the client.\n\n \n \n from gradio_client import Client\n \n client = Client(\"abidlabs/image_generator\")\n client.predict(text=\"an astronaut riding a camel\")\n\nThe default value is the initial value of the corresponding Gradio component.\nIf the component does not have an initial value, but if the corresponding\nargument in the predict function has a default value of `None`, then that\nparameter is also optional in the client. Of course, if you\u2019d like to override\nit, you can include it as well:\n\n \n \n from gradio_client import Client\n \n client = Client(\"abidlabs/image_generator\")\n client.predict(text=\"an astronaut riding a camel\", steps=25)\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL\nto the file enclosed within `gradio_client.file()`. This takes care of\nuploading the file to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n \n \n from gradio_client import Client, file\n \n client = Client(\"abidlabs/whisper\")\n client.predict(\n ", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": " to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n \n \n from gradio_client import Client, file\n \n client = Client(\"abidlabs/whisper\")\n client.predict(\n audio=file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n )\n \n >> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Oe should note that `.predict()` is a _blocking_ operation as it waits for the\noperation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background\nuntil you need the results of the prediction. You can do this by creating a\n`Job` instance using the `.submit()` method, and then later calling\n`.result()` on the job to get the result. For example:\n\n \n \n from gradio_client import Client\n \n client = Client(space=\"abidlabs/en2fr\")\n job = client.submit(\"Hello\", api_name=\"/predict\") This is not blocking\n \n Do something else\n \n job.result() This is blocking\n \n >> Bonjour\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the\njob has completed running, like this:\n\n \n \n from gradio_client import Client\n \n def print_result(x):\n print(\"The translated result is: {x}\")\n \n client = Client(space=\"abidlabs/en2fr\")\n \n job = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n \n Do something else\n \n >> The translated result is: Bonjour\n \n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` object also allows you to get the status of the running job by\ncalling the `.status()` method. This returns a `StatusUpdate` object with the\nfollowing attributes: `code` (the status code, one of a set of defined strings\nrepresenting the status. See the `utils.Status` class), `rank` (the current\nposition of this job in the queue), `queue_size` (the total queue size), `eta`\n(estimated time this job will complete), `success` (a boolean representing\nwhether the job completed successfully), and `time` (the time that the status\nwas generated).\n\n \n \n from gradio_client import Client\n \n client = Client(src=\"gradio/calculator\")\n job = client.submit(5, \"add\", 4, api_name=\"/predict\")\n job.status()\n \n >> \n\n_Note_ : The `Job` class also has a `.done()` instance method which returns a\nboolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that\nhave been queued but not started. For example, if you run:\n\n \n \n client = Client(\"abidlabs/whisper\")\n job1 = client.submit(file(\"audio_sample1.wav\"))\n job2 = client.submit(file(\"audio_sample2.wav\"))\n job1.cancel() will return False, assuming the job has started\n job2.cancel() will return True, indicating that the job has been canceled\n\nIf the first job has started processing, then it will not be canceled. If the\nsecond job has not yet started, it will be successfully canceled and removed\nfrom the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a\nseries of values. You can get the series of values that have been returned at\nany time from such a generator endpoint by running `job.outputs()`:\n\n \n \n from gradio_client import Client\n \n client = Client(src=\"gradio/count_generator\")\n job = client.submit(3, api_name=\"/count\")\n while not job.done():\n time.sleep(0.1)\n job.outputs()\n \n >> ['0', '1', '2']\n\nNote that running `job.result()` on a generator endpoint only gives you the\n_first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the\nresults of a generator function as they are returned from the endpoint. Here\u2019s\nthe equivalent example using the `Job` as a generator:\n\n \n \n from gradio_client import Client\n \n client = Client(src=\"gradio/count_generator\")\n job = client.submit(3, api_name=\"/count\")\n \n for o in job:\n print(o)\n \n >> 0\n >> 1\n >> 2\n\nYou can also cancel jobs that that have iterative outputs, in which case the\njob will finish as soon as the current iteration finishes running.\n\n \n \n from gradio_client import Client\n import time\n \n client = Client(\"abidlabs/test-yield\")\n job = client.submit(\"abcdef\")\n time.sleep(3)\n job.cancel() job cancels after 2 iterations\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-\nin-blocks), which provides a way for demos to persist information from user\ninteractions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that\na user has submitted in a `gr.State` component. When a user submits a new\nword, it is added to the state, and the number of previous occurrences of that\nword is displayed:\n\n \n \n import gradio as gr\n \n def count(word, list_of_words):\n return list_of_words.count(word), list_of_words + [word]\n \n with gr.Blocks() as demo:\n words = gr.State([])\n textbox = gr.Textbox()\n number = gr.Number()\n textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n \n demo.launch()\n\nIf you were to connect this this Gradio app using the Python Client, you would\nnotice that the API information only shows a single input and output:\n\n \n \n Client.predict() Usage Info\n ---------------------------\n Named API endpoints: 1\n \n - predict(word, api_name=\"/count\") -> value_31\n Parameters:\n - [Textbox] word: str (required) \n Returns:\n - [Number] value_31: float \n\nThat is because the Python client handles state automatically for you \u2014 as you\nmake a series of requests, the returned state from one request is stored\ninternally and automatically supplied for the subsequent request. If you\u2019d\nlike to reset the state, you can do that by calling `Client.reset_session()`.\n\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The main Client class for the Python client. This class is used to connect\nto a remote Gradio app and call its API endpoints. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "from gradio_client import Client\n \n client = Client(\"abidlabs/whisper-large-v2\") connecting to a Hugging Face Space\n client.predict(\"test.mp4\", api_name=\"/predict\")\n >> What a nice recording! returns the result of the remote API call\n \n client = Client(\"https://bec81a83-5b5c-471e.gradio.live\") connecting to a temporary Gradio share URL\n job = client.submit(\"hello\", api_name=\"/predict\") runs the prediction in a background thread\n job.result()\n >> 49 returns the result of the remote API call (blocking call)\n\n", "heading1": "Example usage", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n src: str\n\neither the name of the Hugging Face Space to load, (e.g. \"abidlabs/whisper-\nlarge-v2\") or the full URL (including \"http\" or \"https\") of the hosted Gradio\napp to load (e.g. \"http://mydomain.com/app\" or\n\"https://bec81a83-5b5c-471e.gradio.live/\").\n\n\n \n \n token: str | None\n\ndefault `= None`\n\noptional Hugging Face token to use to access private Spaces. By default, the\nlocally saved token is used if there is one. Find your tokens here:\nhttps://huggingface.co/settings/tokens.\n\n\n \n \n max_workers: int\n\ndefault `= 40`\n\nmaximum number of thread workers that can be used to make requests to the\nremote Gradio app simultaneously.\n\n\n \n \n verbose: bool\n\ndefault `= True`\n\nwhether the client should print statements to the console.\n\n\n \n \n auth: tuple[str, str] | None\n\ndefault `= None`\n\n\n \n \n httpx_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nadditional keyword arguments to pass to `httpx.Client`, `httpx.stream`,\n`httpx.get` and `httpx.post`. This can be used to set timeouts, proxies, http\nauth, etc.\n\n\n \n \n headers: dict[str, str] | None\n\ndefault `= None`\n\nadditional headers to send to the remote Gradio app on every request. By\ndefault only the HF authorization and user-agent headers are sent. This\nparameter will override the default headers if they have the same keys.\n\n\n \n \n download_files: str | Path | Literal[False]\n\ndefault `= \"/tmp/gradio\"`\n\ndirectory where the client should download output files on the local machine\nfrom the remote API. By default, uses the value of the GRADIO_TEMP_DIR\nenvironment variable which, if not set by the user, is a temporary directory\non your machine. If False, the client does not download files and returns a\nFileData dataclass object with the filepath on the remote machine instead.\n\n\n \n \n ssl_verify: bool\n\ndefault `= True`\n\nif False, skips certificate validation which allows the client to connect to\nGradio apps that are using self-signed ce", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "n the remote machine instead.\n\n\n \n \n ssl_verify: bool\n\ndefault `= True`\n\nif False, skips certificate validation which allows the client to connect to\nGradio apps that are using self-signed certificates.\n\n\n \n \n analytics_enabled: bool\n\ndefault `= True`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n\n \n \n oauth_token: str | None\n\ndefault `= None`\n\noptional Hugging Face token for the app to act on your behalf, for endpoints\nwhose function takes a `gr.OAuthToken`. Unlike `token`, which only\nauthenticates you to the app, this is passed to the app's code, so it is sent\nonly to endpoints that declare they need it \u2014 `view_api()` marks those. It is\nnever sent anywhere else, and is not inferred from your locally saved token.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Client component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListeners\n\n \n \n Client.predict(fn, \u00b7\u00b7\u00b7)\n\nCalls the Gradio API and returns the result (this is a blocking call).\nArguments can be provided as positional arguments or as keyword arguments\n(latter is recommended).
\n\n \n \n Client.submit(fn, \u00b7\u00b7\u00b7)\n\nCreates and returns a Job object which calls the Gradio API in a background\nthread. The job can be used to retrieve the status and result of the remote\nAPI call. Arguments can be provided as positional arguments or as keyword\narguments (latter is recommended).
\n\n \n \n Client.view_api(fn, \u00b7\u00b7\u00b7)\n\nPrints the usage info for the API. If the Gradio app has multiple API\nendpoints, the usage info for each endpoint will be printed separately. If\nreturn_format=\"dict\" the info is returned in dictionary format, as shown in\nthe example below.
\n\n \n \n Client.duplicate(fn, \u00b7\u00b7\u00b7)\n\nDuplicates a Hugging Face Space under your account and returns a Client object\nfor the new Space. No duplication is created if the Space already exists in\nyour account (to override this, provide a new name for the new Space using\n`to_id`). To use this method, you must provide an `token` or be logged in via\nthe Hugging Face Hub CLI.
The new Space will be private by default and\nuse the same hardware as the original Space. This can be changed by using the\n`private` and `hardware` parameters. For hardware upgrades (beyond the basic\nCPU tier), you may be required to provide billing information on Hugging Face:\n
\n\nEvent Parameters\n\nPar", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "eters. For hardware upgrades (beyond the basic\nCPU tier), you may be required to provide billing information on Hugging Face:\n
\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n args: \n\nThe positional arguments to pass to the remote API endpoint. The order of the\narguments must match the order of the inputs in the Gradio app.\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\nThe name of the API endpoint to call starting with a leading slash, e.g.\n\"/predict\". Does not need to be provided if the Gradio app has only one named\nAPI endpoint.\n\n\n \n \n fn_index: int | None\n\ndefault `= None`\n\nAs an alternative to api_name, this parameter takes the index of the API\nendpoint to call, e.g. 0. Both api_name and fn_index can be provided, but if\nthey conflict, api_name will take precedence.\n\n\n \n \n headers: dict[str, str] | None\n\ndefault `= None`\n\nAdditional headers to send to the remote Gradio app on this request. This\nparameter will overrides the headers provided in the Client constructor if\nthey have the same keys.\n\n\n \n \n kwargs: \n\nThe keyword arguments to pass to the remote API endpoint.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "**Stream From a Gradio app in 5 lines**\n\n \n\nUse the `submit` method to get a job you can iterate over.\n\n \n\nIn python:\n\n \n \n from gradio_client import Client\n \n client = Client(\"gradio/llm_stream\")\n \n for result in client.submit(\"What's the best UI framework in Python?\"):\n print(result)\n\n \n\nIn typescript:\n\n \n \n import { Client } from \"@gradio/client\";\n \n const client = await Client.connect(\"gradio/llm_stream\")\n const job = client.submit(\"/predict\", {\"text\": \"What's the best UI framework in Python?\"})\n \n for await (const msg of job) console.log(msg.data)\n\n \n\n**Use the same keyword arguments as the app**\n\n \nIn the examples below, the upstream app has a function with parameters called\n`message`, `system_prompt`, and `tokens`. We can see that the client `predict`\ncall uses the same arguments.\n\nIn python:\n\n \n \n from gradio_client import Client\n \n client = Client(\"http://127.0.0.1:7860/\")\n result = client.predict(\n \t\tmessage=\"Hello!!\",\n \t\tsystem_prompt=\"You are helpful AI.\",\n \t\ttokens=10,\n \t\tapi_name=\"/chat\"\n )\n print(result)\n\nIn typescript:\n\n \n \n import { Client } from \"@gradio/client\";\n \n const client = await Client.connect(\"http://127.0.0.1:7860/\");\n const result = await client.predict(\"/chat\", { \t\t\n \t\tmessage: \"Hello!!\", \t\t\n \t\tsystem_prompt: \"Hello!!\", \t\t\n \t\ttokens: 10, \n });\n \n console.log(result.data);\n\n \n\n**Better Error Messages**\n\n \nIf something goes wrong in the upstream app, the client will raise the same\nexception as the app provided that `show_error=True` in the original app's\n`launch()` function, or it's a `gr.Error` exception.\n\n", "heading1": "Ergonomic API \ud83d\udc86", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Anything you can do in the UI, you can do with the client:\n\n * \ud83d\udd10Authentication\n * \ud83d\uded1 Job Cancelling\n * \u2139\ufe0f Access Queue Position and API\n * \ud83d\udcd5 View the API information\n\n \nHere's an example showing how to display the queue position of a pending job:\n\n \n \n from gradio_client import Client\n \n client = Client(\"gradio/diffusion_model\")\n \n job = client.submit(\"A cute cat\")\n while not job.done():\n status = job.status()\n print(f\"Current in position {status.rank} out of {status.queue_size}\")\n\n", "heading1": "Transparent Design \ud83e\ude9f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "The client can run from pretty much any python and javascript environment\n(node, deno, the browser, Service Workers). \nHere's an example using the client from a Flask server using gevent:\n\n \n \n from gevent import monkey\n monkey.patch_all()\n \n from gradio_client import Client\n from flask import Flask, send_file\n import time\n \n app = Flask(__name__)\n \n imageclient = Client(\"gradio/diffusion_model\")\n \n @app.route(\"/gen\")\n def gen():\n result = imageclient.predict(\n \"A cute cat\",\n api_name=\"/predict\"\n )\n return send_file(result)\n \n if __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=5000)\n\n", "heading1": "Portable Design \u26fa\ufe0f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Changes\n\n \n\n**Python**\n\n * The `serialize` argument of the `Client` class was removed and has no effect.\n * The `upload_files` argument of the `Client` was removed.\n * All filepaths must be wrapped in the `handle_file` method. For example, `caption = client.predict(handle_file('./dog.jpg'))`.\n * The `output_dir` argument was removed. It is not specified in the `download_files` argument.\n\n \n\n**Javascript**\n\n \nThe client has been redesigned entirely. It was refactored from a function\ninto a class. An instance can now be constructed by awaiting the `connect`\nmethod.\n\n \n \n const app = await Client.connect(\"gradio/whisper\")\n\nThe app variable has the same methods as the python class (`submit`,\n`predict`, `view_api`, `duplicate`).\n\n", "heading1": "v1.0 Migration Guide and Breaking", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "A Job is a wrapper over the Future class that represents a prediction call\nthat has been submitted by the Gradio client. This class is not meant to be\ninstantiated directly, but rather is created by the Client.submit() method. \nA Job object includes methods to get the status of the prediction call, as\nwell to get the outputs of the prediction call. Job objects are also iterable,\nand can be used in a loop to get the outputs of prediction calls as they\nbecome available for generator endpoints.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n future: Future\n\nThe future object that represents the prediction call, created by the\nClient.submit() method\n\n\n \n \n communicator: Communicator | None\n\ndefault `= None`\n\nThe communicator object that is used to communicate between the client and the\nbackground thread running the job\n\n\n \n \n verbose: bool\n\ndefault `= True`\n\nWhether to print any status-related messages to the console\n\n\n \n \n space_id: str | None\n\ndefault `= None`\n\nThe space ID corresponding to the Client object that created this Job object\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Job component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListeners\n\n \n \n Job.result(fn, \u00b7\u00b7\u00b7)\n\nReturn the result of the call that the future represents. Raises\nCancelledError: If the future was cancelled, TimeoutError: If the future\ndidn't finish executing before the given timeout, and Exception: If the call\nraised then that exception will be raised.
\n\n \n \n Job.outputs(fn, \u00b7\u00b7\u00b7)\n\nReturns a list containing the latest outputs from the Job.
If the\nendpoint has multiple output components, the list will contain a tuple of\nresults. Otherwise, it will contain the results without storing them in\ntuples.
For endpoints that are queued, this list will contain the final\njob output even if that endpoint does not use a generator function.
\n\n \n \n Job.status(fn, \u00b7\u00b7\u00b7)\n\nReturns the latest status update from the Job in the form of a StatusUpdate\nobject, which contains the following fields: code, rank, queue_size, success,\ntime, eta, and progress_data.
progress_data is a list of updates emitted\nby the gr.Progress() tracker of the event handler. Each element of the list\nhas the following fields: index, length, unit, progress, desc. If the event\nhandler does not have a gr.Progress() tracker, the progress_data field will be\nNone.
\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n timeout: float | None\n\ndefault `= None`\n\nThe number of seconds to wait for the result if the future isn't done. If\nNone, then there is no limit on the wait time.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "ZeroGPU\n\nZeroGPU spaces are rate-limited to ensure that a single user does not hog all\nof the available GPUs. The limit is controlled by a special token that the\nHugging Face Hub infrastructure adds to all incoming requests to Spaces. This\ntoken is a request header called `X-IP-Token` and its value changes depending\non the user who makes a request to the ZeroGPU space.\n\n \n\nLet\u2019s say you want to create a space (Space A) that uses a ZeroGPU space\n(Space B) programmatically. Normally, calling Space B from Space A with the\nGradio Python client would quickly exhaust Space B\u2019s rate limit, as all the\nrequests to the ZeroGPU space would be missing the `X-IP-Token` request header\nand would therefore be treated as unauthenticated.\n\nIn order to avoid this, we need to extract the `X-IP-Token` of the user using\nSpace A before we call Space B programmatically. Where possible, specifically\nin the case of functions that are passed into event listeners directly, Gradio\nautomatically extracts the `X-IP-Token` from the incoming request and passes\nit into the Gradio Client. But if the Client is instantiated outside of such a\nfunction, then you may need to pass in the token manually.\n\nHow to do this will be explained in the following section.\n\n", "heading1": "Explaining Rate Limits for", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "Token\n\nIn the following hypothetical example, when a user presses enter in the\ntextbox, the `generate()` function is called, which calls a second function,\n`text_to_image()`. Because the Gradio Client is being instantiated indirectly,\nin `text_to_image()`, we will need to extract their token from the `X-IP-\nToken` header of the incoming request. We will use this header when\nconstructing the gradio client.\n\n \n \n import gradio as gr\n from gradio_client import Client\n \n def text_to_image(prompt, request: gr.Request):\n x_ip_token = request.headers['x-ip-token']\n client = Client(\"hysts/SDXL\", headers={\"x-ip-token\": x_ip_token})\n img = client.predict(prompt, api_name=\"/predict\")\n return img\n \n def generate(prompt, request: gr.Request):\n prompt = prompt[:300]\n return text_to_image(prompt, request)\n \n with gr.Blocks() as demo:\n image = gr.Image()\n prompt = gr.Textbox(max_lines=1)\n prompt.submit(generate, [prompt], [image])\n \n demo.launch()\n\n", "heading1": "Avoiding Rate Limits by Manually Passing an IP", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "Sets up an event listener that triggers a function when the specified\nevent(s) occur. This is especially useful when the same function should be\ntriggered by multiple events. Only a single API endpoint is generated for all\nevents in the triggers list. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "import gradio as gr\n \n with gr.Blocks() as demo:\n with gr.Row():\n input = gr.Textbox()\n button = gr.Button(\"Submit\")\n output = gr.Textbox()\n gr.on(\n triggers=[button.click, input.submit],\n fn=lambda x: x,\n inputs=[input],\n outputs=[output]\n )\n \n demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n triggers: list[Trigger] | Trigger | None\n\ndefault `= None`\n\nList of triggers to listen to, e.g. [btn.click, number.change]. If None, will\nrun on app load and changes to any inputs.\n\n\n \n \n fn: Callable[..., Any] | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n \n \n inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n \n \n inputs_kwargs: dict[str, Component | BlockContext] | None\n\ndefault `= None`\n\nDictionary mapping function parameter names to gradio.components. The\ncomponent values are passed to the function as keyword arguments.\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\ncontrols the visibility and accessibility of this endpoint. Can be \"public\"\n(shown in API docs and callable by clients), \"private\" (hidden from API docs\nand not callable by the Gradio client libraries), or \"undocumented\" (hidden\nfrom API docs but callable by clients and via gr.load). If fn is None,\napi_visibility will automatically be set to \"private\".\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string or None. If\nset to a string, the endpoint will be exposed in the API docs with t", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": " \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string or None. If\nset to a string, the endpoint will be exposed in the API docs with the given\nname. If None (default), the name of the function will be used as the API\nendpoint.\n\n\n \n \n api_description: str | None | Literal[False]\n\ndefault `= None`\n\n\n \n \n scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n \n \n show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all,\n\n\n \n \n show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n \n \n queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n \n \n max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n \n \n preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preproc", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "ault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n \n \n preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n \n \n postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n \n \n cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n \n \n trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n \n \n js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend JavaScript to run before 'fn', provided as either a function\nor a raw code string. A function receives the values of 'inputs' as arguments;\nraw code can access them through `arguments`. Return a list of values for the\noutput components.\n\n\n \n \n concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the defau", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "mum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n \n \n concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n \n \n time_limit: int | None\n\ndefault `= None`\n\nThe time limit for the function to run. Parameter only used for the\n`.stream()` event.\n\n\n \n \n stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for the `.stream()` event.\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n \n \n validator: Callable | None\n\ndefault `= None`\n\nOptional validation function to run before the main function. If provided,\nthis function will be executed first with queue=False, and only if it\ncompletes successfully will the main function be called. The validator\nreceives the same inputs as the main function, including the same keyword\narguments when `inputs_kwargs` is used, so its signature must accept those\nkeyword names. It should return a `gr.validate()` for each input value.\n\n[Blocks and Event Listeners](../../guides/blocks-and-event-listeners)\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Creates a video component that can be used to upload/record videos (as an\ninput) or display videos (as an output). For the video to be playable in the\nbrowser it must have a compatible container and codec combination. Allowed\ncombinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with\nvp9 codec. If the component detects that the output video would not be\nplayable in the browser it will attempt to convert it to a playable mp4 video.\nIf the conversion fails, the original video is returned. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "**Using Video as an input component.**\n\nHow Video will pass its value to your function:\n\nType: `str | None`\n\nPasses the uploaded video as a `str` filepath or URL whose extension can be\nmodified by `format`.\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(\n value: str | None\n ):\n process value from the Video component\n return \"prediction\"\n \n interface = gr.Interface(predict, gr.Video(), gr.Textbox())\n interface.launch()\n \n \n\n \n\n**Using Video as an output component**\n\nHow Video expects you to return a value:\n\nType: `str | Path | None`\n\nExpects one of either:\n\n * a `str` or `pathlib.Path` filepath to a video which is displayed\n\n * a `Tuple[str | pathlib.Path, str | pathlib.Path | None]` where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(text) -> str | Path | None\n process value to return to the Video component\n return value\n \n interface = gr.Interface(predict, gr.Textbox(), gr.Video())\n interface.launch()\n \n \n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n value: str | Path | Callable | None\n\ndefault `= None`\n\npath or URL for the default value that Video component is going to take. Or\ncan be callable, in which case the function will be called whenever the app\nloads to set the initial value of the component.\n\n\n \n \n format: str | None\n\ndefault `= None`\n\nthe file extension with which to save video, such as 'avi' or 'mp4'. This\nparameter applies both when this component is used as an input to determine\nwhich file format to convert user-provided video to, and when this component\nis used as an output to determine the format of video returned to the user. If\nNone, no file format conversion is done and the video is kept as is. Use 'mp4'\nto ensure browser playability.\n\n\n \n \n sources: list[Literal['upload', 'webcam']] | Literal['upload', 'webcam'] | None\n\ndefault `= None`\n\nlist of sources permitted for video. \"upload\" creates a box where user can\ndrop a video file, \"webcam\" allows user to record a video from their webcam.\nIf None, defaults to both [\"upload, \"webcam\"].\n\n\n \n \n height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n \n \n width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n \n \n label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n \n \n every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to rec", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "nd\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n \n \n every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n \n \n inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n \n \n show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n \n \n container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n \n \n scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n \n \n min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n \n \n interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a video; if False, can only be used to\ndisplay videos. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n \n \n visible: bool | Literal['hidden']\n\ndefault `= True`\n\nIf False, component will be hidden. If \"hidden\", component will be visually\nhidden and not take up space in the layout but still exist in the DOM\n\n\n \n \n elem_id: str | None\n\ndefault `= None`\n\nan optional string that ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "be hidden. If \"hidden\", component will be visually\nhidden and not take up space in the layout but still exist in the DOM\n\n\n \n \n elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n \n \n elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n \n \n render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n \n \n webcam_options: WebcamOptions | None\n\ndefault `= None`\n\nA `gr.WebcamOptions` instance that allows developers to specify custom media\nconstraints for the webcam stream. This parameter provides flexibility to\ncontrol the video stream's properties, such as resolution and front or rear\ncamera on mobile devices. See $demo/webcam_constraints\n\n\n \n \n include_audio: bool | None\n\ndefault `= None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n \n \n autoplay: bool\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ult `= None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n \n \n autoplay: bool\n\ndefault `= False`\n\nwhether to automatically play the video when the component is used as an\noutput. Note: browsers will not autoplay video files if the user has not\ninteracted with the page yet.\n\n\n \n \n buttons: list[Literal['download', 'share'] | Button] | None\n\ndefault `= None`\n\nA list of buttons to show in the top right corner of the component. Valid\noptions are \"download\", \"share\", or a gr.Button() instance. The \"download\"\nbutton allows the user to save the video to their device. The \"share\" button\nallows the user to share the video via Hugging Face Spaces Discussions. Custom\ngr.Button() instances will appear in the toolbar with their configured icon\nand/or label, and clicking them will trigger any .click() events registered on\nthe button. By default, no buttons are shown if the component is interactive\nand both buttons are shown if the component is not interactive.\n\n\n \n \n loop: bool\n\ndefault `= False`\n\nif True, the video will loop when it reaches the end and continue playing from\nthe beginning.\n\n\n \n \n streaming: bool\n\ndefault `= False`\n\nwhen used set as an output, takes video chunks yielded from the backend and\ncombines them into one streaming video output. Each chunk should be a video\nfile with a .ts extension using an h.264 encoding. Mp4 files are also accepted\nbut they will be converted to h.264 encoding.\n\n\n \n \n watermark: WatermarkOptions | None\n\ndefault `= None`\n\nA `gr.WatermarkOptions` instance that includes an image file and position to\nbe used as a watermark on the video. The image is not scaled and is displayed\non the provided position on the video. Valid formats for the image are: jpeg,\npng.\n\n\n \n \n subtitles: str | Path | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA subtitle file (srt, vtt, or json) for the ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "d position on the video. Valid formats for the image are: jpeg,\npng.\n\n\n \n \n subtitles: str | Path | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA subtitle file (srt, vtt, or json) for the video, or a list of subtitle\ndictionaries in the format [{\"text\": str, \"timestamp\": [start, end]}] where\ntimestamps are in seconds. JSON files should contain an array of subtitle\nobjects.\n\n\n \n \n playback_position: float\n\ndefault `= 0`\n\nThe starting playback position in seconds. This value is also updated as the\nvideo plays, reflecting the current playback position.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Shortcuts\n\n \n \n gradio.Video\n\nInterface String Shortcut `\"video\"`\n\nInitialization Uses default values\n\n \n \n gradio.PlayableVideo\n\nInterface String Shortcut `\"playablevideo\"`\n\nInitialization Uses format=\"mp4\"\n\n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "video_identity_2\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Video component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListeners\n\n \n \n Video.change(fn, \u00b7\u00b7\u00b7)\n\nTriggered when the value of the Video changes either because of user input\n(e.g. a user types in a textbox) OR because of a function update (e.g. an\nimage receives a value from the output of an event trigger). See `.input()`\nfor a listener that is only triggered by user input.\n\n \n \n Video.clear(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user clears the Video using the clear\nbutton for the component.\n\n \n \n Video.start_recording(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user starts recording with the Video.\n\n \n \n Video.stop_recording(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user stops recording with the Video.\n\n \n \n Video.stop(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user reaches the end of the media playing\nin the Video.\n\n \n \n Video.play(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user plays the media in the Video.\n\n \n \n Video.pause(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the media in the Video stops for any reason.\n\n \n \n Video.end(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user reaches the end of the media playing\nin the Video.\n\n \n \n Video.upload(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user uploads a file into the Video.\n\n \n \n Video.input(fn, \u00b7\u00b7\u00b7)\n\nThis listener is triggered when the user changes the value of the Video.\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "d when the user changes the value of the Video.\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n \n \n inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string or None. If\nset to a string, the endpoint will be exposed in the API docs with the given\nname. If None (default), the name of the function will be used as the API\nendpoint.\n\n\n \n \n api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n \n \n scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n \n \n show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runti", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "s: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n \n \n show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n \n \n queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n \n \n max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n \n \n preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n \n \n postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n \n \n cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event,", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n \n \n trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n \n \n js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend JavaScript to run before 'fn', provided as either a function\nor a raw code string. A function receives the values of 'inputs' and 'outputs'\nas arguments; raw code can access them through `arguments`. Return a list of\nvalues for the output components.\n\n\n \n \n concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n \n \n concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\ncontrols the visibility and accessibility of this endpoint. Can be \"public", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "st set concurrency_limit.\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\ncontrols the visibility and accessibility of this endpoint. Can be \"public\"\n(shown in API docs and callable by clients), \"private\" (hidden from API docs\nand not callable by the Gradio client libraries), or \"undocumented\" (hidden\nfrom API docs but callable by clients and via gr.load). If fn is None,\napi_visibility will automatically be set to \"private\".\n\n\n \n \n time_limit: int | None\n\ndefault `= None`\n\n\n \n \n stream_every: float\n\ndefault `= 0.5`\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n\n \n \n validator: Callable | None\n\ndefault `= None`\n\nOptional validation function to run before the main function. If provided,\nthis function will be executed first with queue=False, and only if it\ncompletes successfully will the main function be called. The validator\nreceives the same inputs as the main function, including the same keyword\narguments when `inputs_kwargs` is used, so its signature must accept those\nkeyword names. It should return a `gr.validate()` for each input value.\n\n\n \n \n inputs_kwargs: dict[str, Component | BlockContext] | None\n\ndefault `= None`\n\nDictionary mapping function parameter names to gradio.components. The\ncomponent values are passed to the function as keyword arguments.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n \n \n mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n \n \n constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Validates that the audio length is within the specified min and max length (in\nseconds). You can use this to construct a validator that will check if the\nuser-provided audio is either too short or too long.\n\n \n \n import gradio as gr\n demo = gr.Interface(\n lambda x: x,\n inputs=\"video\",\n outputs=\"video\",\n validator=lambda video: gr.validators.is_video_correct_length(video, min_length=1, max_length=5)\n )\n demo.launch()\n\nInitialization\n\nParameters \u25bc\n\n\n \n \n video: \n\nThe path to the video file.\n\n\n \n \n min_length: float | None\n\nMinimum length of video in seconds. If None, no minimum length check is\nperformed.\n\n\n \n \n max_length: float | None\n\nMaximum length of video in seconds. If None, no maximum length check is\nperformed.\n\n[Streaming Inputs](../../guides/streaming-inputs/)[Streaming\nOutputs](../../guides/streaming-outputs/)[Object Detection From\nVideo](../../guides/object-detection-from-video/)\n\n", "heading1": "is_video_correct_length", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Creates a bar plot component to display data from a pandas DataFrame. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "**Using BarPlot as an input component.**\n\nHow BarPlot will pass its value to your function:\n\nType: `PlotData | None`\n\nThe data to display in a line plot.\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(\n value: PlotData | None\n ):\n process value from the BarPlot component\n return \"prediction\"\n \n interface = gr.Interface(predict, gr.BarPlot(), gr.Textbox())\n interface.launch()\n \n \n\n \n\n**Using BarPlot as an output component**\n\nHow BarPlot expects you to return a value:\n\nType: `pd.DataFrame | dict | None`\n\nExpects a pandas DataFrame containing the data to display in the line plot.\nThe DataFrame should contain at least two columns:\n\n * one for the x-axis (corresponding to this component's `x` argument)\n\n * one for the y-axis (corresponding to `y`).\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(text) -> pd.DataFrame | dict | None\n process value to return to the BarPlot component\n return value\n \n interface = gr.Interface(predict, gr.Textbox(), gr.BarPlot())\n interface.launch()\n \n \n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n \n \n x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n \n \n y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n \n \n color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n \n \n title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n \n \n x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n \n \n y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n \n \n color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n \n \n x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n \n \n y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n \n \n color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n \n \n colors_in_legend: list[str] | None\n\ndefault `= None`\n\nList containing column names of the series to show in the legend. By default,\nall series are shown.\n\n\n \n \n x_lim: list[float | None] | None\n\ndefault `= None`\n\nA tuple or list containing ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "ne`\n\nList containing column names of the series to show in the legend. By default,\nall series are shown.\n\n\n \n \n x_lim: list[float | None] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum value. If x column is datetime type, x_lim\nshould be timestamps.\n\n\n \n \n y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n \n \n x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n \n \n y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n \n \n x_axis_format: str | None\n\ndefault `= None`\n\nA d3 format string for the x-axis labels (e.g., \".2e\" for scientific notation,\n\"~g\" for general format). If None, uses Vega-Lite's default formatting.\n\n\n \n \n y_axis_format: str | None\n\ndefault `= None`\n\nA d3 format string for the y-axis labels (e.g., \".2e\" for scientific notation,\n\"~g\" for general format). If None, uses Vega-Lite's default formatting.\n\n\n \n \n x_axis_labels_visible: bool | Literal['hidden']\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n \n \n caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n \n \n sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n \n \n tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hover", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n \n \n tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n \n \n height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n \n \n label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n \n \n show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n \n \n container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n \n \n scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n \n \n min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n \n \n every: Timer | float | None\n\ndefault `= None`\n\nContinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n \n \n inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recal", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "uts: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n \n \n visible: bool | Literal['hidden']\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n \n \n elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n \n \n elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n \n \n render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n \n \n buttons: list[Literal['fullscreen', 'export'] | Button] | None\n\ndefault `= None`\n\nA list of buttons to show for the component. Valid options are \"fullscreen\",\n\"export\", or a gr.Button() instance. The \"fullscreen\" button allows the user\nto view the plot in fullscreen mode. The \"export\" button allows the user to\nexport and download the current view of the plot as a PNG image. Custom\ngr.Button() instances will appear in the toolbar with their configured icon\nand/or label, and clicking them will trigger any .click() events registered on\nthe button. By default, no buttons are shown.\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rend", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "`gr.LinePlot`, `gr.ScatterPlot`, and `gr.BarPlot` all share the same API. Here\nis a summary of the most important features. For full details and live demos,\nsee the [Creating Plots](/guides/creating-plots) and [Time\nPlots](/guides/time-plots) guides.\n\n**Basic Usage with a DataFrame**\n\nPass a `pd.DataFrame` as the value, and specify `x` and `y` column names. The\ny-axis must be numeric; the x-axis can be strings, numbers, categories, or\ndatetimes.\n\n \n \n import gradio as gr\n import pandas as pd\n \n df = pd.DataFrame({\"origin\": [\"US\", \"EU\", \"Asia\"], \"sales\": [120, 95, 180]})\n \n with gr.Blocks() as demo:\n gr.BarPlot(df, x=\"origin\", y=\"sales\")\n\n**Breaking Out Series by Color**\n\nUse the `color` argument to group bars by a categorical column. Use\n`color_map` to assign specific colors:\n\n \n \n gr.BarPlot(df, x=\"origin\", y=\"sales\", color=\"year\",\n color_map={\"2023\": \"4488FF\", \"2024\": \"FF8844\"})\n\n**Aggregating Values**\n\nUse `x_bin` and `y_aggregate` to group and summarize data. For string x-axes,\nthe string values act as category bins automatically. For numeric x-axes,\n`x_bin` sets the bin size:\n\n \n \n gr.BarPlot(df, x=\"origin\", y=\"sales\", y_aggregate=\"sum\")\n gr.BarPlot(df, x=\"year\", y=\"sales\", x_bin=1, y_aggregate=\"mean\")\n\nFor time-series data, pass a string suffix (`\"s\"`, `\"m\"`, `\"h\"`, or `\"d\"`) to\n`x_bin`:\n\n \n \n gr.BarPlot(df, x=\"timestamp\", y=\"sales\", x_bin=\"1d\", y_aggregate=\"sum\")\n\n**Interactive Selection and Zoom**\n\nUse the `.select` event listener to respond to region selections (click and\ndrag). Combine with `.double_click` and `x_lim` to implement zoom in/out:\n\n \n \n with gr.Blocks() as demo:\n plot = gr.BarPlot(df, x=\"timestamp\", y=\"sales\")\n \n @plot.select\n def zoom(selection: gr.SelectData):\n return gr.BarPlot(x_lim=[selection.index[0], selection.index[1]])\n \n plot.double_click(lambda: gr.BarPlot(x_lim=None), outputs=plot)\n\n**Realtime Data**\n\nU", "heading1": "Key Concepts", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "ection: gr.SelectData):\n return gr.BarPlot(x_lim=[selection.index[0], selection.index[1]])\n \n plot.double_click(lambda: gr.BarPlot(x_lim=None), outputs=plot)\n\n**Realtime Data**\n\nUse `gr.Timer` to keep plots updated with live data. You can attach the timer\nvia `every`, or wire it up manually:\n\n \n \n def get_data():\n return pd.DataFrame(...) fetch latest data\n \n with gr.Blocks() as demo:\n timer = gr.Timer(5)\n plot = gr.BarPlot(get_data, x=\"time\", y=\"sales\", every=timer)\n\n**Interactive Dashboards**\n\nPlots can be driven by other components (dropdowns, sliders, etc.) to create\nfully interactive dashboards:\n\n \n \n with gr.Blocks() as demo:\n year = gr.Dropdown(choices=[2022, 2023, 2024], value=2024)\n plot = gr.BarPlot(x=\"origin\", y=\"sales\")\n \n def update(yr):\n filtered = df[df[\"year\"] == yr]\n return gr.BarPlot(filtered)\n \n year.change(update, inputs=year, outputs=plot)\n\n", "heading1": "Key Concepts", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Shortcuts\n\n \n \n gradio.BarPlot\n\nInterface String Shortcut `\"barplot\"`\n\nInitialization Uses default values\n\n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "bar_plot_demo\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe BarPlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListeners\n\n \n \n BarPlot.change(fn, \u00b7\u00b7\u00b7)\n\nTriggered when the value of the NativePlot changes either because of user\ninput (e.g. a user types in a textbox) OR because of a function update (e.g.\nan image receives a value from the output of an event trigger). See `.input()`\nfor a listener that is only triggered by user input.\n\n \n \n BarPlot.select(fn, \u00b7\u00b7\u00b7)\n\nEvent listener for when the user selects or deselects the NativePlot. Uses\nevent data gradio.SelectData to carry `value` referring to the label of the\nNativePlot, and `selected` to refer to state of the NativePlot. See\n for more details.\n\n \n \n BarPlot.double_click(fn, \u00b7\u00b7\u00b7)\n\nTriggered when the NativePlot is double clicked.\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n \n \n inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "nents to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string or None. If\nset to a string, the endpoint will be exposed in the API docs with the given\nname. If None (default), the name of the function will be used as the API\nendpoint.\n\n\n \n \n api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n \n \n scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n \n \n show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n \n \n show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n \n \n queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n batch: ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n \n \n max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n \n \n preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n \n \n postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n \n \n cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n \n \n trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is c", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": ". If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n \n \n js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend JavaScript to run before 'fn', provided as either a function\nor a raw code string. A function receives the values of 'inputs' and 'outputs'\nas arguments; raw code can access them through `arguments`. Return a list of\nvalues for the output components.\n\n\n \n \n concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n \n \n concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\ncontrols the visibility and accessibility of this endpoint. Can be \"public\"\n(shown in API docs and callable by clients), \"private\" (hidden from API docs\nand not callable by the Gradio client libraries), or \"undocumented\" (hidden\nfrom API docs but callable by clients and via gr.load). If fn is None,\napi_visibility will automatically be set to \"private\".\n\n\n \n \n time_limit: int | None\n\ndefault `= None`\n\n\n \n \n stream_every: float\n\ndefault `= 0.5`\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n\n ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": " ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n\n \n \n validator: Callable | None\n\ndefault `= None`\n\nOptional validation function to run before the main function. If provided,\nthis function will be executed first with queue=False, and only if it\ncompletes successfully will the main function be called. The validator\nreceives the same inputs as the main function, including the same keyword\narguments when `inputs_kwargs` is used, so its signature must accept those\nkeyword names. It should return a `gr.validate()` for each input value.\n\n\n \n \n inputs_kwargs: dict[str, Component | BlockContext] | None\n\ndefault `= None`\n\nDictionary mapping function parameter names to gradio.components. The\ncomponent values are passed to the function as keyword arguments.\n\n[Creating Plots](../../guides/creating-plots/)[Time Plots](../../guides/time-\nplots/)\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Group is a layout element within Blocks which groups together children so\nthat they do not have any padding or margin between them.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "with gr.Group():\n gr.Textbox(label=\"First\")\n gr.Textbox(label=\"Last\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n visible: bool | Literal['hidden']\n\ndefault `= True`\n\nIf False, group will be hidden.\n\n\n \n \n elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n \n \n elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n \n \n render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "Creates a line plot component to display data from a pandas DataFrame. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "**Using LinePlot as an input component.**\n\nHow LinePlot will pass its value to your function:\n\nType: `PlotData | None`\n\nThe data to display in a line plot.\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(\n value: PlotData | None\n ):\n process value from the LinePlot component\n return \"prediction\"\n \n interface = gr.Interface(predict, gr.LinePlot(), gr.Textbox())\n interface.launch()\n \n \n\n \n\n**Using LinePlot as an output component**\n\nHow LinePlot expects you to return a value:\n\nType: `pd.DataFrame | dict | None`\n\nExpects a pandas DataFrame containing the data to display in the line plot.\nThe DataFrame should contain at least two columns:\n\n * one for the x-axis (corresponding to this component's `x` argument)\n\n * one for the y-axis (corresponding to `y`).\n\nExample Code\n\n \n \n \n import gradio as gr\n \n def predict(text) -> pd.DataFrame | dict | None\n process value to return to the LinePlot component\n return value\n \n interface = gr.Interface(predict, gr.Textbox(), gr.LinePlot())\n interface.launch()\n \n \n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n \n \n x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n \n \n y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n \n \n color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n \n \n title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n \n \n x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n \n \n y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n \n \n color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n \n \n x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n \n \n y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n \n \n color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n \n \n colors_in_legend: list[str] | None\n\ndefault `= None`\n\nList containing column names of the series to show in the legend. By default,\nall series are shown.\n\n\n \n \n x_lim: list[float | None] | None\n\ndefault `= None`\n\nA tuple or list containing ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ne`\n\nList containing column names of the series to show in the legend. By default,\nall series are shown.\n\n\n \n \n x_lim: list[float | None] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum value. If x column is datetime type, x_lim\nshould be timestamps.\n\n\n \n \n y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n \n \n x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n \n \n y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n \n \n x_axis_format: str | None\n\ndefault `= None`\n\nA d3 format string for the x-axis labels (e.g., \".2e\" for scientific notation,\n\"~g\" for general format). If None, uses Vega-Lite's default formatting.\n\n\n \n \n y_axis_format: str | None\n\ndefault `= None`\n\nA d3 format string for the y-axis labels (e.g., \".2e\" for scientific notation,\n\"~g\" for general format). If None, uses Vega-Lite's default formatting.\n\n\n \n \n x_axis_labels_visible: bool | Literal['hidden']\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n \n \n caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n \n \n sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n \n \n tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hover", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n \n \n tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n \n \n height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n \n \n label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n \n \n show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n \n \n container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n \n \n scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n \n \n min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n \n \n every: Timer | float | None\n\ndefault `= None`\n\nContinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n \n \n inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recal", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "uts: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n \n \n visible: bool | Literal['hidden']\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n \n \n elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n \n \n elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n \n \n render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n \n \n buttons: list[Literal['fullscreen', 'export'] | Button] | None\n\ndefault `= None`\n\nA list of buttons to show for the component. Valid options are \"fullscreen\",\n\"export\", or a gr.Button() instance. The \"fullscreen\" button allows the user\nto view the plot in fullscreen mode. The \"export\" button allows the user to\nexport and download the current view of the plot as a PNG image. Custom\ngr.Button() instances will appear in the toolbar with their configured icon\nand/or label, and clicking them will trigger any .click() events registered on\nthe button. By default, no buttons are shown.\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rend", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "-render.\n\n\n \n \n preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "`gr.LinePlot`, `gr.ScatterPlot`, and `gr.BarPlot` all share the same API. Here\nis a summary of the most important features. For full details and live demos,\nsee the [Creating Plots](/guides/creating-plots) and [Time\nPlots](/guides/time-plots) guides.\n\n**Basic Usage with a DataFrame**\n\nPass a `pd.DataFrame` as the value, and specify `x` and `y` column names. The\ny-axis must be numeric; the x-axis can be strings, numbers, categories, or\ndatetimes.\n\n \n \n import gradio as gr\n import pandas as pd\n \n df = pd.DataFrame({\"week\": [1, 2, 3, 4], \"price\": [10, 25, 18, 30]})\n \n with gr.Blocks() as demo:\n gr.LinePlot(df, x=\"week\", y=\"price\")\n\n**Breaking Out Series by Color**\n\nUse the `color` argument to split data into multiple series. Use `color_map`\nto assign specific colors:\n\n \n \n gr.LinePlot(df, x=\"week\", y=\"price\", color=\"origin\",\n color_map={\"US\": \"FF9988\", \"EU\": \"88EEAA\"})\n\n**Aggregating Values**\n\nUse `x_bin` and `y_aggregate` to group and summarize data. For numeric x-axes,\n`x_bin` creates histogram-style bins. For string x-axes, strings act as\ncategory bins automatically:\n\n \n \n gr.LinePlot(df, x=\"week\", y=\"price\", x_bin=2, y_aggregate=\"mean\")\n\nFor time-series data, pass a string suffix (`\"s\"`, `\"m\"`, `\"h\"`, or `\"d\"`) to\n`x_bin`:\n\n \n \n gr.LinePlot(df, x=\"timestamp\", y=\"price\", x_bin=\"1d\", y_aggregate=\"mean\")\n\n**Interactive Selection and Zoom**\n\nUse the `.select` event listener to respond to region selections (click and\ndrag). Combine with `.double_click` and `x_lim` to implement zoom in/out:\n\n \n \n with gr.Blocks() as demo:\n plot = gr.LinePlot(df, x=\"week\", y=\"price\")\n \n @plot.select\n def zoom(selection: gr.SelectData):\n return gr.LinePlot(x_lim=[selection.index[0], selection.index[1]])\n \n plot.double_click(lambda: gr.LinePlot(x_lim=None), outputs=plot)\n\n**Realtime Data**\n\nUse `gr.Timer` to keep plots updated with live data. You can attach the ti", "heading1": "Key Concepts", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ex[0], selection.index[1]])\n \n plot.double_click(lambda: gr.LinePlot(x_lim=None), outputs=plot)\n\n**Realtime Data**\n\nUse `gr.Timer` to keep plots updated with live data. You can attach the timer\nvia `every`, or wire it up manually:\n\n \n \n def get_data():\n return pd.DataFrame(...) fetch latest data\n \n with gr.Blocks() as demo:\n timer = gr.Timer(5)\n plot = gr.LinePlot(get_data, x=\"time\", y=\"price\", every=timer)\n\n**Interactive Dashboards**\n\nPlots can be driven by other components (dropdowns, sliders, etc.) to create\nfully interactive dashboards:\n\n \n \n with gr.Blocks() as demo:\n origin = gr.Dropdown(choices=[\"US\", \"EU\", \"Asia\"], value=\"US\")\n plot = gr.LinePlot(x=\"week\", y=\"price\")\n \n def update(origin):\n filtered = df[df[\"origin\"] == origin]\n return gr.LinePlot(filtered)\n \n origin.change(update, inputs=origin, outputs=plot)\n\n", "heading1": "Key Concepts", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Shortcuts\n\n \n \n gradio.LinePlot\n\nInterface String Shortcut `\"lineplot\"`\n\nInitialization Uses default values\n\n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "line_plot_demo\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe LinePlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListeners\n\n \n \n LinePlot.change(fn, \u00b7\u00b7\u00b7)\n\nTriggered when the value of the NativePlot changes either because of user\ninput (e.g. a user types in a textbox) OR because of a function update (e.g.\nan image receives a value from the output of an event trigger). See `.input()`\nfor a listener that is only triggered by user input.\n\n \n \n LinePlot.select(fn, \u00b7\u00b7\u00b7)\n\nEvent listener for when the user selects or deselects the NativePlot. Uses\nevent data gradio.SelectData to carry `value` referring to the label of the\nNativePlot, and `selected` to refer to state of the NativePlot. See\n for more details.\n\n \n \n LinePlot.double_click(fn, \u00b7\u00b7\u00b7)\n\nTriggered when the NativePlot is double clicked.\n\nEvent Parameters\n\nParameters \u25bc\n\n\n \n \n fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n \n \n inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockCon", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "omponents to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n \n \n outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string or None. If\nset to a string, the endpoint will be exposed in the API docs with the given\nname. If None (default), the name of the function will be used as the API\nendpoint.\n\n\n \n \n api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n \n \n scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n \n \n show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n \n \n show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n \n \n queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n bat", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n \n \n batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n \n \n max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n \n \n preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n \n \n postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n \n \n cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n \n \n trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ding. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n \n \n js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend JavaScript to run before 'fn', provided as either a function\nor a raw code string. A function receives the values of 'inputs' and 'outputs'\nas arguments; raw code can access them through `arguments`. Return a list of\nvalues for the output components.\n\n\n \n \n concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n \n \n concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\ncontrols the visibility and accessibility of this endpoint. Can be \"public\"\n(shown in API docs and callable by clients), \"private\" (hidden from API docs\nand not callable by the Gradio client libraries), or \"undocumented\" (hidden\nfrom API docs but callable by clients and via gr.load). If fn is None,\napi_visibility will automatically be set to \"private\".\n\n\n \n \n time_limit: int | None\n\ndefault `= None`\n\n\n \n \n stream_every: float\n\ndefault `= 0.5`\n\n\n \n \n key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n\n \n \n validator: Callable | None\n\ndefault `= None`\n\nOptional validation function to run before the main function. If provided,\nthis function will be executed first with queue=False, and only if it\ncompletes successfully will the main function be called. The validator\nreceives the same inputs as the main function, including the same keyword\narguments when `inputs_kwargs` is used, so its signature must accept those\nkeyword names. It should return a `gr.validate()` for each input value.\n\n\n \n \n inputs_kwargs: dict[str, Component | BlockContext] | None\n\ndefault `= None`\n\nDictionary mapping function parameter names to gradio.components. The\ncomponent values are passed to the function as keyword arguments.\n\n[Creating Plots](../../guides/creating-plots/)[Connecting To A\nDatabase](../../guides/connecting-to-a-database/)\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Interface is Gradio's main high-level class, and allows you to create a\nweb-based GUI / demo around a machine learning model (or any Python function)\nin a few lines of code. You must specify three parameters: (1) the function to\ncreate a GUI for (2) the desired input components and (3) the desired output\ncomponents. Additional parameters can be used to control the appearance and\nbehavior of the demo. \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "import gradio as gr\n \n def image_classifier(inp):\n return {'cat': 0.3, 'dog': 0.7}\n \n demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\")\n demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "Parameters \u25bc\n\n\n \n \n fn: Callable\n\nthe function to wrap an interface around. Often a machine learning model's\nprediction function. Each parameter of the function corresponds to one input\ncomponent, and the function should return a single value or a tuple of values,\nwith each element in the tuple corresponding to one output component.\n\n\n \n \n inputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of input components should match the number of parameters in fn. If\nset to None, then only the output components will be displayed.\n\n\n \n \n outputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of output components should match the number of values returned by\nfn. If set to None, then only the input components will be displayed.\n\n\n \n \n examples: list[Any] | list[list[Any]] | str | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear below the UI components\nand can be clicked to populate the interface. Should be nested list, in which\nthe outer list consists of samples and each inner list consists of an input\ncorresponding to each input component. A string path to a directory of\nexamples can also be provided, but it should be within the directory with the\npython file running the gradio app. If there are multiple input components and\na directory is provided, a log.csv file must be present in the directory to\nlink corresponding inputs.\n\n\n \n \n cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter defaults to True (as long as `fn` and `outputs` are\nalso provided). Note that examples are cached separately from Gradio's queue()\nso certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n \n \n cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\". In HuggingFace Spaces, this\nparameter defaults to \"eager\" except for ZeroGPU Spaces, in which case it\ndefaults to \"lazy\".\n\n\n \n \n examples_per_page: int\n\ndefault `= 10`\n\nif examples are provided, how many to display per page.\n\n\n \n \n example_labels: list[str] | None\n\ndefault `= None`\n\na list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n \n \n preload_example: int | Literal[False]\n\ndefault `= 0`\n\nIf an integer is provided (and examples are being cached eagerly and none of\nthe input components have a developer-provided `value`), the example at that\nindex in the examples list will be preloaded when the Gradio app is first\nloaded. If False, no example will be preloaded.\n\n\n \n \n live: bool\n\ndefault `= False`\n\nwhether the interface should automatically rerun if any of the inputs change.\n\n\n \n \n title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in larg", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "tically rerun if any of the inputs change.\n\n\n \n \n title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n \n \n description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the input and\noutput components and beneath the title in regular font. Accepts Markdown and\nHTML content.\n\n\n \n \n article: str | None\n\ndefault `= None`\n\nan expanded article explaining the interface; if provided, appears below the\ninput and output components in regular font. Accepts Markdown and HTML\ncontent. If it is an HTTP(S) link to a downloadable remote file, the content\nof this file is displayed.\n\n\n \n \n flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\none of \"never\", \"auto\", or \"manual\". If \"never\" or \"auto\", users will not see\na button to flag an input and output. If \"manual\", users will see a button to\nflag. If \"auto\", every input the user submits will be automatically flagged,\nalong with the generated output. If \"manual\", both the input and outputs are\nflagged when the user clicks flag button. This parameter can be set with\nenvironmental variable GRADIO_FLAGGING_MODE; otherwise defaults to \"manual\".\n\n\n \n \n flagging_options: list[str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nif provided, allows user to select from the list of options when flagging.\nOnly applies if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored in the flagging CSV; or\nit can be a list of strings [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n \n \n flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the di", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "s [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n \n \n flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the directory where flagged data is stored. If the directory does not\nexist, it will be created.\n\n\n \n \n flagging_callback: FlaggingCallback | None\n\ndefault `= None`\n\neither None or an instance of a subclass of FlaggingCallback which will be\ncalled when a sample is flagged. If set to None, an instance of\ngradio.flagging.CSVLogger will be created and logs will be saved to a local\nCSV file in flagging_dir. Default to None.\n\n\n \n \n analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n \n \n batch: bool\n\ndefault `= False`\n\nif True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n \n \n max_batch_size: int\n\ndefault `= 4`\n\nthe maximum number of inputs to batch together if this is called from the\nqueue (only relevant if batch=True)\n\n\n \n \n api_visibility: Literal['public', 'private', 'undocumented']\n\ndefault `= \"public\"`\n\nControls the visibility of the prediction endpoint. Can be \"public\" (shown in\nAPI docs and callable), \"private\" (hidden from API docs and not callable by\nthe Gradio client libraries), or \"undocumented\" (hidden from API docs but\ncallable).\n\n\n \n \n api_name: str | None\n\ndefault `= None`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string\nor None. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None, the name of", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " None`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string\nor None. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None, the name of the function will be used.\n\n\n \n \n api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n \n \n allow_duplication: bool\n\ndefault `= False`\n\nif True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.\n\n\n \n \n concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`.queue()`, which itself is 1 by default).\n\n\n \n \n additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThese components will be rendered in an accordion below the main input\ncomponents. By default, no additional input components will be displayed.\n\n\n \n \n additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "an be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n \n \n submit_btn: str | Button\n\ndefault `= \"Submit\"`\n\nthe button to use for submitting inputs. Defaults to a `gr.Button(\"Submit\",\nvariant=\"primary\")`. This parameter does not apply if the Interface is output-\nonly, in which case the submit button always displays \"Generate\". Can be set\nto a string (which becomes the button label) or a `gr.Button` object (which\nallows for more customization).\n\n\n \n \n stop_btn: str | Button\n\ndefault `= \"Stop\"`\n\nthe button to use for stopping the interface. Defaults to a `gr.Button(\"Stop\",\nvariant=\"stop\", visible=False)`. Can be set to a string (which becomes the\nbutton label) or a `gr.Button` object (which allows for more customization).\n\n\n \n \n clear_btn: str | Button | None\n\ndefault `= \"Clear\"`\n\nthe button to use for clearing the inputs. Defaults to a `gr.Button(\"Clear\",\nvariant=\"secondary\")`. Can be set to a string (which becomes the button label)\nor a `gr.Button` object (which allows for more customization). Can be set to\nNone, which hides the button.\n\n\n \n \n delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n \n \n show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n \n \n fill_width: bool\n\ndefault `= False`\n\nwhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n \n \n time_limit: int | None\n\ndefault `= 30`\n\nThe time limit for the stream to run. Default is 30 seconds. Parameter only\nused for streaming images or audio if the interface is live and the input\ncomponents are set to \"streaming=True\".\n\n\n \n \n stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for streaming images or audio if\nthe interface is live and the input components are set to \"streaming=True\".\n\n\n \n \n deep_link: str | DeepLinkButton | bool | None\n\ndefault `= None`\n\na string or `gr.DeepLinkButton` object that creates a unique URL you can use\nto share your app and all components **as they currently are** with others.\nAutomatically enabled on Hugging Face Spaces unless explicitly set to False.\n\n\n \n \n validator: Callable | None\n\ndefault `= None`\n\na function that takes in the inputs and can optionally return a gr.validate()\nobject for each input.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "hello_worldhello_world_2hello_world_3\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n \n \n gradio.Interface.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n \n \n import gradio as gr\n def reverse(text):\n return text[::-1]\n demo = gr.Interface(reverse, \"text\", \"text\")\n demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n \n \n inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n \n \n inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the de", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "pp inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n \n \n inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n \n \n share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n \n \n debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n \n \n max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n \n \n auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n \n \n auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n \n \n prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n \n \n show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n \n \n server_name: str | None\n\ndefault `= No", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n \n \n server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n \n \n server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n \n \n height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n \n \n width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n \n \n favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n \n \n ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n \n \n ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n \n \n ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n \n \n ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n \n \n quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n \n \n footer_links: list[Literal['api', 'gradio', 'settings', 'runs'] | dict[str, str]] | None\n\ndefault `= None`\n\nThe links to display in the footer of the app. Accepts a list, where each\nelement of the list must b", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "inks: list[Literal['api', 'gradio', 'settings', 'runs'] | dict[str, str]] | None\n\ndefault `= None`\n\nThe links to display in the footer of the app. Accepts a list, where each\nelement of the list must be one of \"api\", \"gradio\", \"settings\", or \"runs\"\ncorresponding to the API docs, \"built with Gradio\", the settings page, and the\nrun history page respectively. The \"runs\" link only appears if `run_history`\nis True and the browser has at least one saved run for this app. If None, all\nfour links will be shown in the footer. An empty list means that no footer is\nshown.\n\n\n \n \n run_history: bool | None\n\ndefault `= None`\n\nIf True, users can review and reload calls from the run history page at\n/gradio_api/runs. Runs are saved privately in the browser by default; from\nthat page, a user can instead connect a Hugging Face bucket and save future\nruns there. Browser history is scoped to the logged-in user if the app uses\n`auth`. If False, nothing is recorded, the run history page is disabled, and\nany runs previously saved by this app are deleted from the browser. If None,\nwill use the GRADIO_RUN_HISTORY environment variable or default to True.\n\n\n \n \n allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n \n \n blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironm", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ur app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n \n \n root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n \n \n app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n \n \n state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n \n \n share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n \n \n share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n \n \n share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n \n \n auth_dependency: Callable[[fastapi.Request], str | None | Awaitable[str | None]] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n \n \n max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n \n \n enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n \n \n strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but ma", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "erver\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n \n \n node_server_name: str | None\n\ndefault `= None`\n\n\n \n \n node_port: int | None\n\ndefault `= None`\n\n\n \n \n ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 24+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n \n \n pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n \n \n mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled.\n\n\n \n \n num_workers: int | None\n\ndefault `= None`\n\nNumber of background workers to launch in the background to serve file I/O and\nstatic assets. This offloads traffic from the main server and reduces latency.\nOnly has an effect if ssr mode is set.\n\n\n \n \n i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n\n \n \n theme: Theme | str | None\n\ndefault `= None`", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "abels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n\n \n \n theme: Theme | str | None\n\ndefault `= None`\n\nA Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n \n \n css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n \n \n css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n \n \n js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom JavaScript provided as either a function or a raw code string. A\nfunction is automatically invoked; otherwise the code is executed directly\nwhen the page loads. To run JavaScript as a document-level `\n\n\n\n\n\t{if loading_status}\n\t\t\n\t{/if}\n

{value}

\n\n```\n\n", "heading1": "The Index.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "The `Example.svelte` file should expose the following props:\n\n```typescript\n export let value: string;\n export let type: \"gallery\" | \"table\";\n export let selected = false;\n export let index: number;\n```\n\n* `value`: The example value that should be displayed.\n\n* `type`: This is a variable that can be either `\"gallery\"` or `\"table\"` depending on how the examples are displayed. The `\"gallery\"` form is used when the examples correspond to a single input component, while the `\"table\"` form is used when a user has multiple input components, and the examples need to populate all of them. \n\n* `selected`: You can also adjust how the examples are displayed if a user \"selects\" a particular example by using the selected variable.\n\n* `index`: The current index of the selected value.\n\n* Any additional props your \"non-example\" component takes!\n\nThis is the `Example.svelte` file for the code `Radio` component:\n\n```svelte\n\n\n\n\t{value}\n\n\n\n```\n\n", "heading1": "The Example.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If your component deals with files, these files **should** be uploaded to the backend server. \nThe `@gradio/client` npm package provides the `upload` and `prepare_files` utility functions to help you do this.\n\nThe `prepare_files` function will convert the browser's `File` datatype to gradio's internal `FileData` type.\nYou should use the `FileData` data in your component to keep track of uploaded files.\n\nThe `upload` function will upload an array of `FileData` values to the server.\n\nHere's an example of loading files from an `` element when its value changes.\n\n\n```svelte\n\n\n\n```\n\nThe component exposes a prop named `root`. \nThis is passed down by the parent gradio app and it represents the base url that the files will be uploaded to and fetched from.\n\nFor WASM support, you should get the upload function from the `Context` and pass that as the third parameter of the `upload` function.\n\n```typescript\n\n```\n\n", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Most of Gradio's frontend components are published on [npm](https://www.npmjs.com/), the javascript package repository.\nThis means that you can use them to save yourself time while incorporating common patterns in your component, like uploading files.\nFor example, the `@gradio/upload` package has `Upload` and `ModifyUpload` components for properly uploading files to the Gradio server. \nHere is how you can use them to create a user interface to upload and display PDF files.\n\n```svelte\n\n\n\n{if value === null && interactive}\n \n \n \n{:else if value !== null}\n {if interactive}\n \n {/if}\n \n{:else}\n \t\n{/if}\n```\n\nYou can also combine existing Gradio components to create entirely unique experiences.\nLike rendering a gallery of chatbot conversations. \nThe possibilities are endless, please read the documentation on our javascript packages [here](https://gradio.app/main/docs/js).\nWe'll be adding more packages and documentation over the coming weeks!\n\n", "heading1": "Leveraging Existing Gradio Components", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "You can explore our component library via Storybook. You'll be able to interact with our components and see them in their various states.\n\nFor those interested in design customization, we provide the CSS variables consisting of our color palette, radii, spacing, and the icons we use - so you can easily match up your custom component with the style of our core components. This Storybook will be regularly updated with any new additions or changes.\n\n[Storybook Link](https://gradio.app/main/docs/js/storybook)\n\n", "heading1": "Matching Gradio Core's Design System", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If you want to make use of the vast vite ecosystem, you can use the `gradio.config.js` file to configure your component's build process. This allows you to make use of tools like tailwindcss, mdsvex, and more.\n\nCurrently, it is possible to configure the following:\n\nVite options:\n- `plugins`: A list of vite plugins to use.\n\nSvelte options:\n- `preprocess`: A list of svelte preprocessors to use.\n- `extensions`: A list of file extensions to compile to `.svelte` files.\n- `build.target`: The target to build for, this may be necessary to support newer javascript features. See the [esbuild docs](https://esbuild.github.io/api/target) for more information.\n\nThe `gradio.config.js` file should be placed in the root of your component's `frontend` directory. A default config file is created for you when you create a new component. But you can also create your own config file, if one doesn't exist, and use it to customize your component's build process.\n\nExample for a Vite plugin\n\nCustom components can use Vite plugins to customize the build process. Check out the [Vite Docs](https://vitejs.dev/guide/using-plugins.html) for more information. \n\nHere we configure [TailwindCSS](https://tailwindcss.com), a utility-first CSS framework. Setup is easiest using the version 4 prerelease. \n\n```\nnpm install tailwindcss@next @tailwindcss/vite@next\n```\n\nIn `gradio.config.js`:\n\n```typescript\nimport tailwindcss from \"@tailwindcss/vite\";\nexport default {\n plugins: [tailwindcss()]\n};\n```\n\nThen create a `style.css` file with the following content:\n\n```css\n@import \"tailwindcss\";\n```\n\nImport this file into `Index.svelte`. Note, that you need to import the css file containing `@import` and cannot just use a `\n```\n\nNow import `PdfUploadText.svelte` in your `\n\n\n\t\n\n\n\n```\n\n\nTip: Exercise for the reader - reduce the code duplication between `Index.svelte` and `Example.svelte` \ud83d\ude0a\n\n\nYou will not be able to render examples until we make some changes to the backend code in the next step!\n\n", "heading1": "Step 8.5: The Example view", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The backend changes needed are smaller.\nWe're almost done!\n\nWhat we're going to do is:\n* Add `change` and `upload` events to our component.\n* Add a `height` property to let users control the height of the PDF.\n* Set the `data_model` of our component to be `FileData`. This is so that Gradio can automatically cache and safely serve any files that are processed by our component.\n* Modify the `preprocess` method to return a string corresponding to the path of our uploaded PDF.\n* Modify the `postprocess` to turn a path to a PDF created in an event handler to a `FileData`.\n\nWhen all is said an done, your component's backend code should look like this:\n\n```python\nfrom __future__ import annotations\nfrom typing import Any, Callable, TYPE_CHECKING\n\nfrom gradio.components.base import Component\nfrom gradio.data_classes import FileData\nfrom gradio import processing_utils\nif TYPE_CHECKING:\n from gradio.components import Timer\n\nclass PDF(Component):\n\n EVENTS = [\"change\", \"upload\"]\n\n data_model = FileData\n\n def __init__(self, value: Any = None, *,\n height: int | None = None,\n label: str | I18nData | None = None,\n info: str | I18nData | None = None,\n show_label: bool | None = None,\n container: bool = True,\n scale: int | None = None,\n min_width: int | None = None,\n interactive: bool | None = None,\n visible: bool = True,\n elem_id: str | None = None,\n elem_classes: list[str] | str | None = None,\n render: bool = True,\n load_fn: Callable[..., Any] | None = None,\n every: Timer | float | None = None):\n super().__init__(value, label=label, info=info,\n show_label=show_label, container=container,\n scale=scale, min_width=min_width,\n interactive=interactive, visible=visible,\n ", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": " show_label=show_label, container=container,\n scale=scale, min_width=min_width,\n interactive=interactive, visible=visible,\n elem_id=elem_id, elem_classes=elem_classes,\n render=render, load_fn=load_fn, every=every)\n self.height = height\n\n def preprocess(self, payload: FileData) -> str:\n return payload.path\n\n def postprocess(self, value: str | None) -> FileData:\n if not value:\n return None\n return FileData(path=value)\n\n def example_payload(self):\n return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n\n def example_value(self):\n return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n```\n\n", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "To test our backend code, let's add a more complex demo that performs Document Question and Answering with huggingface transformers.\n\nIn our `demo` directory, create a `requirements.txt` file with the following packages\n\n```\ntorch\ntransformers\npdf2image\npytesseract\n```\n\n\nTip: Remember to install these yourself and restart the dev server! You may need to install extra non-python dependencies for `pdf2image`. See [here](https://pypi.org/project/pdf2image/). Feel free to write your own demo if you have trouble.\n\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\nfrom pdf2image import convert_from_path\nfrom transformers import pipeline\nfrom pathlib import Path\n\ndir_ = Path(__file__).parent\n\np = pipeline(\n \"document-question-answering\",\n model=\"impira/layoutlm-document-qa\",\n)\n\ndef qa(question: str, doc: str) -> str:\n img = convert_from_path(doc)[0]\n output = p(img, question)\n return sorted(output, key=lambda x: x[\"score\"], reverse=True)[0]['answer']\n\n\ndemo = gr.Interface(\n qa,\n [gr.Textbox(label=\"Question\"), PDF(label=\"Document\")],\n gr.Textbox(),\n)\n\ndemo.launch()\n```\n\nSee our demo in action below!\n\n\n\nFinally lets build our component with `gradio cc build` and publish it with the `gradio cc publish` command!\nThis will guide you through the process of uploading your component to [PyPi](https://pypi.org/) and [HuggingFace Spaces](https://huggingface.co/spaces).\n\n\nTip: You may need to add the following lines to the `Dockerfile` of your HuggingFace Space.\n\n```Dockerfile\nRUN mkdir -p /tmp/cache/\nRUN chmod a+rwx -R /tmp/cache/\nRUN apt-get update && apt-get install -y poppler-utils tesseract-ocr\n\nENV TRANSFORMERS_CACHE=/tmp/cache/\n```\n\n", "heading1": "Step 10: Add a demo and publish!", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "In order to use our new component in **any** gradio 4.0 app, simply install it with pip, e.g. `pip install gradio-pdf`. Then you can use it like the built-in `gr.File()` component (except that it will only accept and display PDF files).\n\nHere is a simple demo with the Blocks api:\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\n\nwith gr.Blocks() as demo:\n pdf = PDF(label=\"Upload a PDF\", interactive=True)\n name = gr.Textbox()\n pdf.upload(lambda f: f, pdf, name)\n\ndemo.launch()\n```\n\n\nI hope you enjoyed this tutorial!\nThe complete source code for our component is [here](https://huggingface.co/spaces/freddyaboulton/gradio_pdf/tree/main/src).\nPlease don't hesitate to reach out to the gradio community on the [HuggingFace Discord](https://discord.gg/hugging-face-879548962464493619) if you get stuck.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The documentation will be generated when running `gradio cc build`. You can pass the `--no-generate-docs` argument to turn off this behaviour.\n\nThere is also a standalone `docs` command that allows for greater customisation. If you are running this command manually it should be run _after_ the `version` in your `pyproject.toml` has been bumped but before building the component.\n\nAll arguments are optional.\n\n```bash\ngradio cc docs\n path The directory of the custom component.\n --demo-dir Path to the demo directory.\n --demo-name Name of the demo file\n --space-url URL of the Hugging Face Space to link to\n --generate-space create a documentation space.\n --no-generate-space do not create a documentation space\n --readme-path Path to the README.md file.\n --generate-readme create a REAMDE.md file\n --no-generate-readme do not create a README.md file\n --suppress-demo-check suppress validation checks and warnings\n```\n\n", "heading1": "How do I use it?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The `gradio cc docs` command will generate an interactive Gradio app and a static README file with various features. You can see an example here:\n\n- [Gradio app deployed on Hugging Face Spaces]()\n- [README.md rendered by GitHub]()\n\nThe README.md and space both have the following features:\n\n- A description.\n- Installation instructions.\n- A fully functioning code snippet.\n- Optional links to PyPi, GitHub, and Hugging Face Spaces.\n- API documentation including:\n - An argument table for component initialisation showing types, defaults, and descriptions.\n - A description of how the component affects the user's predict function.\n - A table of events and their descriptions.\n - Any additional interfaces or classes that may be used during initialisation or in the pre- or post- processors.\n\nAdditionally, the Gradio includes:\n\n- A live demo.\n- A richer, interactive version of the parameter tables.\n- Nicer styling!\n\n", "heading1": "What gets generated?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The documentation generator uses existing standards to extract the necessary information, namely Type Hints and Docstrings. There are no Gradio-specific APIs for documentation, so following best practices will generally yield the best results.\n\nIf you already use type hints and docstrings in your component source code, you don't need to do much to benefit from this feature, but there are some details that you should be aware of.\n\nPython version\n\nTo get the best documentation experience, you need to use Python `3.10` or greater when generating documentation. This is because some introspection features used to generate the documentation were only added in `3.10`.\n\nType hints\n\nPython type hints are used extensively to provide helpful information for users. \n\n
\n What are type hints?\n\n\nIf you need to become more familiar with type hints in Python, they are a simple way to express what Python types are expected for arguments and return values of functions and methods. They provide a helpful in-editor experience, aid in maintenance, and integrate with various other tools. These types can be simple primitives, like `list` `str` `bool`; they could be more compound types like `list[str]`, `str | None` or `tuple[str, float | int]`; or they can be more complex types using utility classed like [`TypedDict`](https://peps.python.org/pep-0589/abstract).\n\n[Read more about type hints in Python.](https://realpython.com/lessons/type-hinting/)\n\n\n
\n\nWhat do I need to add hints to?\n\nYou do not need to add type hints to every part of your code. For the documentation to work correctly, you will need to add type hints to the following component methods:\n\n- `__init__` parameters should be typed.\n- `postprocess` parameters and return value should be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you ma", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you make.\n\n`__init__`\n\nHere, you only need to type the parameters. If you have cloned a template with `gradio` cc create`, these should already be in place. You will only need to add new hints for anything you have added or changed:\n\n```py\ndef __init__(\n self,\n value: str | None = None,\n *,\n sources: Literal[\"upload\", \"microphone\"] = \"upload,\n every: Timer | float | None = None,\n ...\n):\n ...\n```\n\n`preprocess` and `postprocess`\n\nThe `preprocess` and `postprocess` methods determine the value passed to the user function and the value that needs to be returned.\n\nEven if the design of your component is primarily as an input or an output, it is worth adding type hints to both the input parameters and the return values because Gradio has no way of limiting how components can be used.\n\nIn this case, we specifically care about:\n\n- The return type of `preprocess`.\n- The input type of `postprocess`.\n\n```py\ndef preprocess(\n self, payload: FileData | None input is optional\n) -> tuple[int, str] | str | None:\n\nuser function input is the preprocess return \u25b2\nuser function output is the postprocess input \u25bc\n\ndef postprocess(\n self, value: tuple[int, str] | None\n) -> FileData | bytes | None: return is optional\n ...\n```\n\nDocstrings\n\nDocstrings are also used extensively to extract more meaningful, human-readable descriptions of certain parts of the API.\n\n
\n What are docstrings?\n\n\nIf you need to become more familiar with docstrings in Python, they are a way to annotate parts of your code with human-readable decisions and explanations. They offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement i", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement is where they appear. Docstrings should be \"a string literal that occurs as the first statement in a module, function, class, or method definition\".\n\n[Read more about Python docstrings.](https://peps.python.org/pep-0257/what-is-a-docstring)\n\n
\n\nWhile docstrings don't have any syntax requirements, we need a particular structure for documentation purposes.\n\nAs with type hint, the specific information we care about is as follows:\n\n- `__init__` parameter docstrings.\n- `preprocess` return docstrings.\n- `postprocess` input parameter docstrings.\n\nEverything else is optional.\n\nDocstrings should always take this format to be picked up by the documentation generator:\n\nClasses\n\n```py\n\"\"\"\nA description of the class.\n\nThis can span multiple lines and can _contain_ *markdown*.\n\"\"\"\n```\n\nMethods and functions \n\nMarkdown in these descriptions will not be converted into formatted text.\n\n```py\n\"\"\"\nParameters:\n param_one: A description for this parameter.\n param_two: A description for this parameter.\nReturns:\n A description for this return value.\n\"\"\"\n```\n\nEvents\n\nIn custom components, events are expressed as a list stored on the `events` field of the component class. While we do not need types for events, we _do_ need a human-readable description so users can understand the behaviour of the event.\n\nTo facilitate this, we must create the event in a specific way.\n\nThere are two ways to add events to a custom component.\n\nBuilt-in events\n\nGradio comes with a variety of built-in events that may be enough for your component. If you are using built-in events, you do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n ...\n\n EVENTS = [\n Events.change,\n Events.up", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n ...\n\n EVENTS = [\n Events.change,\n Events.upload,\n ]\n```\n\nCustom events\n\nYou can define a custom event if the built-in events are unsuitable for your use case. This is a straightforward process, but you must create the event in this way for docstrings to work correctly:\n\n```py\nfrom gradio.events import Events, EventListener\n\nclass ParamViewer(Component):\n ...\n\n EVENTS = [\n Events.change,\n EventListener(\n \"bingbong\",\n doc=\"This listener is triggered when the user does a bingbong.\"\n )\n ]\n```\n\nDemo\n\nThe `demo/app.py`, often used for developing the component, generates the live demo and code snippet. The only strict rule here is that the `demo.launch()` command must be contained with a `__name__ == \"__main__\"` conditional as below:\n\n```py\nif __name__ == \"__main__\":\n demo.launch()\n```\n\nThe documentation generator will scan for such a clause and error if absent. If you are _not_ launching the demo inside the `demo/app.py`, then you can pass `--suppress-demo-check` to turn off this check.\n\nDemo recommendations\n\nAlthough there are no additional rules, there are some best practices you should bear in mind to get the best experience from the documentation generator.\n\nThese are only guidelines, and every situation is unique, but they are sound principles to remember.\n\nKeep the demo compact\n\nCompact demos look better and make it easier for users to understand what the demo does. Try to remove as many extraneous UI elements as possible to focus the users' attention on the core use case. \n\nSometimes, it might make sense to have a `demo/app.py` just for the docs and an additional, more complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\n", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "ore complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\nKeep the code concise\n\nThe 'getting started' snippet utilises the demo code, which should be as short as possible to keep users engaged and avoid confusion.\n\nIt isn't the job of the sample snippet to demonstrate the whole API; this snippet should be the shortest path to success for a new user. It should be easy to type or copy-paste and easy to understand. Explanatory comments should be brief and to the point.\n\nAvoid external dependencies\n\nAs mentioned above, users should be able to copy-paste a snippet and have a fully working app. Try to avoid third-party library dependencies to facilitate this.\n\nYou should carefully consider any examples; avoiding examples that require additional files or that make assumptions about the environment is generally a good idea.\n\nEnsure the `demo` directory is self-contained\n\nOnly the `demo` directory will be uploaded to Hugging Face spaces in certain instances, as the component will be installed via PyPi if possible. It is essential that this directory is self-contained and any files needed for the correct running of the demo are present.\n\nAdditional URLs\n\nThe documentation generator will generate a few buttons, providing helpful information and links to users. They are obtained automatically in some cases, but some need to be explicitly included in the `pyproject.yaml`. \n\n- PyPi Version and link - This is generated automatically.\n- GitHub Repository - This is populated via the `pyproject.toml`'s `project.urls.repository`.\n- Hugging Face Space - This is populated via the `pyproject.toml`'s `project.urls.space`.\n\nAn example `pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "First, we'll install the following requirements in our system:\n\n```\nopencv-python\ntorch\ntransformers>=4.43.0\nspaces\n```\n\nThen, we'll download the model from the Hugging Face Hub:\n\n```python\nfrom transformers import RTDetrForObjectDetection, RTDetrImageProcessor\n\nimage_processor = RTDetrImageProcessor.from_pretrained(\"PekingU/rtdetr_r50vd\")\nmodel = RTDetrForObjectDetection.from_pretrained(\"PekingU/rtdetr_r50vd\").to(\"cuda\")\n```\nWe're moving the model to the GPU. We'll be deploying our model to Hugging Face Spaces and running the inference in the [free ZeroGPU cluster](https://huggingface.co/zero-gpu-explorers). \n\n\n", "heading1": "Setting up the Model", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Our inference function will accept a video and a desired confidence threshold.\nObject detection models identify many objects and assign a confidence score to each object. The lower the confidence, the higher the chance of a false positive. So we will let our users set the confidence threshold.\n\nOur function will iterate over the frames in the video and run the RT-DETR model over each frame.\nWe will then draw the bounding boxes for each detected object in the frame and save the frame to a new output video.\nThe function will yield each output video in chunks of two seconds.\n\nIn order to keep inference times as low as possible on ZeroGPU (there is a time-based quota),\nwe will halve the original frames-per-second in the output video and resize the input frames to be half the original \nsize before running the model.\n\nThe code for the inference function is below - we'll go over it piece by piece.\n\n```python\nimport spaces\nimport cv2\nfrom PIL import Image\nimport torch\nimport time\nimport numpy as np\nimport uuid\n\nfrom draw_boxes import draw_bounding_boxes\n\nSUBSAMPLE = 2\n\n@spaces.GPU\ndef stream_object_detection(video, conf_threshold):\n cap = cv2.VideoCapture(video)\n\n This means we will output mp4 videos\n video_codec = cv2.VideoWriter_fourcc(*\"mp4v\") type: ignore\n fps = int(cap.get(cv2.CAP_PROP_FPS))\n\n desired_fps = fps // SUBSAMPLE\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2\n\n iterating, frame = cap.read()\n\n n_frames = 0\n\n Use UUID to create a unique video file\n output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n\n Output Video\n output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n batch = []\n\n while iterating:\n frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n if n_frames % SUBSAMPLE == 0:\n batch.append(frame)\n if len(batc", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": " frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n if n_frames % SUBSAMPLE == 0:\n batch.append(frame)\n if len(batch) == 2 * desired_fps:\n inputs = image_processor(images=batch, return_tensors=\"pt\").to(\"cuda\")\n\n with torch.no_grad():\n outputs = model(**inputs)\n\n boxes = image_processor.post_process_object_detection(\n outputs,\n target_sizes=torch.tensor([(height, width)] * len(batch)),\n threshold=conf_threshold)\n \n for i, (array, box) in enumerate(zip(batch, boxes)):\n pil_image = draw_bounding_boxes(Image.fromarray(array), box, model, conf_threshold)\n frame = np.array(pil_image)\n Convert RGB to BGR\n frame = frame[:, :, ::-1].copy()\n output_video.write(frame)\n\n batch = []\n output_video.release()\n yield output_video_name\n output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n\n iterating, frame = cap.read()\n n_frames += 1\n```\n\n1. **Reading from the Video**\n\nOne of the industry standards for creating videos in python is OpenCV so we will use it in this app.\n\nThe `cap` variable is how we will read from the input video. Whenever we call `cap.read()`, we are reading the next frame in the video.\n\nIn order to stream video in Gradio, we need to yield a different video file for each \"chunk\" of the output video.\nWe create the next video file to write to with the `output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame i", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "dth, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame in the video, we will resize it to be half the size. OpenCV reads files in `BGR` format, so will convert to the expected `RGB` format of transfomers. That's what the first two lines of the while loop are doing. \n\nWe take every other frame and add it to a `batch` list so that the output video is half the original FPS. When the batch covers two seconds of video, we will run the model. The two second threshold was chosen to keep the processing time of each batch small enough so that video is smoothly displayed in the server while not requiring too many separate forward passes. In order for video streaming to work properly in Gradio, the batch size should be at least 1 second. \n\nWe run the forward pass of the model and then use the `post_process_object_detection` method of the model to scale the detected bounding boxes to the size of the input frame.\n\nWe make use of a custom function to draw the bounding boxes (source [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection/blob/main/draw_boxes.pyL14)). We then have to convert from `RGB` to `BGR` before writing back to the output video.\n\nOnce we have finished processing the batch, we create a new output video file for the next batch.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "The UI code is pretty similar to other kinds of Gradio apps. \nWe'll use a standard two-column layout so that users can see the input and output videos side by side.\n\nIn order for streaming to work, we have to set `streaming=True` in the output video. Setting the video\nto autoplay is not necessary but it's a better experience for users.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as app:\n gr.HTML(\n \"\"\"\n

\n Video Object Detection with RT-DETR\n

\n \"\"\")\n with gr.Row():\n with gr.Column():\n video = gr.Video(label=\"Video Source\")\n conf_threshold = gr.Slider(\n label=\"Confidence Threshold\",\n minimum=0.0,\n maximum=1.0,\n step=0.05,\n value=0.30,\n )\n with gr.Column():\n output_video = gr.Video(label=\"Processed Video\", streaming=True, autoplay=True)\n\n video.upload(\n fn=stream_object_detection,\n inputs=[video, conf_threshold],\n outputs=[output_video],\n )\n\n\n```\n\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "You can check out our demo hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection). \n\nIt is also embedded on this page below\n\n$demo_rt-detr-object-detection", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Automatic speech recognition (ASR), the conversion of spoken speech to text, is a very important and thriving area of machine learning. ASR algorithms run on practically every smartphone, and are becoming increasingly embedded in professional workflows, such as digital assistants for nurses and doctors. Because ASR algorithms are designed to be used directly by customers and end users, it is important to validate that they are behaving as expected when confronted with a wide variety of speech patterns (different accents, pitches, and background audio conditions).\n\nUsing `gradio`, you can easily build a demo of your ASR model and share that with a testing team, or test it yourself by speaking through the microphone on your device.\n\nThis tutorial will show how to take a pretrained speech-to-text model and deploy it with a Gradio interface. We will start with a **_full-context_** model, in which the user speaks the entire audio before the prediction runs. Then we will adapt the demo to make it **_streaming_**, meaning that the audio model will convert speech as you speak. \n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). You will also need a pretrained speech recognition model. In this tutorial, we will build demos from 2 ASR libraries:\n\n- Transformers (for this, `pip install torch transformers torchaudio`)\n\nMake sure you have at least one of these installed so that you can follow along the tutorial. You will also need `ffmpeg` [installed on your system](https://www.ffmpeg.org/download.html), if you do not already have it, to process files from the microphone.\n\nHere's how to build a real time speech recognition (ASR) app:\n\n1. [Set up the Transformers ASR Model](1-set-up-the-transformers-asr-model)\n2. [Create a Full-Context ASR Demo with Transformers](2-create-a-full-context-asr-demo-with-transformers)\n3. [Create a Streaming ASR Demo with Transformers](3-create-a-streaming-asr-demo-with-transformers)\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "First, you will need to have an ASR model that you have either trained yourself or you will need to download a pretrained model. In this tutorial, we will start by using a pretrained ASR model from the model, `whisper`.\n\nHere is the code to load `whisper` from Hugging Face `transformers`.\n\n```python\nfrom transformers import pipeline\n\np = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n```\n\nThat's it!\n\n", "heading1": "1. Set up the Transformers ASR Model", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "We will start by creating a _full-context_ ASR demo, in which the user speaks the full audio before using the ASR model to run inference. This is very easy with Gradio -- we simply create a function around the `pipeline` object above.\n\nWe will use `gradio`'s built in `Audio` component, configured to take input from the user's microphone and return a filepath for the recorded audio. The output component will be a plain `Textbox`.\n\n$code_asr\n$demo_asr\n\nThe `transcribe` function takes a single parameter, `audio`, which is a numpy array of the audio the user recorded. The `pipeline` object expects this in float32 format, so we convert it first to float32, and then extract the transcribed text.\n\n", "heading1": "2. Create a Full-Context ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "To make this a *streaming* demo, we need to make these changes:\n\n1. Set `streaming=True` in the `Audio` component\n2. Set `live=True` in the `Interface`\n3. Add a `state` to the interface to store the recorded audio of a user\n\nTip: You can also set `time_limit` and `stream_every` parameters in the interface. The `time_limit` caps the amount of time each user's stream can take. The default is 30 seconds so users won't be able to stream audio for more than 30 seconds. The `stream_every` parameter controls how frequently data is sent to your function. By default it is 0.5 seconds.\n\nTake a look below.\n\n$code_stream_asr\n\nNotice that we now have a state variable because we need to track all the audio history. `transcribe` gets called whenever there is a new small chunk of audio, but we also need to keep track of all the audio spoken so far in the state. As the interface runs, the `transcribe` function gets called, with a record of all the previously spoken audio in the `stream` and the new chunk of audio as `new_chunk`. We return the new full audio to be stored back in its current state, and we also return the transcription. Here, we naively append the audio together and call the `transcriber` object on the entire audio. You can imagine more efficient ways of handling this, such as re-processing only the last 5 seconds of audio whenever a new chunk of audio is received. \n\n$demo_stream_asr\n\nNow the ASR model will run inference as you speak! \n", "heading1": "3. Create a Streaming ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "The next generation of AI user interfaces is moving towards audio-native experiences. Users will be able to speak to chatbots and receive spoken responses in return. Several models have been built under this paradigm, including GPT-4o and [mini omni](https://github.com/gpt-omni/mini-omni).\n\nIn this guide, we'll walk you through building your own conversational chat application using mini omni as an example. You can see a demo of the finished app below:\n\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Our application will enable the following user experience:\n\n1. Users click a button to start recording their message\n2. The app detects when the user has finished speaking and stops recording\n3. The user's audio is passed to the omni model, which streams back a response\n4. After omni mini finishes speaking, the user's microphone is reactivated\n5. All previous spoken audio, from both the user and omni, is displayed in a chatbot component\n\nLet's dive into the implementation details.\n\n", "heading1": "Application Overview", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "We'll stream the user's audio from their microphone to the server and determine if the user has stopped speaking on each new chunk of audio.\n\nHere's our `process_audio` function:\n\n```python\nimport numpy as np\nfrom utils import determine_pause\n\ndef process_audio(audio: tuple, state: AppState):\n if state.stream is None:\n state.stream = audio[1]\n state.sampling_rate = audio[0]\n else:\n state.stream = np.concatenate((state.stream, audio[1]))\n\n pause_detected = determine_pause(state.stream, state.sampling_rate, state)\n state.pause_detected = pause_detected\n\n if state.pause_detected and state.started_talking:\n return gr.Audio(recording=False), state\n return None, state\n```\n\nThis function takes two inputs:\n1. The current audio chunk (a tuple of `(sampling_rate, numpy array of audio)`)\n2. The current application state\n\nWe'll use the following `AppState` dataclass to manage our application state:\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass\nclass AppState:\n stream: np.ndarray | None = None\n sampling_rate: int = 0\n pause_detected: bool = False\n stopped: bool = False\n conversation: list = []\n```\n\nThe function concatenates new audio chunks to the existing stream and checks if the user has stopped speaking. If a pause is detected, it returns an update to stop recording. Otherwise, it returns `None` to indicate no changes.\n\nThe implementation of the `determine_pause` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/eb027808c7bfe5179b46d9352e3fa1813a45f7c3/app.pyL98).\n\n", "heading1": "Processing User Audio", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "After processing the user's audio, we need to generate and stream the chatbot's response. Here's our `response` function:\n\n```python\nimport io\nimport tempfile\nfrom pydub import AudioSegment\n\ndef response(state: AppState):\n if not state.pause_detected and not state.started_talking:\n return None, AppState()\n \n audio_buffer = io.BytesIO()\n\n segment = AudioSegment(\n state.stream.tobytes(),\n frame_rate=state.sampling_rate,\n sample_width=state.stream.dtype.itemsize,\n channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),\n )\n segment.export(audio_buffer, format=\"wav\")\n\n with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n f.write(audio_buffer.getvalue())\n \n state.conversation.append({\"role\": \"user\",\n \"content\": {\"path\": f.name,\n \"mime_type\": \"audio/wav\"}})\n \n output_buffer = b\"\"\n\n for mp3_bytes in speaking(audio_buffer.getvalue()):\n output_buffer += mp3_bytes\n yield mp3_bytes, state\n\n with tempfile.NamedTemporaryFile(suffix=\".mp3\", delete=False) as f:\n f.write(output_buffer)\n \n state.conversation.append({\"role\": \"assistant\",\n \"content\": {\"path\": f.name,\n \"mime_type\": \"audio/mp3\"}})\n yield None, AppState(conversation=state.conversation)\n```\n\nThis function:\n1. Converts the user's audio to a WAV file\n2. Adds the user's message to the conversation history\n3. Generates and streams the chatbot's response using the `speaking` function\n4. Saves the chatbot's response as an MP3 file\n5. Adds the chatbot's response to the conversation history\n\nNote: The implementation of the `speaking` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/main/app.pyL116).\n\n", "heading1": "Generating the Response", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Now let's put it all together using Gradio's Blocks API:\n\n```python\nimport gradio as gr\n\ndef start_recording_user(state: AppState):\n if not state.stopped:\n return gr.Audio(recording=True)\n\nwith gr.Blocks() as demo:\n with gr.Row():\n with gr.Column():\n input_audio = gr.Audio(\n label=\"Input Audio\", sources=\"microphone\", type=\"numpy\"\n )\n with gr.Column():\n chatbot = gr.Chatbot(label=\"Conversation\")\n output_audio = gr.Audio(label=\"Output Audio\", streaming=True, autoplay=True)\n state = gr.State(value=AppState())\n\n stream = input_audio.stream(\n process_audio,\n [input_audio, state],\n [input_audio, state],\n stream_every=0.5,\n time_limit=30,\n )\n respond = input_audio.stop_recording(\n response,\n [state],\n [output_audio, state]\n )\n respond.then(lambda s: s.conversation, [state], [chatbot])\n\n restart = output_audio.stop(\n start_recording_user,\n [state],\n [input_audio]\n )\n cancel = gr.Button(\"Stop Conversation\", variant=\"stop\")\n cancel.click(lambda: (AppState(stopped=True), gr.Audio(recording=False)), None,\n [state, input_audio], cancels=[respond, restart])\n\nif __name__ == \"__main__\":\n demo.launch()\n```\n\nThis setup creates a user interface with:\n- An input audio component for recording user messages\n- A chatbot component to display the conversation history\n- An output audio component for the chatbot's responses\n- A button to stop and reset the conversation\n\nThe app streams user audio in 0.5-second chunks, processes it, generates responses, and updates the conversation history accordingly.\n\n", "heading1": "Building the Gradio App", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "This guide demonstrates how to build a conversational chatbot application using Gradio and the mini omni model. You can adapt this framework to create various audio-based chatbot demos. To see the full application in action, visit the Hugging Face Spaces demo: https://huggingface.co/spaces/gradio/omni-mini\n\nFeel free to experiment with different models, audio processing techniques, or user interface designs to create your own unique conversational AI experiences!", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Just like the classic Magic 8 Ball, a user should ask it a question orally and then wait for a response. Under the hood, we'll use Whisper to transcribe the audio and then use an LLM to generate a magic-8-ball-style answer. Finally, we'll use Parler TTS to read the response aloud.\n\n", "heading1": "The Overview", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "First let's define the UI and put placeholders for all the python logic.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as block:\n gr.HTML(\n f\"\"\"\n

Magic 8 Ball \ud83c\udfb1

\n

Ask a question and receive wisdom

\n

Powered by Parler-TTS\n \"\"\"\n )\n with gr.Group():\n with gr.Row():\n audio_out = gr.Audio(label=\"Spoken Answer\", streaming=True, autoplay=True)\n answer = gr.Textbox(label=\"Answer\")\n state = gr.State()\n with gr.Row():\n audio_in = gr.Audio(label=\"Speak your question\", sources=\"microphone\", type=\"filepath\")\n\n audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out])\\\n .then(fn=read_response, inputs=state, outputs=[answer, audio_out])\n\nblock.launch()\n```\n\nWe're placing the output Audio and Textbox components and the input Audio component in separate rows. In order to stream the audio from the server, we'll set `streaming=True` in the output Audio component. We'll also set `autoplay=True` so that the audio plays as soon as it's ready.\nWe'll be using the Audio input component's `stop_recording` event to trigger our application's logic when a user stops recording from their microphone.\n\nWe're separating the logic into two parts. First, `generate_response` will take the recorded audio, transcribe it and generate a response with an LLM. We're going to store the response in a `gr.State` variable that then gets passed to the `read_response` function that generates the audio.\n\nWe're doing this in two parts because only `read_response` will require a GPU. Our app will run on Hugging Faces [ZeroGPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU func", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "GPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU function as it will needlessly use our GPU quota.\n\n", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "As mentioned above, we'll use [Hugging Face's Inference API](https://huggingface.co/docs/huggingface_hub/guides/inference) to transcribe the audio and generate a response from an LLM. After instantiating the client, I use the `automatic_speech_recognition` method (this automatically uses Whisper running on Hugging Face's Inference Servers) to transcribe the audio. Then I pass the question to an LLM (Mistal-7B-Instruct) to generate a response. We are prompting the LLM to act like a magic 8 ball with the system message.\n\nOur `generate_response` function will also send empty updates to the output textbox and audio components (returning `None`). \nThis is because I want the Gradio progress tracker to be displayed over the components but I don't want to display the answer until the audio is ready.\n\n\n```python\nfrom huggingface_hub import InferenceClient\n\nclient = InferenceClient(token=os.getenv(\"HF_TOKEN\"))\n\ndef generate_response(audio):\n gr.Info(\"Transcribing Audio\", duration=5)\n question = client.automatic_speech_recognition(audio).text\n\n messages = [{\"role\": \"system\", \"content\": (\"You are a magic 8 ball.\"\n \"Someone will present to you a situation or question and your job \"\n \"is to answer with a cryptic adage or proverb such as \"\n \"'curiosity killed the cat' or 'The early bird gets the worm'.\"\n \"Keep your answers short and do not include the phrase 'Magic 8 Ball' in your response. If the question does not make sense or is off-topic, say 'Foolish questions get foolish answers.'\"\n \"For example, 'Magic 8 Ball, should I get a dog?', 'A dog is ready for you but are you ready for the dog?'\")},\n {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question - {question}\"}]\n \n response = client.chat_completion(messages,", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "for you but are you ready for the dog?'\")},\n {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question - {question}\"}]\n \n response = client.chat_completion(messages, max_tokens=64, seed=random.randint(1, 5000),\n model=\"mistralai/Mistral-7B-Instruct-v0.3\")\n\n response = response.choices[0].message.content.replace(\"Magic 8 Ball\", \"\").replace(\":\", \"\")\n return response, None, None\n```\n\n\nNow that we have our text response, we'll read it aloud with Parler TTS. The `read_response` function will be a python generator that yields the next chunk of audio as it's ready.\n\n\nWe'll be using the [Mini v0.1](https://huggingface.co/parler-tts/parler_tts_mini_v0.1) for the feature extraction but the [Jenny fine tuned version](https://huggingface.co/parler-tts/parler-tts-mini-jenny-30H) for the voice. This is so that the voice is consistent across generations.\n\n\nStreaming audio with transformers requires a custom Streamer class. You can see the implementation [here](https://huggingface.co/spaces/gradio/magic-8-ball/blob/main/streamer.py). Additionally, we'll convert the output to bytes so that it can be streamed faster from the backend. \n\n\n```python\nfrom streamer import ParlerTTSStreamer\nfrom transformers import AutoTokenizer, AutoFeatureExtractor, set_seed\nimport numpy as np\nimport spaces\nimport torch\nfrom threading import Thread\n\n\ndevice = \"cuda:0\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\ntorch_dtype = torch.float16 if device != \"cpu\" else torch.float32\n\nrepo_id = \"parler-tts/parler_tts_mini_v0.1\"\n\njenny_repo_id = \"ylacombe/parler-tts-mini-jenny-30H\"\n\nmodel = ParlerTTSForConditionalGeneration.from_pretrained(\n jenny_repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nf", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "sage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nframe_rate = model.audio_encoder.config.frame_rate\n\n@spaces.GPU\ndef read_response(answer):\n\n play_steps_in_s = 2.0\n play_steps = int(frame_rate * play_steps_in_s)\n\n description = \"Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality.\"\n description_tokens = tokenizer(description, return_tensors=\"pt\").to(device)\n\n streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)\n prompt = tokenizer(answer, return_tensors=\"pt\").to(device)\n\n generation_kwargs = dict(\n input_ids=description_tokens.input_ids,\n prompt_input_ids=prompt.input_ids,\n streamer=streamer,\n do_sample=True,\n temperature=1.0,\n min_new_tokens=10,\n )\n\n set_seed(42)\n thread = Thread(target=model.generate, kwargs=generation_kwargs)\n thread.start()\n\n for new_audio in streamer:\n print(f\"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds\")\n yield answer, numpy_to_mp3(new_audio, sampling_rate=sampling_rate)\n```\n\n", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "You can see our final application [here](https://huggingface.co/spaces/gradio/magic-8-ball)!\n\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "Start by installing all the dependencies. Add the following lines to a `requirements.txt` file and run `pip install -r requirements.txt`:\n\n```bash\nopencv-python\nfastrtc\nonnxruntime-gpu\n```\n\nWe'll use the ONNX runtime to speed up YOLOv10 inference. This guide assumes you have access to a GPU. If you don't, change `onnxruntime-gpu` to `onnxruntime`. Without a GPU, the model will run slower, resulting in a laggy demo.\n\nWe'll use OpenCV for image manipulation and the [WebRTC](https://webrtc.org/) protocol to achieve near-zero latency.\n\n**Note**: If you want to deploy this app on any cloud provider, you'll need to use your Hugging Face token to connect to a TURN server. Learn more in this [guide](https://fastrtc.org/deployment/). If you're not familiar with TURN servers, consult this [guide](https://www.twilio.com/docs/stun-turn/faqfaq-what-is-nat).\n\n", "heading1": "Setting up", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "We'll download the YOLOv10 model from the Hugging Face hub and instantiate a custom inference class to use this model. \n\nThe implementation of the inference class isn't covered in this guide, but you can find the source code [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n/blob/main/inference.pyL9) if you're interested. This implementation borrows heavily from this [github repository](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection).\n\nWe're using the `yolov10-n` variant because it has the lowest latency. See the [Performance](https://github.com/THU-MIG/yolov10?tab=readme-ov-fileperformance) section of the README in the YOLOv10 GitHub repository.\n\n```python\nfrom huggingface_hub import hf_hub_download\nfrom inference import YOLOv10\n\nmodel_file = hf_hub_download(\n repo_id=\"onnx-community/yolov10n\", filename=\"onnx/model.onnx\"\n)\n\nmodel = YOLOv10(model_file)\n\ndef detection(image, conf_threshold=0.3):\n image = cv2.resize(image, (model.input_width, model.input_height))\n new_image = model.detect_objects(image, conf_threshold)\n return new_image\n```\n\nOur inference function, `detection`, accepts a numpy array from the webcam and a desired confidence threshold. Object detection models like YOLO identify many objects and assign a confidence score to each. The lower the confidence, the higher the chance of a false positive. We'll let users adjust the confidence threshold.\n\nThe function returns a numpy array corresponding to the same input image with all detected objects in bounding boxes.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "The Gradio demo is straightforward, but we'll implement a few specific features:\n\n1. Use the `WebRTC` custom component to ensure input and output are sent to/from the server with WebRTC. \n2. The [WebRTC](https://github.com/freddyaboulton/gradio-webrtc) component will serve as both an input and output component.\n3. Utilize the `time_limit` parameter of the `stream` event. This parameter sets a processing time for each user's stream. In a multi-user setting, such as on Spaces, we'll stop processing the current user's stream after this period and move on to the next. \n\nWe'll also apply custom CSS to center the webcam and slider on the page.\n\n```python\nimport gradio as gr\nfrom fastrtc import WebRTC\n\ncss = \"\"\".my-group {max-width: 600px !important; max-height: 600px !important;}\n .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n gr.HTML(\n \"\"\"\n

\n YOLOv10 Webcam Stream (Powered by WebRTC \u26a1\ufe0f)\n

\n \"\"\"\n )\n with gr.Column(elem_classes=[\"my-column\"]):\n with gr.Group(elem_classes=[\"my-group\"]):\n image = WebRTC(label=\"Stream\", rtc_configuration=rtc_configuration)\n conf_threshold = gr.Slider(\n label=\"Confidence Threshold\",\n minimum=0.0,\n maximum=1.0,\n step=0.05,\n value=0.30,\n )\n\n image.stream(\n fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10\n )\n\nif __name__ == \"__main__\":\n demo.launch()\n```\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "Our app is hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n). \n\nYou can use this app as a starting point to build real-time image applications with Gradio. Don't hesitate to open issues in the space or in the [FastRTC GitHub repo](https://github.com/gradio-app/fastrtc) if you have any questions or encounter problems.", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "Modern voice applications should feel natural and responsive, moving beyond the traditional \"click-to-record\" pattern. By combining Groq's fast inference capabilities with automatic speech detection, we can create a more intuitive interaction model where users can simply start talking whenever they want to engage with the AI.\n\n> Credits: VAD and Gradio code inspired by [WillHeld's Diva-audio-chat](https://huggingface.co/spaces/WillHeld/diva-audio-chat/tree/main).\n\nIn this tutorial, you will learn how to create a multimodal Gradio and Groq app that has automatic speech detection. You can also watch the full video tutorial which includes a demo of the application:\n\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Many voice apps currently work by the user clicking record, speaking, then stopping the recording. While this can be a powerful demo, the most natural mode of interaction with voice requires the app to dynamically detect when the user is speaking, so they can talk back and forth without having to continually click a record button. \n\nCreating a natural interaction with voice and text requires a dynamic and low-latency response. Thus, we need both automatic voice detection and fast inference. With @ricky0123/vad-web powering speech detection and Groq powering the LLM, both of these requirements are met. Groq provides a lightning fast response, and Gradio allows for easy creation of impressively functional apps.\n\nThis tutorial shows you how to build a calorie tracking app where you speak to an AI that automatically detects when you start and stop your response, and provides its own text response back to guide you with questions that allow it to give a calorie estimate of your last meal.\n\n", "heading1": "Background", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "- **Gradio**: Provides the web interface and audio handling capabilities\n- **@ricky0123/vad-web**: Handles voice activity detection\n- **Groq**: Powers fast LLM inference for natural conversations\n- **Whisper**: Transcribes speech to text\n\nSetting Up the Environment\n\nFirst, let\u2019s install and import our essential libraries and set up a client for using the Groq API. Here\u2019s how to do it:\n\n`requirements.txt`\n```\ngradio\ngroq\nnumpy\nsoundfile\nlibrosa\nspaces\nxxhash\ndatasets\n```\n\n`app.py`\n```python\nimport groq\nimport gradio as gr\nimport soundfile as sf\nfrom dataclasses import dataclass, field\nimport os\n\nInitialize Groq client securely\napi_key = os.environ.get(\"GROQ_API_KEY\")\nif not api_key:\n raise ValueError(\"Please set the GROQ_API_KEY environment variable.\")\nclient = groq.Client(api_key=api_key)\n```\n\nHere, we\u2019re pulling in key libraries to interact with the Groq API, build a sleek UI with Gradio, and handle audio data. We\u2019re accessing the Groq API key securely with a key stored in an environment variable, which is a security best practice for avoiding leaking the API key.\n\n---\n\nState Management for Seamless Conversations\n\nWe need a way to keep track of our conversation history, so the chatbot remembers past interactions, and manage other states like whether recording is currently active. To do this, let\u2019s create an `AppState` class:\n\n```python\n@dataclass\nclass AppState:\n conversation: list = field(default_factory=list)\n stopped: bool = False\n model_outs: Any = None\n```\n\nOur `AppState` class is a handy tool for managing conversation history and tracking whether recording is on or off. Each instance will have its own fresh list of conversations, making sure chat history is isolated to each session. \n\n---\n\nTranscribing Audio with Whisper on Groq\n\nNext, we\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meani", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "e\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meaningful speech in the input. Here\u2019s how:\n\n```python\ndef transcribe_audio(client, file_name):\n if file_name is None:\n return None\n\n try:\n with open(file_name, \"rb\") as audio_file:\n response = client.audio.transcriptions.with_raw_response.create(\n model=\"whisper-large-v3-turbo\",\n file=(\"audio.wav\", audio_file),\n response_format=\"verbose_json\",\n )\n completion = process_whisper_response(response.parse())\n return completion\n except Exception as e:\n print(f\"Error in transcription: {e}\")\n return f\"Error in transcription: {str(e)}\"\n```\n\nThis function opens the audio file and sends it to Groq\u2019s Whisper model for transcription, requesting detailed JSON output. verbose_json is needed to get information to determine if speech was included in the audio. We also handle any potential errors so our app doesn\u2019t fully crash if there\u2019s an issue with the API request. \n\n```python\ndef process_whisper_response(completion):\n \"\"\"\n Process Whisper transcription response and return text or null based on no_speech_prob\n \n Args:\n completion: Whisper transcription response object\n \n Returns:\n str or None: Transcribed text if no_speech_prob <= 0.7, otherwise None\n \"\"\"\n if completion.segments and len(completion.segments) > 0:\n no_speech_prob = completion.segments[0].get('no_speech_prob', 0)\n print(\"No speech prob:\", no_speech_prob)\n\n if no_speech_prob > 0.7:\n return None\n \n return completion.text.strip()\n \n return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was j", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ext.strip()\n \n return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was just background noise or had actual speaking that was transcribed. It uses a threshold of 0.7 to interpret the no_speech_prob, and will return None if there was no speech. Otherwise, it will return the text transcript of the conversational response from the human.\n\n\n---\n\nAdding Conversational Intelligence with LLM Integration\n\nOur chatbot needs to provide intelligent, friendly responses that flow naturally. We\u2019ll use a Groq-hosted Llama-3.2 for this:\n\n```python\ndef generate_chat_completion(client, history):\n messages = []\n messages.append(\n {\n \"role\": \"system\",\n \"content\": \"In conversation with the user, ask questions to estimate and provide (1) total calories, (2) protein, carbs, and fat in grams, (3) fiber and sugar content. Only ask *one question at a time*. Be conversational and natural.\",\n }\n )\n\n for message in history:\n messages.append(message)\n\n try:\n completion = client.chat.completions.create(\n model=\"llama-3.2-11b-vision-preview\",\n messages=messages,\n )\n return completion.choices[0].message.content\n except Exception as e:\n return f\"Error in generating chat completion: {str(e)}\"\n```\n\nWe\u2019re defining a system prompt to guide the chatbot\u2019s behavior, ensuring it asks one question at a time and keeps things conversational. This setup also includes error handling to ensure the app gracefully manages any issues.\n\n---\n\nVoice Activity Detection for Hands-Free Interaction\n\nTo make our chatbot hands-free, we\u2019ll add Voice Activity Detection (VAD) to automatically detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n const script1 = document.createElement(\"script\");\n scrip", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ly detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n const script1 = document.createElement(\"script\");\n script1.src = \"https://cdn.jsdelivr.net/npm/onnxruntime-web@1.14.0/dist/ort.js\";\n document.head.appendChild(script1)\n const script2 = document.createElement(\"script\");\n script2.onload = async () => {\n console.log(\"vad loaded\");\n var record = document.querySelector('.record-button');\n record.textContent = \"Just Start Talking!\"\n \n const myvad = await vad.MicVAD.new({\n onSpeechStart: () => {\n var record = document.querySelector('.record-button');\n var player = document.querySelector('streaming-out')\n if (record != null && (player == null || player.paused)) {\n record.click();\n }\n },\n onSpeechEnd: (audio) => {\n var stop = document.querySelector('.stop-button');\n if (stop != null) {\n stop.click();\n }\n }\n })\n myvad.start()\n }\n script2.src = \"https://cdn.jsdelivr.net/npm/@ricky0123/vad-web@0.0.7/dist/bundle.min.js\";\n}\n```\n\nThis script loads our VAD model and sets up functions to start and stop recording automatically. When the user starts speaking, it triggers the recording, and when they stop, it ends the recording.\n\n---\n\nBuilding a User Interface with Gradio\n\nNow, let\u2019s create an intuitive and visually appealing user interface with Gradio. This interface will include an audio input for capturing voice, a chat window for displaying responses, and state management to keep things synchronized.\n\n```python\nwith gr.Blocks() as demo:\n with gr.Row():\n input_audio = gr.Audio(\n label=\"Input Audio\",\n sources=[\"microphone\"],\n type=\"numpy\",\n streaming=False,\n waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n )\n with gr.Row():\n chatbot = gr.Chatbot(label=\"Conversation\")\n state = g", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "\",\n streaming=False,\n waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n )\n with gr.Row():\n chatbot = gr.Chatbot(label=\"Conversation\")\n state = gr.State(value=AppState())\ndemo.launch(theme=theme, js=js)\n```\n\nIn this code block, we\u2019re using Gradio\u2019s `Blocks` API to create an interface with an audio input, a chat display, and an application state manager. The color customization for the waveform adds a nice visual touch.\n\n---\n\nHandling Recording and Responses\n\nFinally, let\u2019s link the recording and response components to ensure the app reacts smoothly to user inputs and provides responses in real-time.\n\n```python\n stream = input_audio.start_recording(\n process_audio,\n [input_audio, state],\n [input_audio, state],\n )\n respond = input_audio.stop_recording(\n response, [state, input_audio], [state, chatbot]\n )\n```\n\nThese lines set up event listeners for starting and stopping the recording, processing the audio input, and generating responses. By linking these events, we create a cohesive experience where users can simply talk, and the chatbot handles the rest.\n\n---\n\n", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "1. When you open the app, the VAD system automatically initializes and starts listening for speech\n2. As soon as you start talking, it triggers the recording automatically\n3. When you stop speaking, the recording ends and:\n - The audio is transcribed using Whisper\n - The transcribed text is sent to the LLM\n - The LLM generates a response about calorie tracking\n - The response is displayed in the chat interface\n4. This creates a natural back-and-forth conversation where you can simply talk about your meals and get instant feedback on nutritional content\n\nThis app demonstrates how to create a natural voice interface that feels responsive and intuitive. By combining Groq's fast inference with automatic speech detection, we've eliminated the need for manual recording controls while maintaining high-quality interactions. The result is a practical calorie tracking assistant that users can simply talk to as naturally as they would to a human nutritionist.\n\nLink to GitHub repository: [Groq Gradio Basics](https://github.com/bklieger-groq/gradio-groq-basics/tree/main/calorie-tracker)", "heading1": "Summary", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Use `gradio.Server` instead of `gr.Blocks` when any of the following apply:\n\n- You want a **completely custom (potentially vibe-coded) UI** (your own HTML, React, Svelte, etc.) powered by Gradio's backend\n- You want **full FastAPI control** (custom GET/POST routes, middleware, dependency injection) alongside Gradio API endpoints\n- You're building a service to **host on Spaces** with or without ZeroGPU but don't need Gradio components\n\nIf you're happy with Gradio's built-in UI components, use `gr.Blocks`, `gr.ChatInterface`, or `gr.Interface` instead.\n\n", "heading1": "When to use `gradio.Server`", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "`gradio.Server` is included in the main Gradio package. If you want MCP support, install the extra:\n\n```bash\npip install \"gradio[mcp]\"\n```\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "Here's the simplest possible Server mode app \u2014 a single API endpoint with no UI:\n\n```python\nfrom gradio import Server\n\napp = Server()\n\n@app.api(name=\"hello\")\ndef hello(name: str) -> str:\n return f\"Hello, {name}!\"\n\napp.launch()\n```\n\nThat's it. When you run this script, you get:\n\n- A Gradio API endpoint at `/gradio_api/call/hello` with queuing and SSE streaming\n- Auto-generated API docs at `/gradio_api/info`\n- A Python and JavaScript client that can call `/hello` by name\n\nYou can test it with the Gradio Python client:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"http://localhost:7860\")\nresult = client.predict(\"World\", api_name=\"/hello\")\nprint(result) \"Hello, World!\"\n```\n\n", "heading1": "A Minimal Example", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "Since `gradio.Server` inherits from FastAPI, you can add any route directly:\n\n```python\nfrom gradio import Server\nfrom fastapi.responses import HTMLResponse\n\napp = Server()\n\n@app.api(name=\"hello\")\ndef hello(name: str) -> str:\n return f\"Hello, {name}!\"\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def homepage():\n return \"

Welcome to my API

\"\n\n@app.get(\"/health\")\nasync def health():\n return {\"status\": \"ok\"}\n\napp.launch()\n```\n\nYour custom routes take priority over Gradio's default routes. For example, your `GET /` replaces Gradio's default UI page.\n\nYou can also use all standard FastAPI features \u2014 `app.add_middleware()`, `app.include_router()`, dependency injection, exception handlers, and so on.\n\n", "heading1": "Custom Routes", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "To expose your API endpoints as MCP tools, add the `@app.mcp.tool()` decorator and pass `mcp_server=True` to `launch()`:\n\n```python\nfrom gradio import Server\n\napp = Server()\n\n@app.mcp.tool(name=\"hello\")\n@app.api(name=\"hello\")\ndef hello(name: str) -> str:\n \"\"\"Greet someone by name.\"\"\"\n return f\"Hello, {name}!\"\n\napp.launch(mcp_server=True)\n```\n\nThe `@app.mcp.tool()` and `@app.api()` decorators are independent \u2014 you can have API-only endpoints or MCP-only tools. Stack both when you want a function available through both.\n\n", "heading1": "MCP Tools", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "This example combines everything: custom HTML served at `/`, Gradio API endpoints with concurrency limits, MCP tools, and a custom REST endpoint, and two connected via [the Gradio JavaScript client](/guides/getting-started-with-the-js-client).\n\n$code_server_app\n\nRun it with:\n\n```bash\npython run.py\n```\n\nThen open `http://localhost:7860` in your browser. The custom HTML page uses the `@gradio/client` JavaScript library to call the Gradio API endpoints. Meanwhile, the same endpoints are available as MCP tools and through the REST API at `/gradio_api/call/add` and `/gradio_api/call/multiply`.\n\nNote: if your `Server` app uses ZeroGPU, you _must_ call Gradio API endpoints through `@gradio/client` from the browser. The JavaScript client forwards the Hugging Face iframe auth headers needed for ZeroGPU quota handling.\n\n", "heading1": "A Complete Example with the JavaScript Client", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "`app.api()` supports all of the same concurrency and streaming options as `gr.api()`:\n\n```python\n@app.api(name=\"generate\", concurrency_limit=2, stream_every=0.5)\nasync def generate(prompt: str):\n for token in model.generate(prompt):\n yield token\n```\n\nGenerator functions automatically stream results via SSE, just like in a regular Gradio app. The `concurrency_limit` parameter controls how many concurrent calls to this endpoint are allowed. By default, this is set to 1, since many ML workloads that run on GPU can only support a single user at a time. However, you can increase this, or set to `None` to use FastAPI defaults, if you are e.g. calling an external API.\n\nFor the full API reference, see the [`Server` documentation](/docs/gradio/server).\n", "heading1": "Concurrency and Streaming", "source_page_url": "https://gradio.app/guides/server-mode", "source_page_title": "Gradio Clients And Lite - Server Mode Guide"}, {"text": "Install the @gradio/client package to interact with Gradio APIs using Node.js version >=18.0.0 or in browser-based projects. Use npm or any compatible package manager:\n\n```bash\nnpm i @gradio/client\n```\n\nThis command adds @gradio/client to your project dependencies, allowing you to import it in your JavaScript or TypeScript files.\n\n", "heading1": "Installation via npm", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "For quick addition to your web project, you can use the jsDelivr CDN to load the latest version of @gradio/client directly into your HTML:\n\n```html\n\n```\n\nBe sure to add this to the `` of your HTML. This will install the latest version but we advise hardcoding the version in production. You can find all available versions [here](https://www.jsdelivr.com/package/npm/@gradio/client). This approach is ideal for experimental or prototying purposes, though has some limitations. A complete example would look like this:\n\n```html\n\n\n\n \n\n\n```\n\n", "heading1": "Installation via CDN", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Start by connecting instantiating a `client` instance and connecting it to a Gradio app that is running on Hugging Face Spaces or generally anywhere on the web.\n\n", "heading1": "Connecting to a running Gradio App", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\"); // a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `token` property of the options parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/my-private-space\", { token: \"hf_...\" })\n```\n\n", "heading1": "Connecting to a Hugging Face Space", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space, and then use it to make as many requests as you'd like! You'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens)).\n\n`Client.duplicate` is almost identical to `Client.connect`, the only difference is under the hood:\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", { token: \"hf_...\" });\nconst transcription = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\nIf you have previously duplicated a Space, re-running `Client.duplicate` will _not_ create a new Space. Instead, the client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate` method multiple times with the same space.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 5 minutes of inactivity. You can also set the hardware using the `hardware` and `timeout` properties of `duplicate`'s options object like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", {\n\ttoken: \"hf_...\",\n\ttimeout: 60,\n\thardware: \"a10g-small\"\n});\n```\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = Client.connect(\"https://bec81a83-5b5c-471e.gradio.live\");\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nClient.connect(\n space_name,\n { auth: [username, password] }\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client`'s `view_api` method.\n\nFor the Whisper Space, we can do this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/whisper\");\n\nconst app_info = await app.view_api();\n\nconsole.log(app_info);\n```\n\nAnd we will see the following:\n\n```json\n{\n\t\"named_endpoints\": {\n\t\t\"/predict\": {\n\t\t\t\"parameters\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"text\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t],\n\t\t\t\"returns\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"output\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t]\n\t\t}\n\t},\n\t\"unnamed_endpoints\": {}\n}\n```\n\nThis shows us that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `string`, which is a url to a file.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available. If an app has unnamed API endpoints, these can also be displayed by running `.view_api(all_endpoints=True)`.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the JS Client.\n\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` method with the appropriate arguments:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst result = await app.predict(\"/predict\", [\"Hello\"]);\n```\n\nIf there are multiple parameters, then you should pass them as an array to `.predict()`, like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/calculator\");\nconst result = await app.predict(\"/predict\", [4, \"add\", 5]);\n```\n\nFor certain inputs, such as images, you should pass in a `Buffer`, `Blob` or `File` depending on what is most convenient. In node, this would be a `Buffer` or `Blob`; in a browser environment, this would be a `Blob` or `File`.\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.connect(\"abidlabs/whisper\");\nconst result = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the API you are working with can return results over time, or you wish to access information about the status of a job, you can use the iterable interface for more flexibility. This is especially useful for iterative endpoints or generator endpoints that will produce a series of values over time as discrete responses.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_result(payload) {\n\tconst {\n\t\tdata: [translation]\n\t} = payload;\n\n\tconsole.log(`The translated result is: ${translation}`);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tlog_result(message);\n}\n```\n\n", "heading1": "Using events", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The event interface also allows you to get the status of the running job by instantiating the client with the `events` options passing `status` and `data` as an array:\n\n\n```ts\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\n```\n\nThis ensures that status messages are also reported to the client.\n\n`status`es are returned as an object with the following attributes: `status` (a human readbale status of the current job, `\"pending\" | \"generating\" | \"complete\" | \"error\"`), `code` (the detailed gradio code for the job), `position` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` ( as `Date` object detailing the time that the status was generated).\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_status(status) {\n\tconsole.log(\n\t\t`The current status for this job is: ${JSON.stringify(status, null, 2)}.`\n\t);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tif (message.type === \"status\") {\n\t\tlog_status(message);\n\t}\n}\n```\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The job instance also has a `.cancel()` method that cancels jobs that have been queued but not started. For example, if you run:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job_one = app.submit(\"/predict\", [\"Hello\"]);\nconst job_two = app.submit(\"/predict\", [\"Friends\"]);\n\njob_one.cancel();\njob_two.cancel();\n```\n\nIf the first job has started processing, then it will not be canceled but the client will no longer listen for updates (throwing away the job). If the second job has not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can listen for these values in real time using the iterable interface:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n```\n\nThis will log out the values as they are generated by the endpoint.\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish immediately.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n\nsetTimeout(() => {\n\tjob.cancel();\n}, 3000);\n```\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Let's start with what seems like the most complex bit -- using machine learning to remove the music from a video.\n\nLuckily for us, there's an existing Space we can use to make this process easier: [https://huggingface.co/spaces/abidlabs/music-separation](https://huggingface.co/spaces/abidlabs/music-separation). This Space takes an audio file and produces two separate audio files: one with the instrumental music and one with all other sounds in the original clip. Perfect to use with our client!\n\nOpen a new Python file, say `main.py`, and start by importing the `Client` class from `gradio_client` and connecting it to this Space:\n\n```py\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/music-separation\")\n\ndef acapellify(audio_path):\n result = client.predict(handle_file(audio_path), api_name=\"/predict\")\n return result[0]\n```\n\nThat's all the code that's needed -- notice that the API endpoints returns two audio files (one without the music, and one with just the music) in a list, and so we just return the first element of the list.\n\n---\n\n**Note**: since this is a public Space, there might be other users using this Space as well, which might result in a slow experience. You can duplicate this Space with your own [Hugging Face token](https://huggingface.co/settings/tokens) and create a private Space that only you have will have access to and bypass the queue. To do that, simply replace the first two lines above with:\n\n```py\nfrom gradio_client import Client\n\nclient = Client.duplicate(\"abidlabs/music-separation\", token=YOUR_HF_TOKEN)\n```\n\nEverything else remains the same!\n\n---\n\nNow, of course, we are working with video files, so we first need to extract the audio from the video files. For this, we will be using the `ffmpeg` library, which does a lot of heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video proc", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "f heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video processing workflow will consist of three steps:\n\n1. First, we start by taking in a video filepath and extracting the audio using `ffmpeg`.\n2. Then, we pass in the audio file through the `acapellify()` function above.\n3. Finally, we combine the new audio with the original video to produce a final acapellified video.\n\nHere's the complete code in Python, which you can add to your `main.py` file:\n\n```python\nimport subprocess\n\ndef process_video(video_path):\n old_audio = os.path.basename(video_path).split(\".\")[0] + \".m4a\"\n subprocess.run(['ffmpeg', '-y', '-i', video_path, '-vn', '-acodec', 'copy', old_audio])\n\n new_audio = acapellify(old_audio)\n\n new_video = f\"acap_{video_path}\"\n subprocess.call(['ffmpeg', '-y', '-i', video_path, '-i', new_audio, '-map', '0:v', '-map', '1:a', '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental', f\"static/{new_video}\"])\n return new_video\n```\n\nYou can read up on [ffmpeg documentation](https://ffmpeg.org/ffmpeg.html) if you'd like to understand all of the command line parameters, as they are beyond the scope of this tutorial.\n\n", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Next up, we'll create a simple FastAPI app. If you haven't used FastAPI before, check out [the great FastAPI docs](https://fastapi.tiangolo.com/). Otherwise, this basic template, which we add to `main.py`, will look pretty familiar:\n\n```python\nimport os\nfrom fastapi import FastAPI, File, UploadFile, Request\nfrom fastapi.responses import HTMLResponse, RedirectResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\n\napp = FastAPI()\nos.makedirs(\"static\", exist_ok=True)\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\ntemplates = Jinja2Templates(directory=\"templates\")\n\nvideos = []\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def home(request: Request):\n return templates.TemplateResponse(\n \"home.html\", {\"request\": request, \"videos\": videos})\n\n@app.post(\"/uploadvideo/\")\nasync def upload_video(video: UploadFile = File(...)):\n video_path = video.filename\n with open(video_path, \"wb+\") as fp:\n fp.write(video.file.read())\n\n new_video = process_video(video.filename)\n videos.append(new_video)\n return RedirectResponse(url='/', status_code=303)\n```\n\nIn this example, the FastAPI app has two routes: `/` and `/uploadvideo/`.\n\nThe `/` route returns an HTML template that displays a gallery of all uploaded videos.\n\nThe `/uploadvideo/` route accepts a `POST` request with an `UploadFile` object, which represents the uploaded video file. The video file is \"acapellified\" via the `process_video()` method, and the output video is stored in a list which stores all of the uploaded videos in memory.\n\nNote that this is a very basic example and if this were a production app, you will need to add more logic to handle file storage, user authentication, and security considerations.\n\n", "heading1": "Step 2: Create a FastAPI app (Backend Routes)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we create the frontend of our web application. First, we create a folder called `templates` in the same directory as `main.py`. We then create a template, `home.html` inside the `templates` folder. Here is the resulting file structure:\n\n```csv\n\u251c\u2500\u2500 main.py\n\u251c\u2500\u2500 templates\n\u2502 \u2514\u2500\u2500 home.html\n```\n\nWrite the following as the contents of `home.html`:\n\n```html\n<!DOCTYPE html> <html> <head> <title>Video Gallery</title>\n<style> body { font-family: sans-serif; margin: 0; padding: 0;\nbackground-color: f5f5f5; } h1 { text-align: center; margin-top: 30px;\nmargin-bottom: 20px; } .gallery { display: flex; flex-wrap: wrap;\njustify-content: center; gap: 20px; padding: 20px; } .video { border: 2px solid\nccc; box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.2); border-radius: 5px; overflow:\nhidden; width: 300px; margin-bottom: 20px; } .video video { width: 100%; height:\n200px; } .video p { text-align: center; margin: 10px 0; } form { margin-top:\n20px; text-align: center; } input[type=\"file\"] { display: none; } .upload-btn {\ndisplay: inline-block; background-color: 3498db; color: fff; padding: 10px\n20px; font-size: 16px; border: none; border-radius: 5px; cursor: pointer; }\n.upload-btn:hover { background-color: 2980b9; } .file-name { margin-left: 10px;\n} </style> </head> <body> <h1>Video Gallery</h1> {% if videos %}\n<div class=\"gallery\"> {% for video in videos %} <div class=\"video\">\n<video controls> <source src=\"{{ url_for('static', path=video) }}\"\ntype=\"video/mp4\"> Your browser does not support the video tag. </video>\n<p>{{ video }}</p> </div> {% endfor %} </div> {% else %} <p>No\nvideos uploaded yet.</p> {% endif %} <form action=\"/uploadvideo/\"\nmethod=\"post\" enctype=\"multipart/form-data\"> <label for=\"video-upload\"\nclass=\"upload-btn\">Choose video file</label> <input type=\"file\"\nname=\"video\" id=\"video-upload\"> <span class=\"file-name\"></span> <button\ntype=\"submit\" class=\"upload-btn\">Upload</butto", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "class=\"upload-btn\">Choose video file</label> <input type=\"file\"\nname=\"video\" id=\"video-upload\"> <span class=\"file-name\"></span> <button\ntype=\"submit\" class=\"upload-btn\">Upload</button> </form> <script> //\nDisplay selected file name in the form const fileUpload =\ndocument.getElementById(\"video-upload\"); const fileName =\ndocument.querySelector(\".file-name\"); fileUpload.addEventListener(\"change\", (e)\n=> { fileName.textContent = e.target.files[0].name; }); </script> </body>\n</html>\n```\n\n", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we are ready to run our FastAPI app, powered by the Gradio Python Client!\n\nOpen up a terminal and navigate to the directory containing `main.py`. Then run the following command in the terminal:\n\n```bash\n$ uvicorn main:app\n```\n\nYou should see an output that looks like this:\n\n```csv\nLoaded as API: https://abidlabs-music-separation.hf.space \u2714\nINFO: Started server process [1360]\nINFO: Waiting for application startup.\nINFO: Application startup complete.\nINFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n```\n\nAnd that's it! Start uploading videos and you'll get some \"acapellified\" videos in response (might take seconds to minutes to process depending on the length of your videos). Here's how the UI looks after uploading two videos:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/acapellify.png)\n\nIf you'd like to learn more about how to use the Gradio Python Client in your projects, [read the dedicated Guide](/guides/getting-started-with-the-python-client/).\n", "heading1": "Step 4: Run your FastAPI app", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "What are agents?\n\nA [LangChain agent](https://docs.langchain.com/docs/components/agents/agent) is a Large Language Model (LLM) that takes user input and reports an output based on using one of many tools at its disposal.\n\nWhat is Gradio?\n\n[Gradio](https://github.com/gradio-app/gradio) is the defacto standard framework for building Machine Learning Web Applications and sharing them with the world - all with just python! \ud83d\udc0d\n\n", "heading1": "Some background", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "To get started with `gradio_tools`, all you need to do is import and initialize your tools and pass them to the langchain agent!\n\nIn the following example, we import the `StableDiffusionPromptGeneratorTool` to create a good prompt for stable diffusion, the\n`StableDiffusionTool` to create an image with our improved prompt, the `ImageCaptioningTool` to caption the generated image, and\nthe `TextToVideoTool` to create a video from a prompt.\n\nWe then tell our agent to create an image of a dog riding a skateboard, but to please improve our prompt ahead of time. We also ask\nit to caption the generated image and create a video for it. The agent can decide which tool to use without us explicitly telling it.\n\n```python\nimport os\n\nif not os.getenv(\"OPENAI_API_KEY\"):\n raise ValueError(\"OPENAI_API_KEY must be set\")\n\nfrom langchain.agents import initialize_agent\nfrom langchain.llms import OpenAI\nfrom gradio_tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,\n TextToVideoTool)\n\nfrom langchain.memory import ConversationBufferMemory\n\nllm = OpenAI(temperature=0)\nmemory = ConversationBufferMemory(memory_key=\"chat_history\")\ntools = [StableDiffusionTool().langchain, ImageCaptioningTool().langchain,\n StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain]\n\n\nagent = initialize_agent(tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True)\noutput = agent.run(input=(\"Please create a photo of a dog riding a skateboard \"\n \"but improve my prompt prior to using an image generator.\"\n \"Please caption the generated image and create a video for it using the improved prompt.\"))\n```\n\nYou'll note that we are using some pre-built tools that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf ", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf you would like to use a tool that's not currently in `gradio_tools`, it is very easy to add your own. That's what the next section will cover.\n\n", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "The core abstraction is the `GradioTool`, which lets you define a new tool for your LLM as long as you implement a standard interface:\n\n```python\nclass GradioTool(BaseTool):\n\n def __init__(self, name: str, description: str, src: str) -> None:\n\n @abstractmethod\n def create_job(self, query: str) -> Job:\n pass\n\n @abstractmethod\n def postprocess(self, output: Tuple[Any] | Any) -> str:\n pass\n```\n\nThe requirements are:\n\n1. The name for your tool\n2. The description for your tool. This is crucial! Agents decide which tool to use based on their description. Be precise and be sure to include example of what the input and the output of the tool should look like.\n3. The url or space id, e.g. `freddyaboulton/calculator`, of the Gradio application. Based on this value, `gradio_tool` will create a [gradio client](https://github.com/gradio-app/gradio/blob/main/client/python/README.md) instance to query the upstream application via API. Be sure to click the link and learn more about the gradio client library if you are not familiar with it.\n4. create_job - Given a string, this method should parse that string and return a job from the client. Most times, this is as simple as passing the string to the `submit` function of the client. More info on creating jobs [here](https://github.com/gradio-app/gradio/blob/main/client/python/README.mdmaking-a-prediction)\n5. postprocess - Given the result of the job, convert it to a string the LLM can display to the user.\n6. _Optional_ - Some libraries, e.g. [MiniChain](https://github.com/srush/MiniChain/tree/main), may need some info about the underlying gradio input and output types used by the tool. By default, this will return gr.Textbox() but\n if you'd like to provide more accurate info, implement the `_block_input(self, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n automatically imported by the `GradiTool` parent", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "lf, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n automatically imported by the `GradiTool` parent class and passed to the `_block_input` and `_block_output` methods.\n\nAnd that's it!\n\nOnce you have created your tool, open a pull request to the `gradio_tools` repo! We welcome all contributions.\n\n", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "Here is the code for the StableDiffusion tool as an example:\n\n```python\nfrom gradio_tool import GradioTool\nimport os\n\nclass StableDiffusionTool(GradioTool):\n \"\"\"Tool for calling stable diffusion from llm\"\"\"\n\n def __init__(\n self,\n name=\"StableDiffusion\",\n description=(\n \"An image generator. Use this to generate images based on \"\n \"text input. Input should be a description of what the image should \"\n \"look like. The output will be a path to an image file.\"\n ),\n src=\"gradio-client-demos/stable-diffusion\",\n token=None,\n ) -> None:\n super().__init__(name, description, src, token)\n\n def create_job(self, query: str) -> Job:\n return self.client.submit(query, \"\", 9, fn_index=1)\n\n def postprocess(self, output: str) -> str:\n return [os.path.join(output, i) for i in os.listdir(output) if not i.endswith(\"json\")][0]\n\n def _block_input(self, gr) -> \"gr.components.Component\":\n return gr.Textbox()\n\n def _block_output(self, gr) -> \"gr.components.Component\":\n return gr.Image()\n```\n\nSome notes on this implementation:\n\n1. All instances of `GradioTool` have an attribute called `client` that is a pointed to the underlying [gradio client](https://github.com/gradio-app/gradio/tree/main/client/pythongradio_client-use-a-gradio-app-as-an-api----in-3-lines-of-python). That is what you should use\n in the `create_job` method.\n2. `create_job` just passes the query string to the `submit` function of the client with some other parameters hardcoded, i.e. the negative prompt string and the guidance scale. We could modify our tool to also accept these values from the input string in a subsequent version.\n3. The `postprocess` method simply returns the first image from the gallery of images created by the stable diffusion space. We use the `os` module to get the full path of the image.\n\n", "heading1": "Example tool - Stable Diffusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "You now know how to extend the abilities of your LLM with the 1000s of gradio spaces running in the wild!\nAgain, we welcome any contributions to the [gradio_tools](https://github.com/freddyaboulton/gradio-tools) library.\nWe're excited to see the tools you all build!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "If you already have a recent version of `gradio`, then the `gradio_client` is included as a dependency. But note that this documentation reflects the latest version of the `gradio_client`, so upgrade if you're not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3) and is tested to work with **Python versions 3.10 or higher**:\n\n```bash\n$ pip install --upgrade gradio_client\n```\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Start by connecting instantiating a `Client` object and connecting it to a Gradio app that is running on Hugging Face Spaces.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\") a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `token` parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/my-private-space\", token=\"...\")\n```\n\n\n", "heading1": "Connecting to a Gradio App on Hugging Face Spaces", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this process simple (you'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens) or be logged in using the Hugging Face CLI):\n\n```python\nimport os\nfrom gradio_client import Client, handle_file\n\nHF_TOKEN = os.environ.get(\"HF_TOKEN\")\n\nclient = Client.duplicate(\"abidlabs/whisper\", token=HF_TOKEN)\nclient.predict(handle_file(\"audio_sample.wav\"))\n\n>> \"This is a test of the whisper speech recognition model.\"\n```\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_ create a new Space. Instead, the Client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 1 hour of inactivity. You can also set the hardware using the `hardware` parameter of `duplicate()`.\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```python\nfrom gradio_client import Client\n\nClient(\n space_name,\n auth=[username, password]\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client.view_api()` method. For the Whisper Space, we see the following:\n\n```bash\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(audio, api_name=\"/predict\") -> output\n Parameters:\n - [Audio] audio: filepath (required) \n Returns:\n - [Textbox] output: str \n```\n\nWe see that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the Python Client.\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` function with the appropriate arguments:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\")\nclient.predict(\"Hello\", api_name='/predict')\n\n>> Bonjour\n```\n\nIf there are multiple parameters, then you should pass them as separate arguments to `.predict()`, like this:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(4, \"add\", 5)\n\n>> 9.0\n```\n\nIt is recommended to provide key-word arguments instead of positional arguments:\n\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(num1=4, operation=\"add\", num2=5)\n\n>> 9.0\n```\n\nThis allows you to take advantage of default arguments. For example, this Space includes the default value for the Slider component so you do not need to provide it when accessing it with the client.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\")\n```\n\nThe default value is the initial value of the corresponding Gradio component. If the component does not have an initial value, but if the corresponding argument in the predict function has a default value of `None`, then that parameter is also optional in the client. Of course, if you'd like to override it, you can include it as well:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\", steps=25)\n```\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL to the file enclosed within `gradio_client.handle_file()`. This takes care of uploading the file to the Gradio server and ensures that the file is preprocessed correctly:\n\n```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/s", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n)\n\n>> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "One should note that `.predict()` is a _blocking_ operation as it waits for the operation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background until you need the results of the prediction. You can do this by creating a `Job` instance using the `.submit()` method, and then later calling `.result()` on the job to get the result. For example:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(space=\"abidlabs/en2fr\")\njob = client.submit(\"Hello\", api_name=\"/predict\") This is not blocking\n\nDo something else\n\njob.result() This is blocking\n\n>> Bonjour\n```\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the job has completed running, like this:\n\n```python\nfrom gradio_client import Client\n\ndef print_result(x):\n print(\"The translated result is: {x}\")\n\nclient = Client(space=\"abidlabs/en2fr\")\n\njob = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n\nDo something else\n\n>> The translated result is: Bonjour\n\n```\n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` object also allows you to get the status of the running job by calling the `.status()` method. This returns a `StatusUpdate` object with the following attributes: `code` (the status code, one of a set of defined strings representing the status. See the `utils.Status` class), `rank` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` (the time that the status was generated).\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/calculator\")\njob = client.submit(5, \"add\", 4, api_name=\"/predict\")\njob.status()\n\n>> \n```\n\n_Note_: The `Job` class also has a `.done()` instance method which returns a boolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that have been queued but not started. For example, if you run:\n\n```py\nclient = Client(\"abidlabs/whisper\")\njob1 = client.submit(handle_file(\"audio_sample1.wav\"))\njob2 = client.submit(handle_file(\"audio_sample2.wav\"))\njob1.cancel() will return False, assuming the job has started\njob2.cancel() will return True, indicating that the job has been canceled\n```\n\nIf the first job has started processing, then it will not be canceled. If the second job\nhas not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can get the series of values that have been returned at any time from such a generator endpoint by running `job.outputs()`:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\nwhile not job.done():\n time.sleep(0.1)\njob.outputs()\n\n>> ['0', '1', '2']\n```\n\nNote that running `job.result()` on a generator endpoint only gives you the _first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the results of a generator function as they are returned from the endpoint. Here's the equivalent example using the `Job` as a generator:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\n\nfor o in job:\n print(o)\n\n>> 0\n>> 1\n>> 2\n```\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish as soon as the current iteration finishes running.\n\n```py\nfrom gradio_client import Client\nimport time\n\nclient = Client(\"abidlabs/test-yield\")\njob = client.submit(\"abcdef\")\ntime.sleep(3)\njob.cancel() job cancels after 2 iterations\n```\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-in-blocks), which provides a way for demos to persist information from user interactions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that a user has submitted in a `gr.State` component. When a user submits a new word, it is added to the state, and the number of previous occurrences of that word is displayed:\n\n```python\nimport gradio as gr\n\ndef count(word, list_of_words):\n return list_of_words.count(word), list_of_words + [word]\n\nwith gr.Blocks() as demo:\n words = gr.State([])\n textbox = gr.Textbox()\n number = gr.Number()\n textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n \ndemo.launch()\n```\n\nIf you were to connect this this Gradio app using the Python Client, you would notice that the API information only shows a single input and output:\n\n```csv\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(word, api_name=\"/count\") -> value_31\n Parameters:\n - [Textbox] word: str (required) \n Returns:\n - [Number] value_31: float \n```\n\nThat is because the Python client handles state automatically for you -- as you make a series of requests, the returned state from one request is stored internally and automatically supplied for the subsequent request. If you'd like to reset the state, you can do that by calling `Client.reset_session()`.\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "You generally don't need to install cURL, as it comes pre-installed on many operating systems. Run:\n\n```bash\ncurl --version\n```\n\nto confirm that `curl` is installed. If it is not already installed, you can install it by visiting https://curl.se/download.html. \n\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "To query a Gradio app, you'll need its full URL. This is usually just the URL that the Gradio app is hosted on, for example: https://bec81a83-5b5c-471e.gradio.live\n\n\n**Hugging Face Spaces**\n\nHowever, if you are querying a Gradio on Hugging Face Spaces, you will need to use the URL of the embedded Gradio app, not the URL of the Space webpage. For example:\n\n```bash\n\u274c Space URL: https://huggingface.co/spaces/abidlabs/en2fr\n\u2705 Gradio app URL: https://abidlabs-en2fr.hf.space/\n```\n\nYou can get the Gradio app URL by clicking the \"view API\" link at the bottom of the page. Or, you can right-click on the page and then click on \"View Frame Source\" or the equivalent in your browser to view the URL of the embedded Gradio app.\n\nWhile you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nNote: to query private Spaces, you will need to pass in your Hugging Face (HF) token. You can get your HF token here: https://huggingface.co/settings/tokens. In this case, you will need to include an additional header in both of your `curl` calls that we'll discuss below:\n\n```bash\n-H \"Authorization: Bearer $HF_TOKEN\"\n```\n\nNow, we are ready to make the two `curl` requests.\n\n", "heading1": "Step 0: Get the URL for your Gradio App", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "The first of the two `curl` requests is `POST` request that submits the input payload to the Gradio app. \n\nThe syntax of the `POST` request is as follows:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -H \"Content-Type: application/json\" -d '{\n \"data\": $PAYLOAD\n}'\n```\n\nHere:\n\n* `$URL` is the URL of the Gradio app as obtained in Step 0\n* `$API_NAME` is the name of the API endpoint for the event that you are running. You can get the API endpoint names by clicking the \"view API\" link at the bottom of the page.\n* `$PAYLOAD` is a valid JSON data list containing the input payload, one element for each input component.\n\nWhen you make this `POST` request successfully, you will get an event id that is printed to the terminal in this format:\n\n```bash\n>> {\"event_id\": $EVENT_ID} \n```\n\nThis `EVENT_ID` will be needed in the subsequent `curl` request to fetch the results of the prediction. \n\nHere are some examples of how to make the `POST` request\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, here is how to make the `POST` request for a simple Gradio application that takes in a single input text component:\n\n```bash\n$ curl -X POST https://abidlabs-en2fr.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Multiple Input Components**\n\nThis [Gradio demo](https://huggingface.co/spaces/gradio/hello_world_3) accepts three inputs: a string corresponding to the `gr.Textbox`, a boolean value corresponding to the `gr.Checkbox`, and a numerical value corresponding to the `gr.Slider`. Here is the `POST` request:\n\n```bash\ncurl -X POST https://gradio-hello-world-3.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n \"data\": [\"Hello\", true, 5]\n}'\n```\n\n**Private Spaces**\n\nAs mentioned earlier, if you are making a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "king a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n$ curl -X POST https://private-space.hf.space/call/predict -H \"Content-Type: application/json\" -H \"Authorization: Bearer $HF_TOKEN\" -d '{\n \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Files**\n\nIf you are using `curl` to query a Gradio application that requires file inputs, the files *need* to be provided as URLs, and The URL needs to be enclosed in a dictionary in this format:\n\n```bash\n{\"path\": $URL}\n```\n\nHere is an example `POST` request:\n\n```bash\n$ curl -X POST https://gradio-image-mod.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n \"data\": [{\"path\": \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"}] \n}'\n```\n\n\n**Stateful Demos**\n\nIf your Gradio demo [persists user state](/guides/interface-state) across multiple interactions (e.g. is a chatbot), you can pass in a `session_hash` alongside the `data`. Requests with the same `session_hash` are assumed to be part of the same user session. Here's how that might look:\n\n```bash\nThese two requests will share a session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n \"data\": [\"Are you sentient?\"],\n \"session_hash\": \"randomsequence1234\"\n}'\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n \"data\": [\"Really?\"],\n \"session_hash\": \"randomsequence1234\"\n}'\n\nThis request will be treated as a new session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n \"data\": [\"Are you sentient?\"],\n \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "ient?\"],\n \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "Once you have received the `EVENT_ID` corresponding to your prediction, you can stream the results. Gradio stores these results in a least-recently-used cache in the Gradio app. By default, the cache can store 2,000 results (across all users and endpoints of the app). \n\nTo stream the results for your prediction, make a `GET` request with the following syntax:\n\n```bash\n$ curl -N $URL/call/$API_NAME/$EVENT_ID\n```\n\n\nTip: If you are fetching results from a private Space, include a header with your HF token like this: `-H \"Authorization: Bearer $HF_TOKEN\"` in the `GET` request.\n\nThis should produce a stream of responses in this format:\n\n```bash\nevent: ... \ndata: ...\nevent: ... \ndata: ...\n...\n```\n\nHere: `event` can be one of the following:\n* `generating`: indicating an intermediate result\n* `complete`: indicating that the prediction is complete and the final result \n* `error`: indicating that the prediction was not completed successfully\n* `heartbeat`: sent every 15 seconds to keep the request alive\n\nThe `data` is in the same format as the input payload: valid JSON data list containing the output result, one element for each output component.\n\nHere are some examples of what results you should expect if a request is completed successfully:\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, we would expect the result to look like this:\n\n```bash\nevent: complete\ndata: [\"Bonjour, mon ami.\"]\n```\n\n**Multiple Outputs**\n\nIf your endpoint returns multiple values, they will appear as elements of the `data` list:\n\n```bash\nevent: complete\ndata: [\"Good morning Hello. It is 5 degrees today\", -15.0]\n```\n\n**Streaming Example**\n\nIf your Gradio app [streams a sequence of values](/guides/streaming-outputs), then they will be streamed directly to your terminal, like this:\n\n```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, w", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, world!\"]\nevent: complete\ndata: [\"Hello, world!\"]\n```\n\n**File Example**\n\nIf your Gradio app returns a file, the file will be represented as a dictionary in this format (including potentially some additional keys):\n\n```python\n{\n \"orig_name\": \"example.jpg\",\n \"path\": \"/path/in/server.jpg\",\n \"url\": \"https:/example.com/example.jpg\",\n \"meta\": {\"_type\": \"gradio.FileData\"}\n}\n```\n\nIn your terminal, it may appear like this:\n\n```bash\nevent: complete\ndata: [{\"path\": \"/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"url\": \"https://gradio-image-mod.hf.space/c/file=/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"size\": null, \"orig_name\": \"image.webp\", \"mime_type\": null, \"is_stream\": false, \"meta\": {\"_type\": \"gradio.FileData\"}}]\n```\n\n", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "What if your Gradio application has [authentication enabled](/guides/sharing-your-appauthentication)? In that case, you'll need to make an additional `POST` request with cURL to authenticate yourself before you make any queries. Here are the complete steps:\n\nFirst, login with a `POST` request supplying a valid username and password:\n\n```bash\ncurl -X POST $URL/login \\\n -d \"username=$USERNAME&password=$PASSWORD\" \\\n -c cookies.txt\n```\n\nIf the credentials are correct, you'll get `{\"success\":true}` in response and the cookies will be saved in `cookies.txt`.\n\nNext, you'll need to include these cookies when you make the original `POST` request, like this:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -b cookies.txt -H \"Content-Type: application/json\" -d '{\n \"data\": $PAYLOAD\n}'\n```\n\nFinally, you'll need to `GET` the results, again supplying the cookies from the file:\n\n```bash\ncurl -N $URL/call/$API_NAME/$EVENT_ID -b cookies.txt\n```\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "Global state in Gradio apps is very simple: any variable created outside of a function is shared globally between all users.\n\nThis makes managing global state very simple and without the need for external services. For example, in this application, the `visitor_count` variable is shared between all users\n\n```py\nimport gradio as gr\n\nShared between all users\nvisitor_count = 0\n\ndef increment_counter():\n global visitor_count\n visitor_count += 1\n return visitor_count\n\nwith gr.Blocks() as demo: \n number = gr.Textbox(label=\"Total Visitors\", value=\"Counting...\")\n demo.load(increment_counter, inputs=None, outputs=number)\n\ndemo.launch()\n```\n\nThis means that any time you do _not_ want to share a value between users, you should declare it _within_ a function. But what if you need to share values between function calls, e.g. a chat history? In that case, you should use one of the subsequent approaches to manage state.\n\n", "heading1": "Global State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio supports session state, where data persists across multiple submits within a page session. To reiterate, session data is _not_ shared between different users of your model, and does _not_ persist if a user refreshes the page to reload the Gradio app. To store data in a session state, you need to do three things:\n\n1. Create a `gr.State()` object. If there is a default value to this stateful object, pass that into the constructor. Note that `gr.State` objects must be [deepcopy-able](https://docs.python.org/3/library/copy.html), otherwise you will need to use a different approach as described below.\n2. In the event listener, put the `State` object as an input and output as needed.\n3. In the event listener function, add the variable to the input parameters and the return value.\n\nLet's take a look at a simple example. We have a simple checkout app below where you add items to a cart. You can also see the size of the cart.\n\n$code_simple_state\n\nNotice how we do this with state:\n\n1. We store the cart items in a `gr.State()` object, initialized here to be an empty list.\n2. When adding items to the cart, the event listener uses the cart as both input and output - it returns the updated cart with all the items inside. \n3. We can attach a `.change` listener to cart, that uses the state variable as input as well.\n\nYou can think of `gr.State` as an invisible Gradio component that can store any kind of value. Here, `cart` is not visible in the frontend but is used for calculations.\n\nThe `.change` listener for a state variable triggers after any event listener changes the value of a state variable. If the state variable holds a sequence (like a `list`, `set`, or `dict`), a change is triggered if any of the elements inside change. If it holds an object or primitive, a change is triggered if the **hash** of the value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "riggered if the **hash** of the value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__hash__` implementation.\n\nThe value of a session State variable is cleared when the user refreshes the page. The value is stored on in the app backend for 60 minutes after the user closes the tab (this can be configured by the `delete_cache` parameter in `gr.Blocks`).\n\nLearn more about `State` in the [docs](https://gradio.app/docs/gradio/state).\n\n**What about objects that cannot be deepcopied?**\n\nAs mentioned earlier, the value stored in `gr.State` must be [deepcopy-able](https://docs.python.org/3/library/copy.html). If you are working with a complex object that cannot be deepcopied, you can take a different approach to manually read the user's `session_hash` and store a global `dictionary` with instances of your object for each user. Here's how you would do that:\n\n```py\nimport gradio as gr\n\nclass NonDeepCopyable:\n def __init__(self):\n from threading import Lock\n self.counter = 0\n self.lock = Lock() Lock objects cannot be deepcopied\n \n def increment(self):\n with self.lock:\n self.counter += 1\n return self.counter\n\nGlobal dictionary to store user-specific instances\ninstances = {}\n\ndef initialize_instance(request: gr.Request):\n instances[request.session_hash] = NonDeepCopyable()\n return \"Session initialized!\"\n\ndef cleanup_instance(request: gr.Request):\n if request.session_hash in instances:\n del instances[request.session_hash]\n\ndef increment_counter(request: gr.Request):\n if request.session_hash in instances:\n instance = instances[request.session_hash]\n return instance.increment()\n return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n output = gr.Textbox(label=\"Status\")\n counter = gr.Number(label=\"Counter Value\")\n increment_btn = gr.Button(\"Increment Co", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": " return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n output = gr.Textbox(label=\"Status\")\n counter = gr.Number(label=\"Counter Value\")\n increment_btn = gr.Button(\"Increment Counter\")\n increment_btn.click(increment_counter, inputs=None, outputs=counter)\n \n Initialize instance when page loads\n demo.load(initialize_instance, inputs=None, outputs=output) \n Clean up instance when page is closed/refreshed\n demo.unload(cleanup_instance) \n\ndemo.launch()\n```\n\n", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio also supports browser state, where data persists in the browser's localStorage even after the page is refreshed or closed. This is useful for storing user preferences, settings, API keys, or other data that should persist across sessions. To use local state:\n\n1. Create a `gr.BrowserState` object. You can optionally provide an initial default value and a key to identify the data in the browser's localStorage.\n2. Use it like a regular `gr.State` component in event listeners as inputs and outputs.\n\nHere's a simple example that saves a user's username and password across sessions:\n\n$code_browserstate\n\nNote: The value stored in `gr.BrowserState` does not persist if the Grado app is restarted. To persist it, you can hardcode specific values of `storage_key` and `secret` in the `gr.BrowserState` component and restart the Gradio app on the same server name and server port. However, this should only be done if you are running trusted Gradio apps, as in principle, this can allow one Gradio app to access localStorage data that was created by a different Gradio app.\n", "heading1": "Browser State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Take a look at the demo below.\n\n$code_hello_blocks\n$demo_hello_blocks\n\n- First, note the `with gr.Blocks() as demo:` clause. The Blocks app code will be contained within this clause.\n- Next come the Components. These are the same Components used in `Interface`. However, instead of being passed to some constructor, Components are automatically added to the Blocks as they are created within the `with` clause.\n- Finally, the `click()` event listener. Event listeners define the data flow within the app. In the example above, the listener ties the two Textboxes together. The Textbox `name` acts as the input and Textbox `output` acts as the output to the `greet` method. This dataflow is triggered when the Button `greet_btn` is clicked. Like an Interface, an event listener can take multiple inputs or outputs.\n\nYou can also attach event listeners using decorators - skip the `fn` argument and assign `inputs` and `outputs` directly:\n\n$code_hello_blocks_decorator\n\n", "heading1": "Blocks Structure", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In the example above, you'll notice that you are able to edit Textbox `name`, but not Textbox `output`. This is because any Component that acts as an input to an event listener is made interactive. However, since Textbox `output` acts only as an output, Gradio determines that it should not be made interactive. You can override the default behavior and directly configure the interactivity of a Component with the boolean `interactive` keyword argument, e.g. `gr.Textbox(interactive=True)`.\n\n```python\noutput = gr.Textbox(label=\"Output\", interactive=True)\n```\n\n_Note_: What happens if a Gradio component is neither an input nor an output? If a component is constructed with a default value, then it is presumed to be displaying content and is rendered non-interactive. Otherwise, it is rendered interactive. Again, this behavior can be overridden by specifying a value for the `interactive` argument.\n\n", "heading1": "Event Listeners and Interactivity", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Take a look at the demo below:\n\n$code_blocks_hello\n$demo_blocks_hello\n\nInstead of being triggered by a click, the `welcome` function is triggered by typing in the Textbox `inp`. This is due to the `change()` event listener. Different Components support different event listeners. For example, the `Video` Component supports a `play()` event listener, triggered when a user presses play. See the [Docs](http://gradio.app/docscomponents) for the event listeners for each Component.\n\n", "heading1": "Types of Event Listeners", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "A Blocks app is not limited to a single data flow the way Interfaces are. Take a look at the demo below:\n\n$code_reversible_flow\n$demo_reversible_flow\n\nNote that `num1` can act as input to `num2`, and also vice-versa! As your apps get more complex, you will have many data flows connecting various Components.\n\nHere's an example of a \"multi-step\" demo, where the output of one model (a speech-to-text model) gets fed into the next model (a sentiment classifier).\n\n$code_blocks_speech_text_sentiment\n$demo_blocks_speech_text_sentiment\n\n", "heading1": "Multiple Data Flows", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The event listeners you've seen so far have a single input component. If you'd like to have multiple input components pass data to the function, you have two options on how the function can accept input component values:\n\n1. as a list of arguments, or\n2. as a single dictionary of values, keyed by the component\n\nLet's see an example of each:\n$code_calculator_list_and_dict\n\nBoth `add()` and `sub()` take `a` and `b` as inputs. However, the syntax is different between these listeners.\n\n1. To the `add_btn` listener, we pass the inputs as a list. The function `add()` takes each of these inputs as arguments. The value of `a` maps to the argument `num1`, and the value of `b` maps to the argument `num2`.\n2. To the `sub_btn` listener, we pass the inputs as a set (note the curly brackets!). When you pass a set, the function `sub()` receives a single dictionary argument `data`, where the keys are the input components and the values are the values of those components.\n\nIt is a matter of preference which syntax you prefer! For functions with many input components, option 2 may be easier to manage.\n\n$demo_calculator_list_and_dict\n\n", "heading1": "Function Input List vs Set", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Use `inputs_kwargs` when a component value should be passed to an event function by parameter name instead of by position. This is especially useful when calling an existing function with keyword-only parameters: the function can be connected directly without writing a wrapper just to rearrange its arguments.\n\n$code_keyword_inputs_simple\n$demo_keyword_inputs_simple\n\nIn this example, `first_name` is passed positionally through `inputs`, while the `last_name` component is mapped to the keyword-only `last_name` parameter. Each key in `inputs_kwargs` must match a parameter accepted by the event function. Keyword inputs can be combined with a component or list of components in `inputs`, but not with the set syntax described above, since a set already passes all input values together as one component-keyed dictionary.\n\nIf the event uses a `validator`, the validator receives values with the same positional and keyword mapping as the main function. Its signature must therefore accept every name used in `inputs_kwargs`; if it does not, the event raises a `ValueError` when it is defined.\n\nOne thing to keep in mind: a parameter annotated with a component type (such as `tb: gr.Textbox`) receives the full component instead of just its value, but only when Gradio can fill it positionally. A keyword-only parameter with a component annotation receives the plain value, so annotate such parameters with the value type they actually receive.\n\nNamed inputs are particularly helpful for larger forms because the mapping remains clear even when controls are arranged differently in the interface. The following live inventory dashboard connects five filter controls to explicit keyword-only parameters. Because `gr.on()` has no explicit triggers, it listens to changes from both `inputs` and `inputs_kwargs`, as well as the app's load event.\n\n$code_keyword_inputs_inventory\n$demo_keyword_inputs_inventory\n\n", "heading1": "Passing Inputs by Parameter Name", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Similarly, you may return values for multiple output components either as:\n\n1. a list of values, or\n2. a dictionary keyed by the component\n\nLet's first see an example of (1), where we set the values of two output components by returning two values:\n\n```python\nwith gr.Blocks() as demo:\n food_box = gr.Number(value=10, label=\"Food Count\")\n status_box = gr.Textbox()\n\n def eat(food):\n if food > 0:\n return food - 1, \"full\"\n else:\n return 0, \"hungry\"\n\n gr.Button(\"Eat\").click(\n fn=eat,\n inputs=food_box,\n outputs=[food_box, status_box]\n )\n```\n\nAbove, each return statement returns two values corresponding to `food_box` and `status_box`, respectively.\n\n**Note:** if your event listener has a single output component, you should **not** return it as a single-item list. This will not work, since Gradio does not know whether to interpret that outer list as part of your return value. You should instead just return that value directly.\n\nNow, let's see option (2). Instead of returning a list of values corresponding to each output component in order, you can also return a dictionary, with the key corresponding to the output component and the value as the new value. This also allows you to skip updating some output components.\n\n```python\nwith gr.Blocks() as demo:\n food_box = gr.Number(value=10, label=\"Food Count\")\n status_box = gr.Textbox()\n\n def eat(food):\n if food > 0:\n return {food_box: food - 1, status_box: \"full\"}\n else:\n return {status_box: \"hungry\"}\n\n gr.Button(\"Eat\").click(\n fn=eat,\n inputs=food_box,\n outputs=[food_box, status_box]\n )\n```\n\nNotice how when there is no food, we only update the `status_box` element. We skipped updating the `food_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns,", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "d_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns, we still need to specify the possible outputs in the event listener.\n\n", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The return value of an event listener function is usually the updated value of the corresponding output Component. Sometimes we want to update the configuration of the Component as well, such as the visibility. In this case, we return a new Component, setting the properties we want to change.\n\n$code_blocks_essay_simple\n$demo_blocks_essay_simple\n\nSee how we can configure the Textbox itself through a new `gr.Textbox()` method. The `value=` argument can still be used to update the value along with Component configuration. Any arguments we do not set will preserve their previous values.\n\n", "heading1": "Updating Component Configurations", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In some cases, you may want to leave a component's value unchanged. Gradio includes a special function, `gr.skip()`, which can be returned from your function. Returning this function will keep the output component (or components') values as is. Let us illustrate with an example:\n\n$code_skip\n$demo_skip\n\nNote the difference between returning `None` (which generally resets a component's value to an empty state) versus returning `gr.skip()`, which leaves the component value unchanged.\n\nTip: if you have multiple output components, and you want to leave all of their values unchanged, you can just return a single `gr.skip()` instead of returning a tuple of skips, one for each element.\n\n", "heading1": "Not Changing a Component's Value", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "You can also run events consecutively by using the `then` method of an event listener. This will run an event after the previous event has finished running. This is useful for running events that update components in multiple steps.\n\nFor example, in the chatbot example below, we first update the chatbot with the user message immediately, and then update the chatbot with the computer response after a simulated delay.\n\n$code_chatbot_consecutive\n$demo_chatbot_consecutive\n\nThe `.then()` method of an event listener executes the subsequent event regardless of whether the previous event raised any errors. If you'd like to only run subsequent events if the previous event executed successfully, use the `.success()` method, which takes the same arguments as `.then()`. Conversely, if you'd like to only run subsequent events if the previous event failed (i.e., raised an error), use the `.failure()` method. This is particularly useful for error handling workflows, such as displaying error messages or restoring previous states when an operation fails.\n\n", "heading1": "Running Events Consecutively", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Often times, you may want to bind multiple triggers to the same function. For example, you may want to allow a user to click a submit button, or press enter to submit a form. You can do this using the `gr.on` method and passing a list of triggers to the `trigger`.\n\n$code_on_listener_basic\n$demo_on_listener_basic\n\nYou can use decorator syntax as well:\n\n$code_on_listener_decorator\n\nYou can use `gr.on` to create \"live\" events by binding to the `change` event of components that implement it. If you do not specify any triggers, the function will automatically bind to all `change` event of all input components that include a `change` event (for example `gr.Textbox` has a `change` event whereas `gr.Button` does not).\n\n$code_on_listener_live\n$demo_on_listener_live\n\nYou can follow `gr.on` with `.then`, just like any regular event listener. This handy method should save you from having to write a lot of repetitive code!\n\n", "heading1": "Binding Multiple Triggers to a Function", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "If you want to set a Component's value to always be a function of the value of other Components, you can use the following shorthand:\n\n```python\nwith gr.Blocks() as demo:\n num1 = gr.Number()\n num2 = gr.Number()\n product = gr.Number(lambda a, b: a * b, inputs=[num1, num2])\n```\n\nThis functionally the same as:\n```python\nwith gr.Blocks() as demo:\n num1 = gr.Number()\n num2 = gr.Number()\n product = gr.Number()\n\n gr.on(\n [num1.change, num2.change, demo.load], \n lambda a, b: a * b, \n inputs=[num1, num2], \n outputs=product\n )\n```\n", "heading1": "Binding a Component Value Directly to a Function of Other Components", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In the example below, we will create a variable number of Textboxes. When the user edits the input Textbox, we create a Textbox for each letter in the input. Try it out below:\n\n$code_render_split_simple\n$demo_render_split_simple\n\nSee how we can now create a variable number of Textboxes using our custom logic - in this case, a simple `for` loop. The `@gr.render` decorator enables this with the following steps:\n\n1. Create a function and attach the @gr.render decorator to it.\n2. Add the input components to the `inputs=` argument of @gr.render, and create a corresponding argument in your function for each component. This function will automatically re-run on any change to a component.\n3. Add all components inside the function that you want to render based on the inputs.\n\nNow whenever the inputs change, the function re-runs, and replaces the components created from the previous function run with the latest run. Pretty straightforward! Let's add a little more complexity to this app:\n\n$code_render_split\n$demo_render_split\n\nBy default, `@gr.render` re-runs are triggered by the `.load` listener to the app and the `.change` listener to any input component provided. We can override this by explicitly setting the triggers in the decorator, as we have in this app to only trigger on `input_text.submit` instead. \nIf you are setting custom triggers, and you also want an automatic render at the start of the app, make sure to add `demo.load` to your list of triggers.\n\n", "heading1": "Dynamic Number of Components", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "If you're creating components, you probably want to attach event listeners to them as well. Let's take a look at an example that takes in a variable number of Textbox as input, and merges all the text into a single box.\n\n$code_render_merge_simple\n$demo_render_merge_simple\n\nLet's take a look at what's happening here:\n\n1. The state variable `text_count` is keeping track of the number of Textboxes to create. By clicking on the Add button, we increase `text_count` which triggers the render decorator.\n2. Note that in every single Textbox we create in the render function, we explicitly set a `key=` argument. This key allows us to preserve the value of this Component between re-renders. If you type in a value in a textbox, and then click the Add button, all the Textboxes re-render, but their values aren't cleared because the `key=` maintains the the value of a Component across a render.\n3. We've stored the Textboxes created in a list, and provide this list as input to the merge button event listener. Note that **all event listeners that use Components created inside a render function must also be defined inside that render function**. The event listener can still reference Components outside the render function, as we do here by referencing `merge_btn` and `output` which are both defined outside the render function.\n\nJust as with Components, whenever a function re-renders, the event listeners created from the previous render are cleared and the new event listeners from the latest run are attached. \n\nThis allows us to create highly customizable and complex interactions! \n\n", "heading1": "Dynamic Event Listeners", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "The `key=` argument tells Gradio that a component being created in a render function corresponds to the same logical component as in the previous render.\n\nThis allows Gradio to reuse the existing browser element instead of destroying and recreating it on every render. It also preserves the user's entered value across re-renders when the same keyed component is recreated.\n\nIf your component is nested inside layout items like `gr.Row`, make sure those containers are keyed consistently as well, because parent keys must also match.\n\nYou can also key event listeners, for example `button.click(key=...)`, when the same listener is recreated with the same inputs and outputs across renders. This helps Gradio keep the listener associated with the correct component instances and can prevent issues when events finish processing after a re-render.\n\n", "heading1": "Closer Look at `keys=` parameter", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "Let's look at two examples that use all the features above. First, try out the to-do list app below: \n\n$code_todo_list\n$demo_todo_list\n\nNote that almost the entire app is inside a single `gr.render` that reacts to the tasks `gr.State` variable. This variable is a nested list, which presents some complexity. If you design a `gr.render` to react to a list or dict structure, ensure you do the following:\n\n1. Any event listener that modifies a state variable in a manner that should trigger a re-render must set the state variable as an output. This lets Gradio know to check if the variable has changed behind the scenes. \n2. In a `gr.render`, if a variable in a loop is used inside an event listener function, that variable should be \"frozen\" via setting it to itself as a default argument in the function header. See how we have `task=task` in both `mark_done` and `delete`. This freezes the variable to its \"loop-time\" value.\n\nLet's take a look at one last example that uses everything we learned. Below is an audio mixer. Provide multiple audio tracks and mix them together.\n\n$code_audio_mixer\n$demo_audio_mixer\n\nTwo things to note in this app:\n1. Here we provide `key=` to all the components! We need to do this so that if we add another track after setting the values for an existing track, our input values to the existing track do not get reset on re-render.\n2. When there are lots of components of different types and arbitrary counts passed to an event listener, it is easier to use the set and dictionary notation for inputs rather than list notation. Above, we make one large set of all the input `gr.Audio` and `gr.Slider` components when we pass the inputs to the `merge` function. In the function body we query the component values as a dict.\n\nThe `gr.render` expands gradio capabilities extensively - see what you can make out of it! \n", "heading1": "Putting it Together", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "Elements within a `with gr.Row` clause will all be displayed horizontally. For example, to display two Buttons side by side:\n\n```python\nwith gr.Blocks() as demo:\n with gr.Row():\n btn1 = gr.Button(\"Button 1\")\n btn2 = gr.Button(\"Button 2\")\n```\n\nYou can set every element in a Row to have the same height. Configure this with the `equal_height` argument.\n\n```python\nwith gr.Blocks() as demo:\n with gr.Row(equal_height=True):\n textbox = gr.Textbox()\n btn2 = gr.Button(\"Button 2\")\n```\n\nThe widths of elements in a Row can be controlled via a combination of `scale` and `min_width` arguments that are present in every Component.\n\n- `scale` is an integer that defines how an element will take up space in a Row. If scale is set to `0`, the element will not expand to take up space. If scale is set to `1` or greater, the element will expand. Multiple elements in a row will expand proportional to their scale. Below, `btn2` will expand twice as much as `btn1`, while `btn0` will not expand at all:\n\n```python\nwith gr.Blocks() as demo:\n with gr.Row():\n btn0 = gr.Button(\"Button 0\", scale=0)\n btn1 = gr.Button(\"Button 1\", scale=1)\n btn2 = gr.Button(\"Button 2\", scale=2)\n```\n\n- `min_width` will set the minimum width the element will take. The Row will wrap if there isn't sufficient space to satisfy all `min_width` values.\n\nLearn more about Rows in the [docs](https://gradio.app/docs/row).\n\n", "heading1": "Rows", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Components within a Column will be placed vertically atop each other. Since the vertical layout is the default layout for Blocks apps anyway, to be useful, Columns are usually nested within Rows. For example:\n\n$code_rows_and_columns\n$demo_rows_and_columns\n\nSee how the first column has two Textboxes arranged vertically. The second column has an Image and Button arranged vertically. Notice how the relative widths of the two columns is set by the `scale` parameter. The column with twice the `scale` value takes up twice the width.\n\nLearn more about Columns in the [docs](https://gradio.app/docs/column).\n\nFill Browser Height / Width\n\nTo make an app take the full width of the browser by removing the side padding, use `gr.Blocks(fill_width=True)`. \n\nTo make top level Components expand to take the full height of the browser, use `fill_height` and apply scale to the expanding Components.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks(fill_height=True) as demo:\n gr.Chatbot(scale=1)\n gr.Textbox(scale=0)\n```\n\n", "heading1": "Columns and Nesting", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Some components support setting height and width. These parameters accept either a number (interpreted as pixels) or a string. Using a string allows the direct application of any CSS unit to the encapsulating Block element.\n\nBelow is an example illustrating the use of viewport width (vw):\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n im = gr.ImageEditor(width=\"50vw\")\n\ndemo.launch()\n```\n\n", "heading1": "Dimensions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "You can also create Tabs using the `with gr.Tab('tab_name'):` clause. Any component created inside of a `with gr.Tab('tab_name'):` context appears in that tab. Consecutive Tab clauses are grouped together so that a single tab can be selected at one time, and only the components within that Tab's context are shown.\n\nFor example:\n\n$code_blocks_flipper\n$demo_blocks_flipper\n\nAlso note the `gr.Accordion('label')` in this example. The Accordion is a layout that can be toggled open or closed. Like `Tabs`, it is a layout element that can selectively hide or show content. Any components that are defined inside of a `with gr.Accordion('label'):` will be hidden or shown when the accordion's toggle icon is clicked.\n\nLearn more about [Tabs](https://gradio.app/docs/tab) and [Accordions](https://gradio.app/docs/accordion) in the docs.\n\n", "heading1": "Tabs and Accordions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "The sidebar is a collapsible panel that renders child components on the left side of the screen and can be expanded or collapsed.\n\nFor example:\n\n$code_blocks_sidebar\n\nLearn more about [Sidebar](https://gradio.app/docs/gradio/sidebar) in the docs.\n\n\n", "heading1": "Sidebar", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "In order to provide a guided set of ordered steps, a controlled workflow, you can use the `Walkthrough` component with accompanying `Step` components.\n\nThe `Walkthrough` component has a visual style and user experience tailored for this usecase.\n\nAuthoring this component is very similar to `Tab`, except it is the app developers responsibility to progress through each step, by setting the appropriate ID for the parent `Walkthrough` which should correspond to an ID provided to an indvidual `Step`. \n\n$demo_walkthrough\n\nLearn more about [Walkthrough](https://gradio.app/docs/gradio/walkthrough) in the docs.\n\n\n", "heading1": "Multi-step walkthroughs", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Both Components and Layout elements have a `visible` argument that can set initially and also updated. Setting `gr.Column(visible=...)` on a Column can be used to show or hide a set of Components.\n\n$code_blocks_form\n$demo_blocks_form\n\n", "heading1": "Visibility", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "In some cases, you might want to define components before you actually render them in your UI. For instance, you might want to show an examples section using `gr.Examples` above the corresponding `gr.Textbox` input. Since `gr.Examples` requires as a parameter the input component object, you will need to first define the input component, but then render it later, after you have defined the `gr.Examples` object.\n\nThe solution to this is to define the `gr.Textbox` outside of the `gr.Blocks()` scope and use the component's `.render()` method wherever you'd like it placed in the UI.\n\nHere's a full code example:\n\n```python\ninput_textbox = gr.Textbox()\n\nwith gr.Blocks() as demo:\n gr.Examples([\"hello\", \"bonjour\", \"merhaba\"], input_textbox)\n input_textbox.render()\n```\n\nSimilarly, if you have already defined a component in a Gradio app, but wish to unrender it so that you can define in a different part of your application, then you can call the `.unrender()` method. In the following example, the `Textbox` will appear in the third column:\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n with gr.Row():\n with gr.Column():\n gr.Markdown(\"Row 1\")\n textbox = gr.Textbox()\n with gr.Column():\n gr.Markdown(\"Row 2\")\n textbox.unrender()\n with gr.Column():\n gr.Markdown(\"Row 3\")\n textbox.render()\n\ndemo.launch()\n```\n\n", "heading1": "Defining and Rendering Components Separately", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Gradio themes are the easiest way to customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `launch()` method of the `Blocks` constructor. For example:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Glass())\n ...\n```\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. You can extend these themes or create your own themes from scratch - see the [Theming guide](/guides/theming-guide) for more details.\n\nFor additional styling ability, you can pass any CSS to your app as a string using the `css=` kwarg in the `launch()` method. You can also pass a pathlib.Path to a css file or a list of such paths to the `css_paths=` kwarg in the `launch()` method.\n\n**Warning**: The use of query selectors in custom JS and CSS is _not_ guaranteed to work across Gradio versions that bind to Gradio's own HTML elements as the Gradio HTML DOM may change. We recommend using query selectors sparingly.\n\nThe base class for the Gradio app is `gradio-container`, so here's an example that changes the background color of the Gradio app:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(css=\".gradio-container {background-color: red}\")\n ...\n```\n\nIf you'd like to reference external files in your css, preface the file path (which can be a relative or absolute path) with `\"/gradio_api/file=\"`, for example:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(css=\".gradio-container {background: url('/gradio_api/file=clouds.jpg')}\")\n ...\n```\n\nNote: By default, most files in the host machine are not accessible to users running the Gradio app. As a result, you should make sure that any referenced files (such as `clouds.jpg` here) are either URLs or [allowed paths, as described here](/main/guides/file-access).\n\n\n", "heading1": "Adding custom CSS to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "You can `elem_id` to add an HTML element `id` to any component, and `elem_classes` to add a class or list of classes. This will allow you to select elements more easily with CSS. This approach is also more likely to be stable across Gradio versions as built-in class names or ids may change (however, as mentioned in the warning above, we cannot guarantee complete compatibility between Gradio versions if you use custom CSS as the DOM elements may themselves change).\n\n```python\ncss = \"\"\"\nwarning {background-color: FFCCCB}\n.feedback textarea {font-size: 24px !important}\n\"\"\"\n\nwith gr.Blocks() as demo:\n box1 = gr.Textbox(value=\"Good Job\", elem_classes=\"feedback\")\n box2 = gr.Textbox(value=\"Failure\", elem_id=\"warning\", elem_classes=\"feedback\")\ndemo.launch(css=css)\n```\n\nThe CSS `warning` ruleset will only target the second Textbox, while the `.feedback` ruleset will target both. Note that when targeting classes, you might need to put the `!important` selector to override the default Gradio styles.\n\n", "heading1": "The `elem_id` and `elem_classes` Arguments", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "There are 3 ways to add javascript code to your Gradio demo:\n\n1. You can add JavaScript code as a string to the `js` parameter of the `Blocks` or `Interface` initializer. This will run the JavaScript code when the demo is first loaded.\n\nBelow is an example of adding custom js to show an animated welcome message when the demo first loads.\n\n$code_blocks_js_load\n$demo_blocks_js_load\n\n\n2. When using `Blocks` and event listeners, events have a `js` argument that can take a JavaScript function as a string and treat it just like a Python event listener function. You can pass both a JavaScript function and a Python function (in which case the JavaScript function is run first) or only Javascript (and set the Python `fn` to `None`). Take a look at the code below:\n \n$code_blocks_js_methods\n$demo_blocks_js_methods\n\n3. Lastly, you can add JavaScript code to the `head` param of the `Blocks` initializer. This will add the code to the head of the HTML document. For example, you can add Google Analytics to your demo like so:\n\n\n```python\nhead = f\"\"\"\n\n\n\"\"\"\n\nwith gr.Blocks() as demo:\n gr.HTML(\"

My App

\")\n\ndemo.launch(head=head)\n```\n\nThe `head` parameter accepts any HTML tags you would normally insert into the `` of a page. For example, you can also include `` tags to `head` in order to update the social sharing preview for your Gradio app like this:\n\n```py\nimport gradio as gr\n\ncustom_head = \"\"\"\n\nSample App\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\"\"\"\n\nwith gr.Blocks(title=\"My App\") as demo:\n gr.HTML(\"

My App

\")\n\ndemo.launch(head=custom_head)\n```\n\n\n\nNote that injecting custom JS can affect browser behavior and accessibility (e.g. keyboard shortcuts may be lead to unexpected behavior if your Gradio app is embedded in another webpage). You should test your interface across different browsers and be mindful of how scripts may interact with browser defaults. Here's an example where pressing `Shift + s` triggers the `click` event of a specific `Button` component if the browser focus is _not_ on an input component (e.g. `Textbox` component):\n\n```python\nimport gradio as gr\n\nshortcut_js = \"\"\"\n\n\"\"\"\n\nwith gr.Blocks() as demo:\n action_button = gr.Button(value=\"Name\", elem_id=\"my_btn\")\n textbox = gr.Textbox()\n action_button.click(lambda : \"button pressed\", None, textbox)\n \ndemo.launch(head=shortcut_js)\n```\n\n", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "The `gr.HTML` component can also be used to create custom input components by triggering events. You will provide `js_on_load`, javascript code that runs when the component loads. The code has access to the `trigger` function to trigger events that Gradio can listen to, and the object `props` which has access to all the props of the component, including `value`.\n\n$code_star_rating_events\n$demo_star_rating_events\n\nTake a look at the `js_on_load` code above. We add click event listeners to each star image to update the value via `props.value` when a star is clicked. This also re-renders the template to show the updated value. We also add a click event listener to the submit button that triggers the `submit` event. In our app, we listen to this trigger to run a function that outputs the `value` of the star rating.\n\nThe `js_on_load` scope also includes an `upload` async function that lets you upload a JavaScript `File` object directly to the Gradio server. It returns a dictionary with `path` (the server-side file path) and `url` (the public URL to access the file).\n\n```js\nconst { path, url } = await upload(file);\n```\n\nHere is an example of a custom file-upload widget built with `gr.HTML`:\n\n$code_html_upload\n$demo_html_upload\n\nYou can update any other props of the component via `props.`, and trigger events via `trigger('')`. The trigger event can also be send event data, e.g.\n\n```js\ntrigger('event_name', { key: value, count: 123 });\n```\n\nThis event data will be accessible the Python event listener functions via gr.EventData.\n\n```python\ndef handle_event(evt: gr.EventData):\n print(evt.key)\n print(evt.count)\n\nstar_rating.event(fn=handle_event, inputs=[], outputs=[])\n```\n\nKeep in mind that event listeners attached in `js_on_load` are only attached once when the component is first rendered. If your component creates new elements dynamically that need event listeners, attach the event listener to a parent element that exists when the component load", "heading1": "Triggering Events and Custom Input Components", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "ce when the component is first rendered. If your component creates new elements dynamically that need event listeners, attach the event listener to a parent element that exists when the component loads, and check for the target. For example:\n\n```js\nelement.addEventListener('click', (e) =>\n if (e.target && e.target.matches('.child-element')) {\n props.value = e.target.dataset.value;\n }\n);\n```\n\nYou can trigger an event with any name. As long as the event name appears enclosed in quotes in your `js_on_load` string, you can attach a Python listener using `component.do_something(fn, ...)`.\n\n", "heading1": "Triggering Events and Custom Input Components", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "The `watch` function, available inside `js_on_load`, lets you run a callback whenever specific props change when the component is an output to a Python event listener. Read current values directly from `props` inside the callback.\n\n```js\n// Watch a single prop\nwatch('value', () => {\n console.log('value is now:', props.value);\n});\n\n// Watch multiple props\nwatch(['value', 'color'], () => {\n console.log('value or color changed');\n});\n```\n\n", "heading1": "Watching Props with `watch`", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "The `head` parameter lets you load external JavaScript or CSS libraries directly on the component. The `head` content is injected and loaded **before** `js_on_load` runs, so your code can immediately use the library.\n\n```python\ngr.HTML(\n value=[30, 70, 45, 90, 60],\n html_template=\"\",\n js_on_load=\"\"\"\n new Chart(element.querySelector('chart'), {\n type: 'bar',\n data: {\n labels: props.value.map((_, i) => 'Item ' + (i + 1)),\n datasets: [{ label: 'Values', data: props.value }]\n }\n });\n \"\"\",\n head='',\n)\n```\n\n", "heading1": "Loading Third-Party Scripts with `head`", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "You can call Python functions directly from your `js_on_load` code using the `server_functions` parameter. Pass a list of Python functions to `server_functions`, and they become available as async methods on a `server` object inside `js_on_load`.\n\n$code_html_server_functions\n$demo_html_server_functions\n\n\n", "heading1": "Server Functions", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "If you are reusing the same HTML component in multiple places, you can create a custom component class by subclassing `gr.HTML` and setting default values for the templates and other arguments. Here's an example of creating a reusable StarRating component.\n\n$code_star_rating_component\n$demo_star_rating_component\n\nNote: Gradio requires all components to accept certain arguments, such as `render`. You do not need\nto handle these arguments, but you do need to accept them in your component constructor and pass\nthem to the parent `gr.HTML` class. Otherwise, your component may not behave correctly. The easiest\nway is to add `**kwargs` to your `__init__` method and pass it to `super().__init__()`, just like in the code example above.\n\nWe've created several custom HTML components as reusable components as examples you can reference in [this directory](https://github.com/gradio-app/gradio/tree/main/gradio/components/custom_html_components).\n\n\n", "heading1": "Component Classes", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "The `gr.HTML` component can also be used as a container for other Gradio components using the `@children` placeholder. This allows you to create custom layouts with HTML/CSS. \n\nThe `@children` must be at the top-level of the `html_template`. Since children cannot be nested inside the template, target the parent element directly with your CSS and JavaScript if you need to style or interact with the container of the children.\n\nHere's a basic example:\n\n$code_html_children\n$demo_html_children\n\nIn this example, the `@children` placeholder marks where the child components (the Name and Email textboxes) will be rendered. Notice how in the `css_template` we target the parent element to style the container div that wraps the children.\n\n\nAPI / MCP support\n\nTo make your custom HTML component work with Gradio's built-in support for API and MCP (Model Context Protocol) usage, you need to define how its data should be serialized. There are two ways to do this:\n\n**Option 1: Define an `api_info()` method**\n\nAdd an `api_info()` method that returns a JSON schema dictionary describing your component's data format. This is what we do in the StarRating class above.\n\n**Option 2: Define a Pydantic data model**\n\nFor more complex data structures, you can define a Pydantic model that inherits from `GradioModel` or `GradioRootModel`:\n\n```python\nfrom gradio.data_classes import GradioModel, GradioRootModel\n\nclass MyComponentData(GradioModel):\n items: List[str]\n count: int\n\nclass MyComponent(gr.HTML):\n data_model = MyComponentData\n```\n\nUse `GradioModel` when your data is a dictionary with named fields, or `GradioRootModel` when your data is a simple type (string, list, etc.) that doesn't need to be wrapped in a dictionary. By defining a `data_model`, your component automatically implements API methods.\n\n", "heading1": "Embedding Components in HTML", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "Once you've built a custom HTML component, you can share it with the community by pushing it to the [HTML Components Gallery](https://www.gradio.app/custom-components/html-gallery). The gallery lets anyone browse, interact with, and copy the Python code for community-contributed components.\n\nCall `push_to_hub` on any `gr.HTML` instance or subclass:\n\n```python\nstar_rating = StarRating()\nstar_rating.push_to_hub(\n name=\"Star Rating\",\n description=\"Interactive 5-star rating with click-to-rate\",\n author=\"your-hf-username\",\n tags=[\"input\", \"rating\"],\n repo_url=\"https://github.com/your-username/your-repo\",\n)\n```\n\nThis opens a pull request on the gallery's HuggingFace dataset repo. Once approved, your component will appear in the gallery for others to discover and use.\n\nTip: The `push_to_hub` method has a `head` parameter that deserves special attention. If your component uses an external library loaded via the `head` parameter of `launch` (e.g. `head=''`), pass the same `head` string to `push_to_hub` so that the gallery can load those scripts when rendering your component.\n\nAuthentication\n\nYou need a HuggingFace **write token** to push components. Either pass it directly:\n\n```python\nstar_rating.push_to_hub(..., token=\"hf_xxxxx\")\n```\n\nOr log in beforehand with the HuggingFace CLI, and the cached token will be used automatically:\n\n```bash\nhuggingface-cli login\n```\n\n", "heading1": "Sharing Components with `push_to_hub`", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "Keep in mind that using `gr.HTML` to create custom components involves injecting raw HTML and JavaScript into your Gradio app. Be cautious about using untrusted user input into `html_template` and `js_on_load`, as this could lead to cross-site scripting (XSS) vulnerabilities. \n\nYou should also expect that any Python event listeners that take your `gr.HTML` component as input could have any arbitrary value passed to them, not just the values you expect the frontend to be able to set for `value`. Sanitize and validate user input appropriately in public applications.\n\n", "heading1": "Security Considerations", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "- Browse the [HTML Components Gallery](https://www.gradio.app/custom-components/html-gallery) to see what the community has built and copy components into your own apps.\n- Check out more examples in [this directory](https://github.com/gradio-app/gradio/tree/main/gradio/components/custom_html_components).\n- Share your own components with `push_to_hub` to help others!", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/custom-HTML-components", "source_page_title": "Building With Blocks - Custom Html Components Guide"}, {"text": "As of version 5.36.0, Gradio now comes with a built-in MCP server that can upload files to a running Gradio application. In the `View API` page of the server, you should see the following code snippet if any of the tools require file inputs:\n\n\n\nThe command to start the MCP server takes two arguments:\n\n- The URL (or Hugging Face space id) of the gradio application to upload the files to. In this case, `http://127.0.0.1:7860`.\n- The local directory on your computer with which the server is allowed to upload files from (``). For security, please make this directory as narrow as possible to prevent unintended file uploads.\n\nAs stated in the image, you need to install [uv](https://docs.astral.sh/uv/getting-started/installation/) (a python package manager that can run python scripts) before connecting from your MCP client. \n\nIf you have gradio installed locally and you don't want to install uv, you can replace the `uvx` command with the path to gradio binary. It should look like this:\n\n```json\n\"upload-files\": {\n \"command\": \"\",\n \"args\": [\n \"upload-mcp\",\n \"http://localhost:7860/\",\n \"/Users/freddyboulton/Pictures\"\n ]\n}\n```\n\nAfter connecting to the upload server, your LLM agent will know when to upload files for you automatically!\n\n\n\n", "heading1": "Using the File Upload MCP Server", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "In this guide, we've covered how you can connect to the Upload File MCP Server so that your agent can upload files before using Gradio MCP servers. Remember to set the `` as small as possible to prevent unintended file uploads!\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "If you're using LLMs in your workflow, adding this server will augment them with just the right context on gradio - which makes your experience a lot faster and smoother. \n\n\n\nThe server is running on Spaces and was launched entirely using Gradio, you can see all the code [here](https://huggingface.co/spaces/gradio/docs-mcp). For more on building an mcp server with gradio, see the [previous guide](./building-an-mcp-client-with-gradio). \n\n", "heading1": "Why an MCP Server?", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "For clients that support streamable HTTP (e.g. Cursor, Windsurf, Cline), simply add the following configuration to your MCP config:\n\n```json\n{\n \"mcpServers\": {\n \"gradio\": {\n \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/\"\n }\n }\n}\n```\n\nWe've included step-by-step instructions for Cursor below, but you can consult the docs for Windsurf [here](https://docs.windsurf.com/windsurf/mcp), and Cline [here](https://docs.cline.bot/mcp-servers/configuring-mcp-servers) which are similar to set up. \n\n\n\nCursor \n\n1. Make sure you're using the latest version of Cursor, and go to Cursor > Settings > Cursor Settings > MCP \n2. Click on '+ Add new global MCP server' \n3. Copy paste this json into the file that opens and then save it. \n```json\n{\n \"mcpServers\": {\n \"gradio\": {\n \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/\"\n }\n }\n}\n```\n4. That's it! You should see the tools load and the status go green in the settings page. You may have to click the refresh icon or wait a few seconds. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/cursor-mcp.png)\n\nClaude Desktop\n\n1. Since Claude Desktop only supports stdio, you will need to [install Node.js](https://nodejs.org/en/download/) to get this to work. \n2. Make sure you're using the latest version of Claude Desktop, and go to Claude > Settings > Developer > Edit Config \n3. Open the file with your favorite editor and copy paste this json, then save the file. \n```json\n{\n \"mcpServers\": {\n \"gradio\": {\n \"command\": \"npx\",\n \"args\": [\n \"mcp-remote\",\n \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/\"\n ]\n }\n }\n}\n```\n4. Quit and re-open Claude Desktop, and you should be good to go. You should see it loaded in the Search and Tools icon or on the developer settings page. \n \n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/claude-desktop-mcp.gif)\n\n", "heading1": "Installing in the Clients", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "There are currently only two tools in the server: `gradio_docs_mcp_load_gradio_docs` and `gradio_docs_mcp_search_gradio_docs`. \n\n1. `gradio_docs_mcp_load_gradio_docs`: This tool takes no arguments and will load an /llms.txt style summary of Gradio's latest, full documentation. Very useful context the LLM can parse before answering questions or generating code. \n\n2. `gradio_docs_mcp_search_gradio_docs`: This tool takes a query as an argument and will run embedding search on Gradio's docs, guides, and demos to return the most useful context for the LLM to parse.", "heading1": "Tools", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "An MCP (Model Control Protocol) server is a standardized way to expose tools so that they can be used by LLMs. A tool can provide an LLM functionality that it does not have natively, such as the ability to generate images or calculate the prime factors of a number. \n\n", "heading1": "What is an MCP Server?", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "LLMs are famously not great at counting the number of letters in a word (e.g. the number of \"r\"-s in \"strawberry\"). But what if we equip them with a tool to help? Let's start by writing a simple Gradio app that counts the number of letters in a word or phrase:\n\n$code_letter_counter\n\nNotice that we have: (1) included a detailed docstring for our function, and (2) set `mcp_server=True` in `.launch()`. This is all that's needed for your Gradio app to serve as an MCP server! Now, when you run this app, it will:\n\n1. Start the regular Gradio web interface\n2. Start the MCP server\n3. Print the MCP server URL in the console\n\nThe MCP server will be accessible at:\n```\nhttp://your-server:port/gradio_api/mcp/\n```\n\nGradio automatically converts the `letter_counter` function into an MCP tool that can be used by LLMs. The docstring of the function and the type hints of arguments will be used to generate the description of the tool and its parameters. The name of the function will be used as the name of your tool. Any initial values you provide to your input components (e.g. \"strawberry\" and \"r\" in the `gr.Textbox` components above) will be used as the default values if your LLM doesn't specify a value for that particular input parameter.\n\nNow, all you need to do is add this URL endpoint to your MCP Client (e.g. Claude Desktop, Cursor, or Cline), which typically means pasting this config in the settings:\n\n```\n{\n \"mcpServers\": {\n \"gradio\": {\n \"url\": \"http://your-server:port/gradio_api/mcp/\"\n }\n }\n}\n```\n\n(By the way, you can find the exact config to copy-paste by going to the \"View API\" link in the footer of your Gradio app, and then clicking on \"MCP\").\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\n", "heading1": "Example: Counting Letters in a Word", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "1. **Tool Conversion**: Each API endpoint in your Gradio app is automatically converted into an MCP tool with a corresponding name, description, and input schema. To view the tools and schemas, visit http://your-server:port/gradio_api/mcp/schema or go to the \"View API\" link in the footer of your Gradio app, and then click on \"MCP\".\n\n\n2. **Environment variable support**. There are two ways to enable the MCP server functionality:\n\n* Using the `mcp_server` parameter, as shown above:\n ```python\n demo.launch(mcp_server=True)\n ```\n\n* Using environment variables:\n ```bash\n export GRADIO_MCP_SERVER=True\n ```\n\n3. **File Handling**: The Gradio MCP server automatically handles file data conversions, including:\n - Processing image files and returning them in the correct format\n - Managing temporary file storage\n\n By default, the Gradio MCP server accepts input images and files as full URLs (\"http://...\" or \"https:/...\"). For convenience, an additional STDIO-based MCP server is also generated, which can be used to upload files to any remote Gradio app and which returns a URL that can be used for subsequent tool calls.\n\n4. **Hosted MCP Servers on \udb40\udc20\ud83e\udd17 Spaces**: You can publish your Gradio application for free on Hugging Face Spaces, which will allow you to have a free hosted MCP server. Here's an example of such a Space: https://huggingface.co/spaces/abidlabs/mcp-tools. Notice that you can add this config to your MCP Client to start using the tools from this Space immediately:\n\n```\n{\n \"mcpServers\": {\n \"gradio\": {\n \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/\"\n }\n }\n}\n```\n\n\n\nTip: To minimize latency and increase throughput by as much as 10 times, set queue=False in the event handlers of your Gradio app. However, this disables progress notifications so its recommended that l", "heading1": "Key features of the Gradio <> MCP Integration", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "p: To minimize latency and increase throughput by as much as 10 times, set queue=False in the event handlers of your Gradio app. However, this disables progress notifications so its recommended that long running events set queue=True\n\n", "heading1": "Key features of the Gradio <> MCP Integration", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If there's an existing Space that you'd like to use an MCP server, you'll need to do three things:\n\n1. First, [duplicate the Space](https://huggingface.co/docs/hub/en/spaces-more-ways-to-createduplicating-a-space) if it is not your own Space. This will allow you to make changes to the app. If the Space requires a GPU, set the hardware of the duplicated Space to be same as the original Space. You can make it either a public Space or a private Space, since it is possible to use either as an MCP server, as described below.\n2. Then, add docstrings to the functions that you'd like the LLM to be able to call as a tool. The docstring should be in the same format as the example code above.\n3. Finally, add `mcp_server=True` in `.launch()`.\n\nThat's it!\n\n", "heading1": "Converting an Existing Space", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "You can use either a public Space or a private Space as an MCP server. If you'd like to use a private Space as an MCP server (or a ZeroGPU Space with your own quota), then you will need to provide your [Hugging Face token](https://huggingface.co/settings/token) when you make your request. To do this, simply add it as a header in your config like this:\n\n```\n{\n \"mcpServers\": {\n \"gradio\": {\n \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/\",\n \"headers\": {\n \"Authorization\": \"Bearer \"\n }\n }\n }\n}\n```\n\n", "heading1": "Private Spaces", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If your app is launched with `auth=` (or with an `auth_dependency`), the MCP server requires the same credentials as the rest of the app, and requests without them get a `401` response. MCP clients need to send the login cookie with each request, or whatever headers your `auth_dependency` reads (e.g. an `Authorization` header). Tools, resources, and prompts then run as the logged-in user, so `request.username` is set in your functions.\n\nYou may wish to authenticate users more precisely or let them provide other kinds of credentials or tokens in order to provide a custom experience for different users. \n\nGradio allows you to access the underlying `starlette.Request` that has made the tool call, which means that you can access headers, originating IP address, or any other information that is part of the network request. To do this, simply add a parameter in your function of the type `gr.Request`, and Gradio will automatically inject the request object as the parameter.\n\nHere's an example:\n\n```py\nimport gradio as gr\n\ndef echo_headers(x, request: gr.Request):\n return str(dict(request.headers))\n\ngr.Interface(echo_headers, \"textbox\", \"textbox\").launch(mcp_server=True)\n```\n\nThis MCP server will simply ignore the user's input and echo back all of the headers from a user's request. One can build more complex apps using the same idea. See the [docs on `gr.Request`](https://www.gradio.app/main/docs/gradio/request) for more information (note that only the core Starlette attributes of the `gr.Request` object will be present, attributes such as Gradio's `.session_hash` will not be present).\n\nUsing the gr.Header class\n\nA common pattern in MCP server development is to use authentication headers to call services on behalf of your users. Instead of using a `gr.Request` object like in the example above, you can use a `gr.Header` argument. Gradio will automatically extract that header from the incoming request (if it exists) and pass it to your function.\n\nIn the example below, t", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": " in the example above, you can use a `gr.Header` argument. Gradio will automatically extract that header from the incoming request (if it exists) and pass it to your function.\n\nIn the example below, the `X-API-Token` header is extracted from the incoming request and passed in as the `x_api_token` argument to `make_api_request_on_behalf_of_user`.\n\nThe benefit of using `gr.Header` is that the MCP connection docs will automatically display the headers you need to supply when connecting to the server! See the image below:\n\n```python\nimport gradio as gr\n\ndef make_api_request_on_behalf_of_user(prompt: str, x_api_token: gr.Header):\n \"\"\"Make a request to everyone's favorite API.\n Args:\n prompt: The prompt to send to the API.\n Returns:\n The response from the API.\n Raises:\n AssertionError: If the API token is not valid.\n \"\"\"\n return \"Hello from the API\" if not x_api_token else \"Hello from the API with token!\"\n\n\ndemo = gr.Interface(\n make_api_request_on_behalf_of_user,\n [\n gr.Textbox(label=\"Prompt\"),\n ],\n gr.Textbox(label=\"Response\"),\n)\n\ndemo.launch(mcp_server=True)\n```\n\n![MCP Header Connection Page](https://github.com/user-attachments/assets/e264eedf-a91a-476b-880d-5be0d5934134)\n\nSending Progress Updates\n\nThe Gradio MCP server automatically sends progress updates to your MCP Client based on the queue in the Gradio application. If you'd like to send custom progress updates, you can do so using the same mechanism as you would use to display progress updates in the UI of your Gradio app: by using the `gr.Progress` class!\n\nHere's an example of how to do this:\n\n$code_mcp_progress\n\n[Here are the docs](https://www.gradio.app/docs/gradio/progress) for the `gr.Progress` class, which can also automatically track `tqdm` calls.\n\nNote: by default, progress notifications are enabled for all MCP tools, even if the corresponding Gradio functions do not include a `gr.Progress`. However, this can add some overhead to the MCP tool (t", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "ote: by default, progress notifications are enabled for all MCP tools, even if the corresponding Gradio functions do not include a `gr.Progress`. However, this can add some overhead to the MCP tool (typically ~500ms). To disable progress notification, you can set `queue=False` in your Gradio event handler to skip the overhead related to subscribing to the queue's progress updates.\n\n\n", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "Gradio automatically sets the tool name based on the name of your function, and the description from the docstring of your function. But you may want to change how the description appears to your LLM. You can do this by using the `api_description` parameter in `Interface`, `ChatInterface`, or any event listener. This parameter takes three different kinds of values:\n\n* `None` (default): the tool description is automatically created from the docstring of the function (or its parent's docstring if it does not have a docstring but inherits from a method that does.)\n* `False`: no tool description appears to the LLM.\n* `str`: an arbitrary string to use as the tool description.\n\nIn addition to modifying the tool descriptions, you can also toggle which tools appear to the LLM. You can do this by setting the `show_api` parameter, which is by default `True`. Setting it to `False` hides the endpoint from the API docs and from the MCP server. If you expose multiple tools, users of your app will also be able to toggle which tools they'd like to add to their MCP server by checking boxes in the \"view MCP or API\" panel.\n\nHere's an example that shows the `api_description` and `show_api` parameters in actions:\n\n$code_mcp_tools\n\n\n\n", "heading1": "Modifying Tool Descriptions", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "In addition to tools (which execute functions generally and are the default for any function exposed through the Gradio MCP integration), MCP supports two other important primitives: **resources** (for exposing data) and **prompts** (for defining reusable templates). Gradio provides decorators to easily create MCP servers with all three capabilities.\n\n\nCreating MCP Resources\n\nUse the `@gr.mcp.resource` decorator on any function to expose data through your Gradio app. Resources can be static (always available at a fixed URI) or templated (with parameters in the URI).\n\n$code_mcp_resources_and_prompts\n\nIn this example:\n- The `get_greeting` function is exposed as a resource with a URI template `greeting://{name}`\n- When an MCP client requests `greeting://Alice`, it receives \"Hello, Alice!\"\n- Resources can also return images and other types of files or binary data. In order to return non-text data, you should specify the `mime_type` parameter in `@gr.mcp.resource()` and return a Base64 string from your function.\n\nCreating MCP Prompts \n\nPrompts help standardize how users interact with your tools. They're especially useful for complex workflows that require specific formatting or multiple steps.\n\nThe `greet_user` function in the example above is decorated with `@gr.mcp.prompt()`, which:\n- Makes it available as a prompt template in MCP clients\n- Accepts parameters (`name` and `style`) to customize the output\n- Returns a structured prompt that guides the LLM's behavior\n\n\n", "heading1": "MCP Resources and Prompts", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "So far, all of our MCP tools, resources, or prompts have corresponded to event listeners in the UI. This works well for functions that directly update the UI, but may not work if you wish to expose a \"pure logic\" function that should return raw data (e.g. a JSON object) without directly causing a UI update.\n\nIn order to expose such an MCP tool, you can create a pure Gradio API endpoint using `gr.api` (see [full docs here](https://www.gradio.app/main/docs/gradio/api)). Here's an example of creating an MCP tool that slices a list:\n\n$code_mcp_tool_only\n\nNote that if you use this approach, your function signature must be fully typed, including the return value, as these signature are used to determine the typing information for the MCP tool.\n\n", "heading1": "Adding MCP-Only Functions", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "In some cases, you may decide not to use Gradio's built-in integration and instead manually create an FastMCP Server that calls a Gradio app. This approach is useful when you want to:\n\n- Store state / identify users between calls instead of treating every tool call completely independently\n- Start the Gradio app MCP server when a tool is called (if you are running multiple Gradio apps locally and want to save memory / GPU)\n\nThis is very doable thanks to the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) and the [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)'s `FastMCP` class. Here's an example of creating a custom MCP server that connects to various Gradio apps hosted on [HuggingFace Spaces](https://huggingface.co/spaces) using the `stdio` protocol:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom gradio_client import Client\nimport sys\nimport io\nimport json \n\nmcp = FastMCP(\"gradio-spaces\")\n\nclients = {}\n\ndef get_client(space_id: str) -> Client:\n \"\"\"Get or create a Gradio client for the specified space.\"\"\"\n if space_id not in clients:\n clients[space_id] = Client(space_id)\n return clients[space_id]\n\n\n@mcp.tool()\nasync def generate_image(prompt: str, space_id: str = \"ysharma/SanaSprint\") -> str:\n \"\"\"Generate an image using Flux.\n \n Args:\n prompt: Text prompt describing the image to generate\n space_id: HuggingFace Space ID to use \n \"\"\"\n client = get_client(space_id)\n result = client.predict(\n prompt=prompt,\n model_size=\"1.6B\",\n seed=0,\n randomize_seed=True,\n width=1024,\n height=1024,\n guidance_scale=4.5,\n num_inference_steps=2,\n api_name=\"/infer\"\n )\n return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n \"\"\"Text-to-Speech Synthesis.\n \n Args:\n prompt: Text prompt describing the co", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n \"\"\"Text-to-Speech Synthesis.\n \n Args:\n prompt: Text prompt describing the conversation between speakers S1, S2\n space_id: HuggingFace Space ID to use \n \"\"\"\n client = get_client(space_id)\n result = client.predict(\n text_input=f\"\"\"{prompt}\"\"\",\n audio_prompt_input=None, \n max_new_tokens=3072,\n cfg_scale=3,\n temperature=1.3,\n top_p=0.95,\n cfg_filter_top_k=30,\n speed_factor=0.94,\n api_name=\"/generate_audio\"\n )\n return result\n\n\nif __name__ == \"__main__\":\n import sys\n import io\n sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n \n mcp.run(transport='stdio')\n```\n\nThis server exposes two tools:\n1. `run_dia_tts` - Generates a conversation for the given transcript in the form of `[S1]first-sentence. [S2]second-sentence. [S1]...`\n2. `generate_image` - Generates images using a fast text-to-image model\n\nTo use this MCP Server with Claude Desktop (as MCP Client):\n\n1. Save the code to a file (e.g., `gradio_mcp_server.py`)\n2. Install the required dependencies: `pip install mcp gradio-client`\n3. Configure Claude Desktop to use your server by editing the configuration file at `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows):\n\n```json\n{\n \"mcpServers\": {\n \"gradio-spaces\": {\n \"command\": \"python\",\n \"args\": [\n \"/absolute/path/to/gradio_mcp_server.py\"\n ]\n }\n }\n}\n```\n\n4. Restart Claude Desktop\n\nNow, when you ask Claude about generating an image or transcribing audio, it can use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "The MCP protocol is still in its infancy and you might see issues connecting to an MCP Server that you've built. We generally recommend using the [MCP Inspector Tool](https://github.com/modelcontextprotocol/inspector) to try connecting and debugging your MCP Server.\n\nHere are some things that may help:\n\n**1. Ensure that you've provided type hints and valid docstrings for your functions**\n\nAs mentioned earlier, Gradio reads the docstrings for your functions and the type hints of input arguments to generate the description of the tool and parameters. A valid function and docstring looks like this (note the \"Args:\" block with indented parameter names underneath):\n\n```py\ndef image_orientation(image: Image.Image) -> str:\n \"\"\"\n Returns whether image is portrait or landscape.\n\n Args:\n image (Image.Image): The image to check.\n \"\"\"\n return \"Portrait\" if image.height > image.width else \"Landscape\"\n```\n\nNote: You can preview the schema that is created for your MCP server by visiting the `http://your-server:port/gradio_api/mcp/schema` URL.\n\n**2. Try accepting input arguments as `str`**\n\nSome MCP Clients do not recognize parameters that are numeric or other complex types, but all of the MCP Clients that we've tested accept `str` input parameters. When in doubt, change your input parameter to be a `str` and then cast to a specific type in the function, as in this example:\n\n```py\ndef prime_factors(n: str):\n \"\"\"\n Compute the prime factorization of a positive integer.\n\n Args:\n n (str): The integer to factorize. Must be greater than 1.\n \"\"\"\n n_int = int(n)\n if n_int <= 1:\n raise ValueError(\"Input must be an integer greater than 1.\")\n\n factors = []\n while n_int % 2 == 0:\n factors.append(2)\n n_int //= 2\n\n divisor = 3\n while divisor * divisor <= n_int:\n while n_int % divisor == 0:\n factors.append(divisor)\n n_int //= divisor\n divisor += 2\n\n if n_int > 1:\n factors.", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "= 3\n while divisor * divisor <= n_int:\n while n_int % divisor == 0:\n factors.append(divisor)\n n_int //= divisor\n divisor += 2\n\n if n_int > 1:\n factors.append(n_int)\n\n return factors\n```\n\n**3. Ensure that your MCP Client Supports Streamable HTTP**\n\nSome MCP Clients do not yet support streamable HTTP-based MCP Servers. In those cases, you can use a tool such as [mcp-remote](https://github.com/geelen/mcp-remote). First install [Node.js](https://nodejs.org/en/download/). Then, add the following to your own MCP Client config:\n\n```\n{\n \"mcpServers\": {\n \"gradio\": {\n \"command\": \"npx\",\n \"args\": [\n \"mcp-remote\",\n \"http://your-server:port/gradio_api/mcp/\"\n ]\n }\n }\n}\n```\n\n**4. Restart your MCP Client and MCP Server**\n\nSome MCP Clients require you to restart them every time you update the MCP configuration. Other times, if the connection between the MCP Client and servers breaks, you might need to restart the MCP server. If all else fails, try restarting both your MCP Client and MCP Servers!\n\n", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "The Model Context Protocol (MCP) standardizes how applications provide context to LLMs. It allows Claude to interact with external tools, like image generators, file systems, or APIs, etc.\n\n", "heading1": "What is MCP?", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "- Python 3.10+\n- An Anthropic API key\n- Basic understanding of Python programming\n\n", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "First, install the required packages:\n\n```bash\npip install gradio anthropic mcp\n```\n\nCreate a `.env` file in your project directory and add your Anthropic API key:\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "The server provides tools that Claude can use. In this example, we'll create a server that generates images through [a HuggingFace space](https://huggingface.co/spaces/ysharma/SanaSprint).\n\nCreate a file named `gradio_mcp_server.py`:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nimport json\nimport sys\nimport io\nimport time\nfrom gradio_client import Client\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')\nsys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')\n\nmcp = FastMCP(\"huggingface_spaces_image_display\")\n\n@mcp.tool()\nasync def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:\n \"\"\"Generate an image using SanaSprint model.\n \n Args:\n prompt: Text prompt describing the image to generate\n width: Image width (default: 512)\n height: Image height (default: 512)\n \"\"\"\n client = Client(\"https://ysharma-sanasprint.hf.space/\")\n \n try:\n result = client.predict(\n prompt,\n \"0.6B\",\n 0,\n True,\n width,\n height,\n 4.0,\n 2,\n api_name=\"/infer\"\n )\n \n if isinstance(result, list) and len(result) >= 1:\n image_data = result[0]\n if isinstance(image_data, dict) and \"url\" in image_data:\n return json.dumps({\n \"type\": \"image\",\n \"url\": image_data[\"url\"],\n \"message\": f\"Generated image for prompt: {prompt}\"\n })\n \n return json.dumps({\n \"type\": \"error\",\n \"message\": \"Failed to generate image\"\n })\n \n except Exception as e:\n return json.dumps({\n \"type\": \"error\",\n \"message\": f\"Error generating image: {str(e)}\"\n })\n\nif __name__ == \"__main__\":\n mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `gene", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": " \"message\": f\"Error generating image: {str(e)}\"\n })\n\nif __name__ == \"__main__\":\n mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `generate_image` tool\n2. The tool connects to the SanaSprint model hosted on HuggingFace Spaces\n3. It handles the asynchronous nature of image generation by polling for results\n4. When an image is ready, it returns the URL in a structured JSON format\n\n", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now let's create a Gradio chat interface as MCP Client that connects Claude to our MCP server.\n\nCreate a file named `app.py`:\n\n```python\nimport asyncio\nimport os\nimport json\nfrom typing import List, Dict, Any, Union\nfrom contextlib import AsyncExitStack\n\nimport gradio as gr\nfrom gradio.components.chatbot import ChatMessage\nfrom mcp import ClientSession, StdioServerParameters\nfrom mcp.client.stdio import stdio_client\nfrom anthropic import Anthropic\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nloop = asyncio.new_event_loop()\nasyncio.set_event_loop(loop)\n\nclass MCPClientWrapper:\n def __init__(self):\n self.session = None\n self.exit_stack = None\n self.anthropic = Anthropic()\n self.tools = []\n \n def connect(self, server_path: str) -> str:\n return loop.run_until_complete(self._connect(server_path))\n \n async def _connect(self, server_path: str) -> str:\n if self.exit_stack:\n await self.exit_stack.aclose()\n \n self.exit_stack = AsyncExitStack()\n \n is_python = server_path.endswith('.py')\n command = \"python\" if is_python else \"node\"\n \n server_params = StdioServerParameters(\n command=command,\n args=[server_path],\n env={\"PYTHONIOENCODING\": \"utf-8\", \"PYTHONUNBUFFERED\": \"1\"}\n )\n \n stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))\n self.stdio, self.write = stdio_transport\n \n self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))\n await self.session.initialize()\n \n response = await self.session.list_tools()\n self.tools = [{ \n \"name\": tool.name,\n \"description\": tool.description,\n \"input_schema\": tool.inputSchema\n } for tool in response.tools]\n \n tool_names = [tool[\"name\"] for tool in self.tools]\n return f\"Connected to MCP server.", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "iption,\n \"input_schema\": tool.inputSchema\n } for tool in response.tools]\n \n tool_names = [tool[\"name\"] for tool in self.tools]\n return f\"Connected to MCP server. Available tools: {', '.join(tool_names)}\"\n \n def process_message(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]) -> tuple:\n if not self.session:\n return history + [\n {\"role\": \"user\", \"content\": message}, \n {\"role\": \"assistant\", \"content\": \"Please connect to an MCP server first.\"}\n ], gr.Textbox(value=\"\")\n \n new_messages = loop.run_until_complete(self._process_query(message, history))\n return history + [{\"role\": \"user\", \"content\": message}] + new_messages, gr.Textbox(value=\"\")\n \n async def _process_query(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]):\n claude_messages = []\n for msg in history:\n if isinstance(msg, ChatMessage):\n role, content = msg.role, msg.content\n else:\n role, content = msg.get(\"role\"), msg.get(\"content\")\n \n if role in [\"user\", \"assistant\", \"system\"]:\n claude_messages.append({\"role\": role, \"content\": content})\n \n claude_messages.append({\"role\": \"user\", \"content\": message})\n \n response = self.anthropic.messages.create(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1000,\n messages=claude_messages,\n tools=self.tools\n )\n\n result_messages = []\n \n for content in response.content:\n if content.type == 'text':\n result_messages.append({\n \"role\": \"assistant\", \n \"content\": content.text\n })\n \n elif content.type == 'tool_use':\n tool_name = content.name\n tool_args = content.input\n ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "ntent\": content.text\n })\n \n elif content.type == 'tool_use':\n tool_name = content.name\n tool_args = content.input\n \n result_messages.append({\n \"role\": \"assistant\",\n \"content\": f\"I'll use the {tool_name} tool to help answer your question.\",\n \"metadata\": {\n \"title\": f\"Using tool: {tool_name}\",\n \"log\": f\"Parameters: {json.dumps(tool_args, ensure_ascii=True)}\",\n \"status\": \"pending\",\n \"id\": f\"tool_call_{tool_name}\"\n }\n })\n \n result_messages.append({\n \"role\": \"assistant\",\n \"content\": \"```json\\n\" + json.dumps(tool_args, indent=2, ensure_ascii=True) + \"\\n```\",\n \"metadata\": {\n \"parent_id\": f\"tool_call_{tool_name}\",\n \"id\": f\"params_{tool_name}\",\n \"title\": \"Tool Parameters\"\n }\n })\n \n result = await self.session.call_tool(tool_name, tool_args)\n \n if result_messages and \"metadata\" in result_messages[-2]:\n result_messages[-2][\"metadata\"][\"status\"] = \"done\"\n \n result_messages.append({\n \"role\": \"assistant\",\n \"content\": \"Here are the results from the tool:\",\n \"metadata\": {\n \"title\": f\"Tool Result for {tool_name}\",\n \"status\": \"done\",\n \"id\": f\"result_{tool_name}\"\n }\n })\n \n result_content = result.content\n if isinstance(result_content, list):\n result_content = \"\\n\".join(str(item) for item in re", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": " })\n \n result_content = result.content\n if isinstance(result_content, list):\n result_content = \"\\n\".join(str(item) for item in result_content)\n \n try:\n result_json = json.loads(result_content)\n if isinstance(result_json, dict) and \"type\" in result_json:\n if result_json[\"type\"] == \"image\" and \"url\" in result_json:\n result_messages.append({\n \"role\": \"assistant\",\n \"content\": {\"path\": result_json[\"url\"], \"alt_text\": result_json.get(\"message\", \"Generated image\")},\n \"metadata\": {\n \"parent_id\": f\"result_{tool_name}\",\n \"id\": f\"image_{tool_name}\",\n \"title\": \"Generated Image\"\n }\n })\n else:\n result_messages.append({\n \"role\": \"assistant\",\n \"content\": \"```\\n\" + result_content + \"\\n```\",\n \"metadata\": {\n \"parent_id\": f\"result_{tool_name}\",\n \"id\": f\"raw_result_{tool_name}\",\n \"title\": \"Raw Output\"\n }\n })\n except:\n result_messages.append({\n \"role\": \"assistant\",\n \"content\": \"```\\n\" + result_content + \"\\n```\",\n \"metadata\": {\n \"parent_id\": f\"result_{tool_name}\",\n \"id\": f\"raw_result_{tool_name}\",\n \"title\": \"Raw Output\"\n }\n })\n ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": " \"parent_id\": f\"result_{tool_name}\",\n \"id\": f\"raw_result_{tool_name}\",\n \"title\": \"Raw Output\"\n }\n })\n \n claude_messages.append({\"role\": \"user\", \"content\": f\"Tool result for {tool_name}: {result_content}\"})\n next_response = self.anthropic.messages.create(\n model=\"claude-3-5-sonnet-20241022\",\n max_tokens=1000,\n messages=claude_messages,\n )\n \n if next_response.content and next_response.content[0].type == 'text':\n result_messages.append({\n \"role\": \"assistant\",\n \"content\": next_response.content[0].text\n })\n\n return result_messages\n\nclient = MCPClientWrapper()\n\ndef gradio_interface():\n with gr.Blocks(title=\"MCP Weather Client\") as demo:\n gr.Markdown(\"MCP Weather Assistant\")\n gr.Markdown(\"Connect to your MCP weather server and chat with the assistant\")\n \n with gr.Row(equal_height=True):\n with gr.Column(scale=4):\n server_path = gr.Textbox(\n label=\"Server Script Path\",\n placeholder=\"Enter path to server script (e.g., weather.py)\",\n value=\"gradio_mcp_server.py\"\n )\n with gr.Column(scale=1):\n connect_btn = gr.Button(\"Connect\")\n \n status = gr.Textbox(label=\"Connection Status\", interactive=False)\n \n chatbot = gr.Chatbot(\n value=[], \n height=500,\n show_copy_button=True,\n avatar_images=(\"\ud83d\udc64\", \"\ud83e\udd16\")\n )\n \n with gr.Row(equal_height=True):\n msg = gr.Textbox(\n label=\"Your Question\",\n placeholder=\"Ask about weather or alerts (e.g., What's the weather in New York?)\",\n ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "w(equal_height=True):\n msg = gr.Textbox(\n label=\"Your Question\",\n placeholder=\"Ask about weather or alerts (e.g., What's the weather in New York?)\",\n scale=4\n )\n clear_btn = gr.Button(\"Clear Chat\", scale=1)\n \n connect_btn.click(client.connect, inputs=server_path, outputs=status)\n msg.submit(client.process_message, [msg, chatbot], [chatbot, msg])\n clear_btn.click(lambda: [], None, chatbot)\n \n return demo\n\nif __name__ == \"__main__\":\n if not os.getenv(\"ANTHROPIC_API_KEY\"):\n print(\"Warning: ANTHROPIC_API_KEY not found in environment. Please set it in your .env file.\")\n \n interface = gradio_interface()\n interface.launch(debug=True)\n```\n\nWhat this MCP Client does:\n\n- Creates a friendly Gradio chat interface for user interaction\n- Connects to the MCP server you specify\n- Handles conversation history and message formatting\n- Makes call to Claude API with tool definitions\n- Processes tool usage requests from Claude\n- Displays images and other tool outputs in the chat\n- Sends tool results back to Claude for interpretation\n\n", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "To run your MCP application:\n\n- Start a terminal window and run the MCP Client:\n ```bash\n python app.py\n ```\n- Open the Gradio interface at the URL shown (typically http://127.0.0.1:7860)\n- In the Gradio interface, you'll see a field for the MCP Server path. It should default to `gradio_mcp_server.py`.\n- Click \"Connect\" to establish the connection to the MCP server.\n- You should see a message indicating the server connection was successful.\n\n", "heading1": "Running the Application", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now you can chat with Claude and it will be able to generate images based on your descriptions.\n\nTry prompts like:\n- \"Can you generate an image of a mountain landscape at sunset?\"\n- \"Create an image of a cool tabby cat\"\n- \"Generate a picture of a panda wearing sunglasses\"\n\nClaude will recognize these as image generation requests and automatically use the `generate_image` tool from your MCP server.\n\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Here's the high-level flow of what happens during a chat session:\n\n1. Your prompt enters the Gradio interface\n2. The client forwards your prompt to Claude\n3. Claude analyzes the prompt and decides to use the `generate_image` tool\n4. The client sends the tool call to the MCP server\n5. The server calls the external image generation API\n6. The image URL is returned to the client\n7. The client sends the image URL back to Claude\n8. Claude provides a response that references the generated image\n9. The Gradio chat interface displays both Claude's response and the image\n\n\n", "heading1": "How it Works", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now that you have a working MCP system, here are some ideas to extend it:\n\n- Add more tools to your server\n- Improve error handling \n- Add private Huggingface Spaces with authentication for secure tool access\n- Create custom tools that connect to your own APIs or services\n- Implement streaming responses for better user experience\n\n", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Congratulations! You've successfully built an MCP Client and Server that allows Claude to generate images based on text prompts. This is just the beginning of what you can do with Gradio and MCP. This guide enables you to build complex AI applications that can use Claude or any other powerful LLM to interact with virtually any external tool or service.\n\nRead our other Guide on using [Gradio apps as MCP Servers](./building-mcp-server-with-gradio).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from facial recognition to manufacturing quality control.\n\nState-of-the-art image classifiers are based on the _transformers_ architectures, originally popularized for NLP tasks. Such architectures are typically called vision transformers (ViT). Such models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in a **single line of Python**, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a model from the [Hugging Face Model Hub](https://huggingface.co/models?pipeline_tag=image-classification). The Hub contains thousands of models covering dozens of different machine learning tasks.\n\nExpand the Tasks category on the left sidebar and select \"Image Classification\" as our task of interest. You will then see all of the models on the Hub that are designed to classify images.\n\nAt the time of writing, the most popular one is `google/vit-base-patch16-224`, which has been trained on ImageNet images at a resolution of 224x224 pixels. We will use this model for our demo.\n\n", "heading1": "Step 1 \u2014 Choosing a Vision Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "When using a model from the Hugging Face Hub, we do not need to define the input or output components for the demo. Similarly, we do not need to be concerned with the details of preprocessing or postprocessing.\nAll of these are automatically inferred from the model tags.\n\nBesides the import statement, it only takes a single line of Python to load and launch the demo.\n\nWe use the `gr.Interface.load()` method and pass in the path to the model including the `huggingface/` to designate that it is from the Hugging Face Hub.\n\n```python\nimport gradio as gr\n\ngr.Interface.load(\n \"huggingface/google/vit-base-patch16-224\",\n examples=[\"alligator.jpg\", \"laptop.jpg\"]).launch()\n```\n\nNotice that we have added one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples.\n\nThis produces the following interface, which you can try right here in your browser. When you input an image, it is automatically preprocessed and sent to the Hugging Face Hub API, where it is passed through the model and returned as a human-interpretable prediction. Try uploading your own image!\n\n\n\n---\n\nAnd you're done! In one line of code, you have built a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 2 \u2014 Loading the Vision Transformer Model with Gradio", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "Encoder functions to send audio as base64-encoded data and images as base64-encoded JPEG.\n\n```python\nimport base64\nimport numpy as np\nfrom io import BytesIO\nfrom PIL import Image\n\ndef encode_audio(data: np.ndarray) -> dict:\n \"\"\"Encode audio data (int16 mono) for Gemini.\"\"\"\n return {\n \"mime_type\": \"audio/pcm\",\n \"data\": base64.b64encode(data.tobytes()).decode(\"UTF-8\"),\n }\n\ndef encode_image(data: np.ndarray) -> dict:\n with BytesIO() as output_bytes:\n pil_image = Image.fromarray(data)\n pil_image.save(output_bytes, \"JPEG\")\n bytes_data = output_bytes.getvalue()\n base64_str = str(base64.b64encode(bytes_data), \"utf-8\")\n return {\"mime_type\": \"image/jpeg\", \"data\": base64_str}\n```\n\n\n", "heading1": "1) Encoders for audio and images", "source_page_url": "https://gradio.app/guides/create-immersive-demo", "source_page_title": "Other Tutorials - Create Immersive Demo Guide"}, {"text": "This handler:\n- Opens a Gemini Live session on startup\n- Receives streaming audio from Gemini and yields it back to the client\n- Sends microphone audio as it arrives\n- Sends a video frame at most once per second (to avoid flooding the API)\n- Optionally sends an uploaded image (`gr.Image`) alongside the webcam frame\n\n```python\nimport asyncio\nimport os\nimport time\nimport numpy as np\nimport websockets\nfrom dotenv import load_dotenv\nfrom google import genai\nfrom fastrtc import AsyncAudioVideoStreamHandler, wait_for_item, WebRTCError\n\nload_dotenv()\n\nclass GeminiHandler(AsyncAudioVideoStreamHandler):\n def __init__(self) -> None:\n super().__init__(\n \"mono\",\n output_sample_rate=24000,\n input_sample_rate=16000,\n )\n self.audio_queue = asyncio.Queue()\n self.video_queue = asyncio.Queue()\n self.session = None\n self.last_frame_time = 0.0\n self.quit = asyncio.Event()\n\n def copy(self) -> \"GeminiHandler\":\n return GeminiHandler()\n\n async def start_up(self):\n await self.wait_for_args()\n api_key = self.latest_args[3]\n hf_token = self.latest_args[4]\n if hf_token is None or hf_token == \"\":\n raise WebRTCError(\"HF Token is required\")\n os.environ[\"HF_TOKEN\"] = hf_token\n client = genai.Client(\n api_key=api_key, http_options={\"api_version\": \"v1alpha\"}\n )\n config = {\"response_modalities\": [\"AUDIO\"], \"system_instruction\": \"You are an art critic that will critique the artwork passed in as an image to the user. Critique the artwork in a funny and lighthearted way. Be concise and to the point. Be friendly and engaging. Be helpful and informative. Be funny and lighthearted.\"}\n async with client.aio.live.connect(\n model=\"gemini-2.0-flash-exp\",\n config=config,\n ) as session:\n self.session = session\n while not self.quit.is_set():\n turn = self.session.receiv", "heading1": "2) Implement the Gemini audio-video handler", "source_page_url": "https://gradio.app/guides/create-immersive-demo", "source_page_title": "Other Tutorials - Create Immersive Demo Guide"}, {"text": " model=\"gemini-2.0-flash-exp\",\n config=config,\n ) as session:\n self.session = session\n while not self.quit.is_set():\n turn = self.session.receive()\n try:\n async for response in turn:\n if data := response.data:\n audio = np.frombuffer(data, dtype=np.int16).reshape(1, -1)\n self.audio_queue.put_nowait(audio)\n except websockets.exceptions.ConnectionClosedOK:\n print(\"connection closed\")\n break\n\n Video: receive and (optionally) send frames to Gemini\n async def video_receive(self, frame: np.ndarray):\n self.video_queue.put_nowait(frame)\n if self.session and (time.time() - self.last_frame_time > 1.0):\n self.last_frame_time = time.time()\n await self.session.send(input=encode_image(frame))\n If there is an uploaded image passed alongside the WebRTC component,\n it will be available in latest_args[2]\n if self.latest_args[2] is not None:\n await self.session.send(input=encode_image(self.latest_args[2]))\n\n async def video_emit(self) -> np.ndarray:\n frame = await wait_for_item(self.video_queue, 0.01)\n if frame is not None:\n return frame\n Fallback while waiting for first frame\n return np.zeros((100, 100, 3), dtype=np.uint8)\n\n Audio: forward microphone audio to Gemini\n async def receive(self, frame: tuple[int, np.ndarray]) -> None:\n _, array = frame\n array = array.squeeze() (num_samples,)\n audio_message = encode_audio(array)\n if self.session:\n await self.session.send(input=audio_message)\n\n Audio: emit Gemini\u2019s audio back to the client\n async def emit(self):\n array = await wait_for_item(self.audio_queue, 0.01)\n if array is not None:\n return (self.output_sam", "heading1": "2) Implement the Gemini audio-video handler", "source_page_url": "https://gradio.app/guides/create-immersive-demo", "source_page_title": "Other Tutorials - Create Immersive Demo Guide"}, {"text": " Audio: emit Gemini\u2019s audio back to the client\n async def emit(self):\n array = await wait_for_item(self.audio_queue, 0.01)\n if array is not None:\n return (self.output_sample_rate, array)\n return array\n\n async def shutdown(self) -> None:\n if self.session:\n self.quit.set()\n await self.session.close()\n self.quit.clear()\n```\n\n\n", "heading1": "2) Implement the Gemini audio-video handler", "source_page_url": "https://gradio.app/guides/create-immersive-demo", "source_page_title": "Other Tutorials - Create Immersive Demo Guide"}, {"text": "We\u2019ll add an optional `gr.Image` input alongside the `WebRTC` component. The handler will access this in `self.latest_args[1]` when sending frames to Gemini.\n\n```python\nimport gradio as gr\nfrom fastrtc import Stream, WebRTC, get_hf_turn_credentials\n\n\nstream = Stream(\n handler=GeminiHandler(),\n modality=\"audio-video\",\n mode=\"send-receive\",\n server_rtc_configuration=get_hf_turn_credentials(ttl=600*10000),\n rtc_configuration=get_hf_turn_credentials(),\n additional_inputs=[\n gr.Markdown(\n \"\ud83c\udfa8 Art Critic\\n\\n\"\n \"Provide an image of your artwork or hold it up to the webcam, and Gemini will critique it for you.\"\n \"To get a Gemini API key, please visit the [Gemini API Key](https://aistudio.google.com/apikey) page.\"\n \"To get an HF Token, please visit the [HF Token](https://huggingface.co/settings/tokens) page.\"\n ),\n gr.Image(label=\"Artwork\", value=\"mona_lisa.jpg\", type=\"numpy\", sources=[\"upload\", \"clipboard\"]),\n gr.Textbox(label=\"Gemini API Key\", type=\"password\"),\n gr.Textbox(label=\"HF Token\", type=\"password\"),\n ],\n ui_args={\n \"icon\": \"https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png\",\n \"pulse_color\": \"rgb(255, 255, 255)\",\n \"icon_button_color\": \"rgb(255, 255, 255)\",\n \"title\": \"Gemini Audio Video Chat\",\n },\n time_limit=90,\n concurrency_limit=5,\n)\n\nif __name__ == \"__main__\":\n stream.ui.launch()\n\n```\n\nReferences\n- Gemini Audio Video Chat reference code: [Hugging Face Space](https://huggingface.co/spaces/gradio/gemini-audio-video/blob/main/app.py)\n- FastRTC docs: `https://fastrtc.org`\n- Audio + video user guide: `https://fastrtc.org/userguide/audio-video/`\n- Gradio component integration: `https://fastrtc.org/userguide/gradio/`\n- Cookbook (live demos + code): `https://fastrtc.org/cookbook/`\n", "heading1": "3) Setup Stream and Gradio UI", "source_page_url": "https://gradio.app/guides/create-immersive-demo", "source_page_title": "Other Tutorials - Create Immersive Demo Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from autonomous vehicles to medical imaging.\n\nSuch models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). We will be using a pretrained image classification model, so you should also have `torch` installed.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a pretrained Resnet-18 model, as it is easily downloadable from [PyTorch Hub](https://pytorch.org/hub/pytorch_vision_resnet/). You can use a different pretrained model or train your own.\n\n```python\nimport torch\n\nmodel = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n```\n\nBecause we will be using the model for inference, we have called the `.eval()` method.\n\n", "heading1": "Step 1 \u2014 Setting up the Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Next, we will need to define a function that takes in the _user input_, which in this case is an image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://git.io/JJkYN).\n\nIn the case of our pretrained model, it will look like this:\n\n```python\nimport requests\nfrom PIL import Image\nfrom torchvision import transforms\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\ndef predict(inp):\n inp = transforms.ToTensor()(inp).unsqueeze(0)\n with torch.no_grad():\n prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n return confidences\n```\n\nLet's break this down. The function takes one parameter:\n\n- `inp`: the input image as a `PIL` image\n\nThen, the function converts the image to a PIL Image and then eventually a PyTorch `tensor`, passes it through the model, and returns:\n\n- `confidences`: the predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Now that we have our predictive function set up, we can create a Gradio Interface around it.\n\nIn this case, the input component is a drag-and-drop image component. To create this input, we use `Image(type=\"pil\")` which creates the component and handles the preprocessing to convert that to a `PIL` image.\n\nThe output component will be a `Label`, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as `Label(num_top_classes=3)`.\n\nFinally, we'll add one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:\n\n```python\nimport gradio as gr\n\ngr.Interface(fn=predict,\n inputs=gr.Image(type=\"pil\"),\n outputs=gr.Label(num_top_classes=3),\n examples=[\"lion.jpg\", \"cheetah.jpg\"]).launch()\n```\n\nThis produces the following interface, which you can try right here in your browser (try uploading your own examples!):\n\n\n\n\n---\n\nAnd you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 3 \u2014 Creating a Gradio Interface", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Let's deploy a Gradio-style \"Hello, world\" app that lets a user input their name and then responds with a short greeting. We're not going to use this code as-is in our app, but it's useful to see what the initial Gradio version looks like.\n\n```python\nimport gradio as gr\n\nA simple Gradio interface for a greeting function\ndef greet(name):\n return f\"Hello {name}!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\ndemo.launch()\n```\n\nTo deploy this app on Modal you'll need to\n- define your container image,\n- wrap the Gradio app in a Modal Function,\n- and deploy it using Modal's CLI!\n\nPrerequisite: Install and set up Modal\n\nBefore you get started, you'll need to create a Modal account if you don't already have one. Then you can set up your environment by authenticating with those account credentials.\n\n- Sign up at [modal.com](https://www.modal.com?utm_source=partner&utm_medium=github&utm_campaign=livekit). \n- Install the Modal client in your local development environment.\n```bash\npip install modal\n```\n- Authenticate your account.\n```\nmodal setup\n```\n\nGreat, now we can start building our app!\n\nStep 1: Define our `modal.Image`\nTo start, let's make a new file named `gradio_app.py`, import `modal`, and define our image. Modal `Images` are defined by sequentially calling methods on our `Image` instance. \n\nFor this simple app, we'll \n- start with the `debian_slim` image,\n- choose a Python version (3.12),\n- and install the dependencies - only `fastapi` and `gradio`.\n\n```python\nimport modal\n\napp = modal.App(\"gradio-app\")\nweb_image = modal.Image.debian_slim(python_version=\"3.12\").uv_pip_install(\n \"fastapi[standard]\",\n \"gradio\",\n)\n```\n\nNote, that you don't need to install `gradio` or `fastapi` in your local environement - only `modal` is required locally.\n\nStep 2: Wrap the Gradio app in a Modal-deployed FastAPI app\nLike many Gradio apps, the example above is run by calling `launch()` on our demo at the end of the script. However, Modal doesn't ru", "heading1": "Deploying a simple Gradio app on Modal", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": ".\n\nStep 2: Wrap the Gradio app in a Modal-deployed FastAPI app\nLike many Gradio apps, the example above is run by calling `launch()` on our demo at the end of the script. However, Modal doesn't run scripts, it runs functions - serverless functions to be exact.\n\nTo get Modal to serve our `demo`, we can leverage Gradio and Modal's support for `fastapi` apps. We do this with the `@modal.asgi_app()` function decorator which deploys the web app returned by the function. And we use the `mount_gradio_app` function to add our Gradio `demo` as a route in the web app.\n\n```python\nwith web_image.imports():\n import gradio as gr\n from gradio.routes import mount_gradio_app\n from fastapi import FastAPI\n \n@app.function(\n image=web_image,\n max_containers = 1, we'll come to this later \n)\n@modal.concurrent(max_inputs=100) allow multiple users at one time\n@modal.asgi_app()\ndef ui():\n \"\"\"A simple Gradio interface for a greeting function.\"\"\"\n def greet(name):\n return f\"Hello {name}!\"\n\n demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n\n return mount_gradio_app(app=FastAPI(), blocks=demo, path=\"/\")\n```\n\nLet's quickly review what's going on here:\n- We use the `Image.imports` context manager to define our imports. These will be available when your function runs in the cloud.\n- We move our code inside a Python function, `ui`, and decorate it with `@app.function` which wraps it as a Modal serverless Function. We provide the image and other parameters (we'll cover this later) as inputs to the decorator.\n- We add the `@modal.concurrent` decorator which allows multiple requests per container to be processed at the same time.\n- We add the `@modal.asgi_app` decorator which tells Modal that this particular function is serving an ASGI app (here a `fastapi` app). To use this decorator, your ASGI app needs to be the return value from the function.\n\nStep 3: Deploying on Modal\nTo deploy the app, just run the following command:\n```bash\nmodal dep", "heading1": "Deploying a simple Gradio app on Modal", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": "fastapi` app). To use this decorator, your ASGI app needs to be the return value from the function.\n\nStep 3: Deploying on Modal\nTo deploy the app, just run the following command:\n```bash\nmodal deploy \n```\n\nThe first time you run your app, Modal will build and cache the image which, takes about 30 seconds. As long as you don't change the image, subsequent deployments will only take a few seconds.\n\nAfter the image builds Modal will print the URL to your webapp and to your Modal dashboard. The webapp URL should look something like `https://{workspace}-{environment}--gradio-app-ui.modal.run`. Paste it into your web browser a try out your app!\n\n", "heading1": "Deploying a simple Gradio app on Modal", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": "Sticky Sessions\nGradio relies on a REST API, which is itself stateless. But it does require sticky sessions, meaning that every request from a particular client must be routed to the same container.\n\nA simple way to satisfy this constraint is to set `max_containers = 1` in the `@app.function` decorator and setting the `max_inputs` argument of `@modal.concurrent` to a fairly large number - as we did above. This means that Modal won't spin up more than one container to serve requests to your app which effectively satisfies the sticky session requirement.\n\nFor multi-container deployments that require scaling past one container, Modal's [Sticky Sessions](https://modal.com/docs/guide/sticky-sessions) built on [Modal Servers](https://modal.com/docs/guide/servers) provides strong affinity while still retaining serverless autoscaling. \n\nA Modal Server runs a HTTP server that you start in a `@modal.enter()` method, so we can call `demo.launch()` directly instead of mounting the demo on a FastAPI app. You can enable sticky sessions for a Modal Server by adding the `@modal.sessioned()` decorator. \n\n```python\n@app.server(\n image=web_image,\n port=8000,\n max_concurrency=1, sessions per container before Modal scales up\n unauthenticated=true\n)\n@modal.sessioned()\nclass GradioServer:\n @modal.enter()\n def start(self):\n import gradio as gr\n\n \"\"\"A simple Gradio interface for a greeting function.\"\"\"\n def greet(name):\n return f\"Hello {name}!\"\n\n demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n demo.launch(server_name=\"0.0.0.0\", server_port=8000, prevent_thread_lock=True)\n```\n\nEvery request to a sessioned Server must carry a session token, so something needs to start a session for each new visitor. Starting a session is done through the SDK ([sessions.start()](https://modal.com/docs/sdk/py/latest/Serversessionsstart)) or calling the HTTP endpoint (`$SERVER_URL/_modal/sessions/start`). This returns a `modal_s", "heading1": "Important Considerations", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": "sion is done through the SDK ([sessions.start()](https://modal.com/docs/sdk/py/latest/Serversessionsstart)) or calling the HTTP endpoint (`$SERVER_URL/_modal/sessions/start`). This returns a `modal_session_token` that you can add as a query parameter `$SERVER_URL/?modal_session_token=$SESSION_TOKEN` to authorize and route the request to the right container. \n\nModal replaces the query parameter with a cookie so that all of Gradio's subsequent requests reach the same container. A small Modal Function can act as the entry point that starts a session and redirects the visitor to the Gradio app.\n\n```python\n@app.function(image=web_image)\n@modal.fastapi_endpoint()\ndef ui():\n from fastapi.responses import RedirectResponse\n\n server = modal.Server.from_name(\"gradio-app\", \"GradioServer\")\n session = server.sessions.start(idle_timeout=600)\n return RedirectResponse(f\"{server.get_url()}/?modal_session_token={session.token}\")\n```\n\nDeploy the Server with the same command used to deploy the FastAPI demo.\n\n```bash\nmodal deploy \n\n\u2713 Created objects.\n\u251c\u2500\u2500 \u2713 Uploaded \n\u251c\u2500\u2500 \u2713 Created Function ui\n\u2502 \u2514\u2500\u2500 https://modal-labs--gradio-app-ui.modal.run (unauthenticated)\n\u2514\u2500\u2500 \u2713 Created Server GradioServer\n \u2514\u2500\u2500 https://modal-labs--gradio-app-gradioserver.modal.direct (unauthenticated)\n```\n\nUsers can visit the `ui` endpoint's URL and are redirected to the Gradio app with their own sticky session. A session ends after it has had no in-flight requests for `idle_timeout` seconds.\n\nConcurrency and Queues\n\nBoth Gradio and Modal have concepts of concurrency and queues, and getting the most of out of your compute resources requires understanding how these interact.\n\nModal queues client requests to each deployed Function and simultaneously executes requests up to the concurrency limit for that Function. If requests come in and the concurrency limit is already satisfied, Modal will spin up a new container - up to the maximum set for the Function. In our case, our", "heading1": "Important Considerations", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": "e concurrency limit for that Function. If requests come in and the concurrency limit is already satisfied, Modal will spin up a new container - up to the maximum set for the Function. In our case, our Gradio app is represented by one Modal Function, so all requests share one queue and concurrency limit. Therefore Modal constrains the _total_ number of requests running at one time, regardless of what they are doing.\n\nGradio on the other hand, allows developers to utilize multiple queues each with its own concurrency limit. One or more event listeners can then be assigned to a queue which is useful to manage GPU resources for computationally expensive requests.\n\nThinking carefully about how these queues and limits interact can help you optimize your app's performance and resource optimization while avoiding unwanted results like shared or lost state.\n\nCreating a GPU Function\n\nAnother option to manage GPU utilization is to deploy your GPU computations in their own Modal Function and calling this remote Function from inside your Gradio app. This allows you to take full advantage of Modal's serverless autoscaling while routing all of the client HTTP requests to a single Gradio CPU container.", "heading1": "Important Considerations", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-modal", "source_page_title": "Other Tutorials - Deploying Gradio With Modal Guide"}, {"text": "Gradio features a built-in theming engine that lets you customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `launch()` method of `Blocks` or `Interface`. For example:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Soft())\n ...\n```\n\n
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\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. These are:\n\n\n* `gr.themes.Base()` - the `\"base\"` theme sets the primary color to blue but otherwise has minimal styling, making it particularly useful as a base for creating new, custom themes.\n* `gr.themes.Default()` - the `\"default\"` Gradio 5 theme, with a vibrant orange primary color and gray secondary color.\n* `gr.themes.Origin()` - the `\"origin\"` theme is most similar to Gradio 4 styling. Colors, especially in light mode, are more subdued than the Gradio 5 default theme.\n* `gr.themes.Citrus()` - the `\"citrus\"` theme uses a yellow primary color, highlights form elements that are in focus, and includes fun 3D effects when buttons are clicked.\n* `gr.themes.Monochrome()` - the `\"monochrome\"` theme uses a black primary and white secondary color, and uses serif-style fonts, giving the appearance of a black-and-white newspaper. \n* `gr.themes.Soft()` - the `\"soft\"` theme uses a purple primary color and white secondary color. It also increases the border radius around buttons and form elements and highlights labels.\n* `gr.themes.Glass()` - the `\"glass\"` theme has a blue primary color and a transclucent gray secondary color. The theme also uses vertical gradients to create a glassy effect.\n* `gr.themes.Ocean()` - the `\"ocean\"` theme has a blue-green primary color and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": " the `\"ocean\"` theme has a blue-green primary color and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values for hundreds of CSS variables. You can use prebuilt themes as a starting point for your own custom themes, or you can create your own themes from scratch. Let's take a look at each approach.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "The easiest way to build a theme is using the Theme Builder. To launch the Theme Builder locally, run the following code:\n\n```python\nimport gradio as gr\n\ngr.themes.builder()\n```\n\n$demo_theme_builder\n\nYou can use the Theme Builder running on Spaces above, though it runs much faster when you launch it locally via `gr.themes.builder()`.\n\nAs you edit the values in the Theme Builder, the app will preview updates in real time. You can download the code to generate the theme you've created so you can use it in any Gradio app.\n\nIn the rest of the guide, we will cover building themes programmatically.\n\n", "heading1": "Using the Theme Builder", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Although each theme has hundreds of CSS variables, the values for most these variables are drawn from 8 core variables which can be set through the constructor of each prebuilt theme. Modifying these 8 arguments allows you to quickly change the look and feel of your app.\n\nCore Colors\n\nThe first 3 constructor arguments set the colors of the theme and are `gradio.themes.Color` objects. Internally, these Color objects hold brightness values for the palette of a single hue, ranging from 50, 100, 200..., 800, 900, 950. Other CSS variables are derived from these 3 colors.\n\nThe 3 color constructor arguments are:\n\n- `primary_hue`: This is the color draws attention in your theme. In the default theme, this is set to `gradio.themes.colors.orange`.\n- `secondary_hue`: This is the color that is used for secondary elements in your theme. In the default theme, this is set to `gradio.themes.colors.blue`.\n- `neutral_hue`: This is the color that is used for text and other neutral elements in your theme. In the default theme, this is set to `gradio.themes.colors.gray`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Default(primary_hue=\"red\", secondary_hue=\"pink\"))\n ...\n```\n\nor you could use the `Color` objects directly, like this:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink))\n```\n\n
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\n\nPredefined colors are:\n\n- `slate`\n- `gray`\n- `zinc`\n- `neutral`\n- `stone`\n- `red`\n- `orange`\n- `amber`\n- `yellow`\n- `lime`\n- `green`\n- `emerald`\n- `teal`\n- `cyan`\n- `sky`\n- `blue`\n- `indigo`\n- `violet`\n- `purple`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe nex", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "ld`\n- `teal`\n- `cyan`\n- `sky`\n- `blue`\n- `indigo`\n- `violet`\n- `purple`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe next 3 constructor arguments set the sizing of the theme and are `gradio.themes.Size` objects. Internally, these Size objects hold pixel size values that range from `xxs` to `xxl`. Other CSS variables are derived from these 3 sizes.\n\n- `spacing_size`: This sets the padding within and spacing between elements. In the default theme, this is set to `gradio.themes.sizes.spacing_md`.\n- `radius_size`: This sets the roundedness of corners of elements. In the default theme, this is set to `gradio.themes.sizes.radius_md`.\n- `text_size`: This sets the font size of text. In the default theme, this is set to `gradio.themes.sizes.text_md`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Default(spacing_size=\"sm\", radius_size=\"none\"))\n ...\n```\n\nor you could use the `Size` objects directly, like this:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none))\n ...\n```\n\n
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\n\nThe predefined size objects are:\n\n- `radius_none`\n- `radius_sm`\n- `radius_md`\n- `radius_lg`\n- `spacing_sm`\n- `spacing_md`\n- `spacing_lg`\n- `text_sm`\n- `text_md`\n- `text_lg`\n\nYou could also create your own custom `Size` objects and pass them in.\n\nCore Fonts\n\nThe final 2 constructor arguments set the fonts of the theme. You can pass a list of fonts to each of these arguments to specify fallbacks. If you provide a string, it will be loaded as a system font. If you provide a `gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n- `font`: Th", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "these arguments to specify fallbacks. If you provide a string, it will be loaded as a system font. If you provide a `gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n- `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Sans\")`.\n- `font_mono`: This sets the monospace font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Mono\")`.\n\nYou could modify these values such as the following:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=gr.themes.Default(font=[gr.themes.GoogleFont(\"Inconsolata\"), \"Arial\", \"sans-serif\"]))\n ...\n```\n\n
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\n\nCustom CSS\n\nFor styling beyond what theme variables provide, you can add custom CSS via the `custom_css` attribute. This CSS is bundled with the theme, so it will be included when you upload or download themes from the Hub.\n\n```python\ntheme = gr.themes.Default()\ntheme.custom_css = \"\"\"\nbutton.primary {\n background: linear-gradient(135deg, var(--primary-400), var(--primary-600));\n transition: transform 0.15s ease, box-shadow 0.15s ease;\n}\nbutton.primary:hover {\n transform: translateY(-2px);\n box-shadow: 0 4px 12px color-mix(in srgb, var(--primary-500) 40%, transparent);\n}\n\"\"\"\n\nwith gr.Blocks(theme=theme) as demo:\n gr.Textbox(label=\"Input\")\n gr.Button(\"Submit\", variant=\"primary\")\ndemo.launch()\n```\n\n", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "You can also modify the values of CSS variables after the theme has been loaded. To do so, use the `.set()` method of the theme object to get access to the CSS variables. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n loader_color=\"FF0000\",\n slider_color=\"FF0000\",\n)\n\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=theme)\n```\n\nIn the example above, we've set the `loader_color` and `slider_color` variables to `FF0000`, despite the overall `primary_color` using the blue color palette. You can set any CSS variable that is defined in the theme in this manner.\n\nYour IDE type hinting should help you navigate these variables. Since there are so many CSS variables, let's take a look at how these variables are named and organized.\n\nCSS Variable Naming Conventions\n\nCSS variable names can get quite long, like `button_primary_background_fill_hover_dark`! However they follow a common naming convention that makes it easy to understand what they do and to find the variable you're looking for. Separated by underscores, the variable name is made up of:\n\n1. The target element, such as `button`, `slider`, or `block`.\n2. The target element type or sub-element, such as `button_primary`, or `block_label`.\n3. The property, such as `button_primary_background_fill`, or `block_label_border_width`.\n4. Any relevant state, such as `button_primary_background_fill_hover`.\n5. If the value is different in dark mode, the suffix `_dark`. For example, `input_border_color_focus_dark`.\n\nOf course, many CSS variable names are shorter than this, such as `table_border_color`, or `input_shadow`.\n\nCSS Variable Organization\n\nThough there are hundreds of CSS variables, they do not all have to have individual values. They draw their values by referencing a set of core variables and referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of indi", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "cing a set of core variables and referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of individual elements that we may want to modify.\n\nReferencing Core Variables\n\nTo reference one of the core constructor variables, precede the variable name with an asterisk. To reference a core color, use the `*primary_`, `*secondary_`, or `*neutral_` prefix, followed by the brightness value. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n button_primary_background_fill=\"*primary_200\",\n button_primary_background_fill_hover=\"*primary_300\",\n)\n```\n\nIn the example above, we've set the `button_primary_background_fill` and `button_primary_background_fill_hover` variables to `*primary_200` and `*primary_300`. These variables will be set to the 200 and 300 brightness values of the blue primary color palette, respectively.\n\nSimilarly, to reference a core size, use the `*spacing_`, `*radius_`, or `*text_` prefix, followed by the size value. For example:\n\n```python\ntheme = gr.themes.Default(radius_size=\"md\").set(\n button_primary_border_radius=\"*radius_xl\",\n)\n```\n\nIn the example above, we've set the `button_primary_border_radius` variable to `*radius_xl`. This variable will be set to the `xl` setting of the medium radius size range.\n\nReferencing Other Variables\n\nVariables can also reference each other. For example, look at the example below:\n\n```python\ntheme = gr.themes.Default().set(\n button_primary_background_fill=\"FF0000\",\n button_primary_background_fill_hover=\"FF0000\",\n button_primary_border=\"FF0000\",\n)\n```\n\nHaving to set these values to a common color is a bit tedious. Instead, we can reference the `button_primary_background_fill` variable in the `button_primary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n button_primary_background_fill=\"F", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "ll` variable in the `button_primary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n button_primary_background_fill=\"FF0000\",\n button_primary_background_fill_hover=\"*button_primary_background_fill\",\n button_primary_border=\"*button_primary_background_fill\",\n)\n```\n\nNow, if we change the `button_primary_background_fill` variable, the `button_primary_background_fill_hover` and `button_primary_border` variables will automatically update as well.\n\nThis is particularly useful if you intend to share your theme - it makes it easy to modify the theme without having to change every variable.\n\nNote that dark mode variables automatically reference each other. For example:\n\n```python\ntheme = gr.themes.Default().set(\n button_primary_background_fill=\"FF0000\",\n button_primary_background_fill_dark=\"AAAAAA\",\n button_primary_border=\"*button_primary_background_fill\",\n button_primary_border_dark=\"*button_primary_background_fill_dark\",\n)\n```\n\n`button_primary_border_dark` will draw its value from `button_primary_background_fill_dark`, because dark mode always draw from the dark version of the variable.\n\n", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "For a full list of all available CSS variables, see the [CSS Variables Reference](/main/guides/css-variables-reference).\n\n", "heading1": "CSS Variables Reference", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Let's say you want to create a theme from scratch! We'll go through it step by step - you can also see the source of prebuilt themes in the gradio source repo for reference - [here's the source](https://github.com/gradio-app/gradio/blob/main/gradio/themes/monochrome.py) for the Monochrome theme.\n\nOur new theme class will inherit from `gradio.themes.Base`, a theme that sets a lot of convenient defaults. Let's make a simple demo that creates a dummy theme called Seafoam, and make a simple app that uses it.\n\n$code_theme_new_step_1\n\n
\n\n
\n\nThe Base theme is very barebones, and uses `gr.themes.Blue` as it primary color - you'll note the primary button and the loading animation are both blue as a result. Let's change the defaults core arguments of our app. We'll overwrite the constructor and pass new defaults for the core constructor arguments.\n\nWe'll use `gr.themes.Emerald` as our primary color, and set secondary and neutral hues to `gr.themes.Blue`. We'll make our text larger using `text_lg`. We'll use `Quicksand` as our default font, loaded from Google Fonts.\n\n$code_theme_new_step_2\n\n
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\n\nSee how the primary button and the loading animation are now green? These CSS variables are tied to the `primary_hue` variable.\n\nLet's modify the theme a bit more directly. We'll call the `set()` method to overwrite CSS variable values explicitly. We can use any CSS logic, and reference our core constructor arguments using the `*` prefix.\n\n$code_theme_new_step_3\n\n
\n\n
\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code ", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "ght\"\n\tframeborder=\"0\"\n>\n\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code of the other prebuilt themes](https://github.com/gradio-app/gradio/blob/main/gradio/themes) to see how they modified the base theme. You can also find your browser's Inspector useful to select elements from the UI and see what CSS variables are being used in the styles panel.\n\n", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Once you have created a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it!\n\nUploading a Theme\n\nThere are two ways to upload a theme, via the theme class instance or the command line. We will cover both of them with the previously created `seafoam` theme.\n\n- Via the class instance\n\nEach theme instance has a method called `push_to_hub` we can use to upload a theme to the HuggingFace hub.\n\n```python\nseafoam.push_to_hub(repo_name=\"seafoam\",\n version=\"0.0.1\",\n\t\t\t\t\ttoken=\"\")\n```\n\n- Via the command line\n\nFirst save the theme to disk\n\n```python\nseafoam.dump(filename=\"seafoam.json\")\n```\n\nThen use the `upload_theme` command:\n\n```bash\nupload_theme\\\n\"seafoam.json\"\\\n\"seafoam\"\\\n--version \"0.0.1\"\\\n--token \"\"\n```\n\nIn order to upload a theme, you must have a HuggingFace account and pass your [Access Token](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\nas the `token` argument. However, if you log in via the [HuggingFace command line](https://huggingface.co/docs/huggingface_hub/quick-startlogin) (which comes installed with `gradio`),\nyou can omit the `token` argument.\n\nThe `version` argument lets you specify a valid [semantic version](https://www.geeksforgeeks.org/introduction-semantic-versioning/) string for your theme.\nThat way your users are able to specify which version of your theme they want to use in their apps. This also lets you publish updates to your theme without worrying\nabout changing how previously created apps look. The `version` argument is optional. If omitted, the next patch version is automatically applied.\n\nTheme Previews\n\nBy calling `push_to_hub` or `upload_theme`, the theme assets will be stored in a [HuggingFace space](https://huggingface.co/docs/hub/spaces-overview).\n\nFor example, the theme preview for the calm seafoam theme is here: [calm seafoam preview](https://huggingface.co/spaces/shivalikasingh/calm_seafoam).\n\n
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\n\nDiscovering Themes\n\nThe [Theme Gallery](https://gradio.app/themes/gallery) on the Gradio website shows all official and community themes. You can search, filter by official or community themes, and preview each theme's colors, fonts, and live demo.\n\nCommunity themes are sourced from the [gradio/theme-gallery](https://huggingface.co/datasets/gradio/theme-gallery) dataset on HuggingFace. To add your theme to the gallery, open a Pull Request to that dataset with your theme's metadata in `manifest.json`.\n\nDownloading\n\nTo use a theme from the hub, use the `from_hub` method on the `ThemeClass` and pass it to your app:\n\n```python\nmy_theme = gr.Theme.from_hub(\"gradio/seafoam\")\n\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=my_theme)\n```\n\nYou can also pass the theme string directly to the `launch()` method of `Blocks` or `Interface` (e.g. `demo.launch(theme=\"gradio/seafoam\")`)\n\nYou can pin your app to an upstream theme version by using semantic versioning expressions.\n\nFor example, the following would ensure the theme we load from the `seafoam` repo was between versions `0.0.1` and `0.1.0`:\n\n```python\nwith gr.Blocks() as demo:\n ... your code here\ndemo.launch(theme=\"gradio/seafoam@>=0.0.1,<0.1.0\")\n ....\n```\n\nVersion Compatibility\n\nWhen you save a theme with `theme.dump()` or upload it with `theme.push_to_hub()`, the current Gradio version is automatically added. When someone loads a theme created with a different major or minor version of Gradio, they'll see a warning:\n\n```\nUserWarning: This theme was created for Gradio 5.0.0, but you are using Gradio 5.1.0. Some styles may not work as expected.\n```\n\nThis helps prevent unexpected styling issues", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "hey'll see a warning:\n\n```\nUserWarning: This theme was created for Gradio 5.0.0, but you are using Gradio 5.1.0. Some styles may not work as expected.\n```\n\nThis helps prevent unexpected styling issues when themes rely on CSS variables that may have changed between Gradio versions. Patch version differences (e.g. 5.0.0 vs 5.0.1) will not trigger a warning.\n\nEnjoy creating your own themes! If you make one you're proud of, please share it with the world by uploading it to the hub!\nIf you tag us on [Twitter](https://twitter.com/gradio) we can give your theme a shout out!\n\n\n", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Data visualization is a crucial aspect of data analysis and machine learning. The Gradio `DataFrame` component is a popular way to display tabular data within a web application. \n\nBut what if you want to stylize the table of data? What if you want to add background colors, partially highlight cells, or change the display precision of numbers? This Guide is for you!\n\n\n\nLet's dive in!\n\n**Prerequisites**: We'll be using the `gradio.Blocks` class in our examples.\nYou can [read the Guide to Blocks first](https://gradio.app/blocks-and-event-listeners) if you are not already familiar with it. Also please make sure you are using the **latest version** version of Gradio: `pip install --upgrade gradio`.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "The Gradio `DataFrame` component now supports values of the type `Styler` from the `pandas` class. This allows us to reuse the rich existing API and documentation of the `Styler` class instead of inventing a new style format on our own. Here's a complete example of how it looks:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n \"A\" : [14, 4, 5, 4, 1], \n \"B\" : [5, 2, 54, 3, 2], \n \"C\" : [20, 20, 7, 3, 8], \n \"D\" : [14, 3, 6, 2, 6], \n \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n gr.DataFrame(styler)\n \ndemo.launch()\n```\n\nThe Styler class can be used to apply conditional formatting and styling to dataframes, making them more visually appealing and interpretable. You can highlight certain values, apply gradients, or even use custom CSS to style the DataFrame. The Styler object is applied to a DataFrame and it returns a new object with the relevant styling properties, which can then be previewed directly, or rendered dynamically in a Gradio interface.\n\nTo read more about the Styler object, read the official `pandas` documentation at: https://pandas.pydata.org/docs/user_guide/style.html\n\nBelow, we'll explore a few examples:\n\nHighlighting Cells\n\nOk, so let's revisit the previous example. We start by creating a `pd.DataFrame` object and then highlight the highest value in each row with a light green color:\n\n```python\nimport pandas as pd \n\nCreating a sample dataframe\ndf = pd.DataFrame({\n \"A\" : [14, 4, 5, 4, 1], \n \"B\" : [5, 2, 54, 3, 2], \n \"C\" : [20, 20, 7, 3, 8], \n \"D\" : [14, 3, 6, 2, 6], \n \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFra", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": ", 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFrame` and we can visualize our colorful table of data in 4 lines of python:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n gr.Dataframe(styler)\n \ndemo.launch()\n```\n\nHere's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-highlight.png)\n\nFont Colors\n\nApart from highlighting cells, you might want to color specific text within the cells. Here's how you can change text colors for certain columns:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n \"A\" : [14, 4, 5, 4, 1], \n \"B\" : [5, 2, 54, 3, 2], \n \"C\" : [20, 20, 7, 3, 8], \n \"D\" : [14, 3, 6, 2, 6], \n \"E\" : [23, 45, 64, 32, 23]\n}) \n\nFunction to apply text color\ndef highlight_cols(x): \n df = x.copy() \n df.loc[:, :] = 'color: purple'\n df[['B', 'C', 'E']] = 'color: green'\n return df \n\nApplying the style function\ns = df.style.apply(highlight_cols, axis = None)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n gr.DataFrame(s)\n \ndemo.launch()\n```\n\nIn this script, we define a custom function highlight_cols that changes the text color to purple for all cells, but overrides this for columns B, C, and E with green. Here's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-color.png)\n\nDisplay Precision \n\nSometimes, the data you are dealing with might have long floating numbers, and you may want to display only a fixed number of decimals for simplicity. The pandas Styler object allows you to format the precision of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n \"A\" : [14.12345, 4.", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "on of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n \"A\" : [14.12345, 4.23456, 5.34567, 4.45678, 1.56789], \n \"B\" : [5.67891, 2.78912, 54.89123, 3.91234, 2.12345], \n ... other columns\n}) \n\nSetting the precision of numbers to 2 decimal places\ns = df.style.format(\"{:.2f}\")\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n gr.DataFrame(s)\n \ndemo.launch()\n```\n\nIn this script, the format method of the Styler object is used to set the precision of numbers to two decimal places. Much cleaner now:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-precision.png)\n\n\n\n", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "So far, we've been restricting ourselves to styling that is supported by the Pandas `Styler` class. But what if you want to create custom styles like partially highlighting cells based on their values:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/dataframe_custom_styling.png)\n\n\nThis isn't possible with `Styler`, but you can do this by creating your own **`styling`** array, which is a 2D array the same size and shape as your data. Each element in this list should be a CSS style string (e.g. `\"background-color: green\"`) that applies to the `` element containing the cell value (or an empty string if no custom CSS should be applied). Similarly, you can create a **`display_value`** array which controls the value that is displayed in each cell (which can be different the underlying value which is the one that is used for searching/sorting).\n\nHere's the complete code for how to can use custom styling with `gr.Dataframe` as in the screenshot above:\n\n$code_dataframe_custom_styling\n\n\n", "heading1": "Custom Styling", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "One thing to keep in mind is that the gradio `DataFrame` component only accepts custom styling objects when it is non-interactive (i.e. in \"static\" mode). If the `DataFrame` component is interactive, then the styling information is ignored and instead the raw table values are shown instead. \n\nThe `DataFrame` component is by default non-interactive, unless it is used as an input to an event. In which case, you can force the component to be non-interactive by setting the `interactive` prop like this:\n\n```python\nc = gr.DataFrame(styler, interactive=False)\n```\n\n", "heading1": "Note about Interactivity", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "This is just a taste of what's possible using the `gradio.DataFrame` component with the `Styler` class from `pandas`. Try it out and let us know what you think!", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "It seems that cryptocurrencies, [NFTs](https://www.nytimes.com/interactive/2022/03/18/technology/nft-guide.html), and the web3 movement are all the rage these days! Digital assets are being listed on marketplaces for astounding amounts of money, and just about every celebrity is debuting their own NFT collection. While your crypto assets [may be taxable, such as in Canada](https://www.canada.ca/en/revenue-agency/programs/about-canada-revenue-agency-cra/compliance/digital-currency/cryptocurrency-guide.html), today we'll explore some fun and tax-free ways to generate your own assortment of procedurally generated [CryptoPunks](https://www.larvalabs.com/cryptopunks).\n\nGenerative Adversarial Networks, often known just as _GANs_, are a specific class of deep-learning models that are designed to learn from an input dataset to create (_generate!_) new material that is convincingly similar to elements of the original training set. Famously, the website [thispersondoesnotexist.com](https://thispersondoesnotexist.com/) went viral with lifelike, yet synthetic, images of people generated with a model called StyleGAN2. GANs have gained traction in the machine learning world, and are now being used to generate all sorts of images, text, and even [music](https://salu133445.github.io/musegan/)!\n\nToday we'll briefly look at the high-level intuition behind GANs, and then we'll build a small demo around a pre-trained GAN to see what all the fuss is about. Here's a [peek](https://nimaboscarino-cryptopunks.hf.space) at what we're going to be putting together.\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). To use the pretrained model, also install `torch` and `torchvision`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Originally proposed in [Goodfellow et al. 2014](https://arxiv.org/abs/1406.2661), GANs are made up of neural networks which compete with the intention of outsmarting each other. One network, known as the _generator_, is responsible for generating images. The other network, the _discriminator_, receives an image at a time from the generator along with a **real** image from the training data set. The discriminator then has to guess: which image is the fake?\n\nThe generator is constantly training to create images which are trickier for the discriminator to identify, while the discriminator raises the bar for the generator every time it correctly detects a fake. As the networks engage in this competitive (_adversarial!_) relationship, the images that get generated improve to the point where they become indistinguishable to human eyes!\n\nFor a more in-depth look at GANs, you can take a look at [this excellent post on Analytics Vidhya](https://www.analyticsvidhya.com/blog/2021/06/a-detailed-explanation-of-gan-with-implementation-using-tensorflow-and-keras/) or this [PyTorch tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html). For now, though, we'll dive into a demo!\n\n", "heading1": "GANs: a very brief introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "To generate new images with a GAN, you only need the generator model. There are many different architectures that the generator could use, but for this demo we'll use a pretrained GAN generator model with the following architecture:\n\n```python\nfrom torch import nn\n\nclass Generator(nn.Module):\n Refer to the link below for explanations about nc, nz, and ngf\n https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n def __init__(self, nc=4, nz=100, ngf=64):\n super(Generator, self).__init__()\n self.network = nn.Sequential(\n nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n nn.BatchNorm2d(ngf * 4),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n nn.BatchNorm2d(ngf * 2),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n nn.BatchNorm2d(ngf),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n nn.Tanh(),\n )\n\n def forward(self, input):\n output = self.network(input)\n return output\n```\n\nWe're taking the generator from [this repo by @teddykoker](https://github.com/teddykoker/cryptopunks-gan/blob/main/train.pyL90), where you can also see the original discriminator model structure.\n\nAfter instantiating the model, we'll load in the weights from the Hugging Face Hub, stored at [nateraw/cryptopunks-gan](https://huggingface.co/nateraw/cryptopunks-gan):\n\n```python\nfrom huggingface_hub import hf_hub_download\nimport torch\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n```\n\n", "heading1": "Step 1 \u2014 Create the Generator model", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "The `predict` function is the key to making Gradio work! Whatever inputs we choose through the Gradio interface will get passed through our `predict` function, which should operate on the inputs and generate outputs that we can display with Gradio output components. For GANs it's common to pass random noise into our model as the input, so we'll generate a tensor of random numbers and pass that through the model. We can then use `torchvision`'s `save_image` function to save the output of the model as a `png` file, and return the file name:\n\n```python\nfrom torchvision.utils import save_image\n\ndef predict(seed):\n num_punks = 4\n torch.manual_seed(seed)\n z = torch.randn(num_punks, 100, 1, 1)\n punks = model(z)\n save_image(punks, \"punks.png\", normalize=True)\n return 'punks.png'\n```\n\nWe're giving our `predict` function a `seed` parameter, so that we can fix the random tensor generation with a seed. We'll then be able to reproduce punks if we want to see them again by passing in the same seed.\n\n_Note!_ Our model needs an input tensor of dimensions 100x1x1 to do a single inference, or (BatchSize)x100x1x1 for generating a batch of images. In this demo we'll start by generating 4 punks at a time.\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "At this point you can even run the code you have with `predict()`, and you'll find your freshly generated punks in your file system at `./punks.png`. To make a truly interactive demo, though, we'll build out a simple interface with Gradio. Our goals here are to:\n\n- Set a slider input so users can choose the \"seed\" value\n- Use an image component for our output to showcase the generated punks\n- Use our `predict()` to take the seed and generate the images\n\nWith `gr.Interface()`, we can define all of that with a single function call:\n\n```python\nimport gradio as gr\n\ngr.Interface(\n predict,\n inputs=[\n gr.Slider(0, 1000, label='Seed', value=42),\n ],\n outputs=\"image\",\n).launch()\n```\n\n\n", "heading1": "Step 3 \u2014 Creating a Gradio interface", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Generating 4 punks at a time is a good start, but maybe we'd like to control how many we want to make each time. Adding more inputs to our Gradio interface is as simple as adding another item to the `inputs` list that we pass to `gr.Interface`:\n\n```python\ngr.Interface(\n predict,\n inputs=[\n gr.Slider(0, 1000, label='Seed', value=42),\n gr.Slider(4, 64, label='Number of Punks', step=1, value=10), Adding another slider!\n ],\n outputs=\"image\",\n).launch()\n```\n\nThe new input will be passed to our `predict()` function, so we have to make some changes to that function to accept a new parameter:\n\n```python\ndef predict(seed, num_punks):\n torch.manual_seed(seed)\n z = torch.randn(num_punks, 100, 1, 1)\n punks = model(z)\n save_image(punks, \"punks.png\", normalize=True)\n return 'punks.png'\n```\n\nWhen you relaunch your interface, you should see a second slider that'll let you control the number of punks!\n\n", "heading1": "Step 4 \u2014 Even more punks!", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Your Gradio app is pretty much good to go, but you can add a few extra things to really make it ready for the spotlight \u2728\n\nWe can add some examples that users can easily try out by adding this to the `gr.Interface`:\n\n```python\ngr.Interface(\n ...\n keep everything as it is, and then add\n examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n cache_examples=True, cache_examples is optional\n).launch()\n```\n\nThe `examples` parameter takes a list of lists, where each item in the sublists is ordered in the same order that we've listed the `inputs`. So in our case, `[seed, num_punks]`. Give it a try!\n\nYou can also try adding a `title`, `description`, and `article` to the `gr.Interface`. Each of those parameters accepts a string, so try it out and see what happens \ud83d\udc40 `article` will also accept HTML, as [explored in a previous guide](/guides/key-features/descriptive-content)!\n\nWhen you're all done, you may end up with something like [this](https://nimaboscarino-cryptopunks.hf.space).\n\nFor reference, here is our full code:\n\n```python\nimport torch\nfrom torch import nn\nfrom huggingface_hub import hf_hub_download\nfrom torchvision.utils import save_image\nimport gradio as gr\n\nclass Generator(nn.Module):\n Refer to the link below for explanations about nc, nz, and ngf\n https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n def __init__(self, nc=4, nz=100, ngf=64):\n super(Generator, self).__init__()\n self.network = nn.Sequential(\n nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n nn.BatchNorm2d(ngf * 4),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n nn.BatchNorm2d(ngf * 2),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n nn.BatchNorm2d(ngf),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n nn.Tanh(),\n )\n\n def forward", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": ", ngf, 4, 2, 0, bias=False),\n nn.BatchNorm2d(ngf),\n nn.ReLU(True),\n nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n nn.Tanh(),\n )\n\n def forward(self, input):\n output = self.network(input)\n return output\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n\ndef predict(seed, num_punks):\n torch.manual_seed(seed)\n z = torch.randn(num_punks, 100, 1, 1)\n punks = model(z)\n save_image(punks, \"punks.png\", normalize=True)\n return 'punks.png'\n\ngr.Interface(\n predict,\n inputs=[\n gr.Slider(0, 1000, label='Seed', value=42),\n gr.Slider(4, 64, label='Number of Punks', step=1, value=10),\n ],\n outputs=\"image\",\n examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n cache_examples=True,\n).launch()\n```\n\n---\n\nCongratulations! You've built out your very own GAN-powered CryptoPunks generator, with a fancy Gradio interface that makes it easy for anyone to use. Now you can [scour the Hub for more GANs](https://huggingface.co/models?other=gan) (or train your own) and continue making even more awesome demos \ud83e\udd17\n", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Gradio is a Python library that allows you to quickly create customizable web apps for your machine learning models and data processing pipelines. Gradio apps can be deployed on [Hugging Face Spaces](https://hf.space) for free.\n\nIn some cases though, you might want to deploy a Gradio app on your own web server. You might already be using [Nginx](https://www.nginx.com/), a highly performant web server, to serve your website (say `https://www.example.com`), and you want to attach Gradio to a specific subpath on your website (e.g. `https://www.example.com/gradio-demo`).\n\nIn this Guide, we will guide you through the process of running a Gradio app behind Nginx on your own web server to achieve this.\n\n**Prerequisites**\n\n1. A Linux web server with [Nginx installed](https://www.nginx.com/blog/setting-up-nginx/) and [Gradio installed](/quickstart)\n2. A working Gradio app saved as a python file on your web server\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Start by editing the Nginx configuration file on your web server. By default, this is located at: `/etc/nginx/nginx.conf`\n\nIn the `http` block, add the following line to include server block configurations from a separate file:\n\n```bash\ninclude /etc/nginx/sites-enabled/*;\n```\n\n2. Create a new file in the `/etc/nginx/sites-available` directory (create the directory if it does not already exist), using a filename that represents your app, for example: `sudo nano /etc/nginx/sites-available/my_gradio_app`\n\n3. Paste the following into your file editor:\n\n```bash\nserver {\n listen 80;\n server_name example.com www.example.com; Change this to your domain name\n\n location /gradio-demo/ { Change this if you'd like to server your Gradio app on a different path\n proxy_pass http://127.0.0.1:7860/; Change this if your Gradio app will be running on a different port\n proxy_buffering off;\n proxy_redirect off;\n proxy_http_version 1.1;\n proxy_set_header Upgrade $http_upgrade;\n proxy_set_header Connection \"upgrade\";\n proxy_set_header Host $host;\n proxy_set_header X-Forwarded-Host $host;\n proxy_set_header X-Forwarded-Proto $scheme;\n }\n}\n```\n\n\nTip: Setting the `X-Forwarded-Host` and `X-Forwarded-Proto` headers is important as Gradio uses these, in conjunction with the `root_path` parameter discussed below, to construct the public URL that your app is being served on. Gradio uses the public URL to fetch various static assets. If these headers are not set, your Gradio app may load in a broken state.\n\n*Note:* The `$host` variable does not include the host port. If you are serving your Gradio application on a raw IP address and port, you should use the `$http_host` variable instead, in these lines:\n\n```bash\n proxy_set_header Host $host;\n proxy_set_header X-Forwarded-Host $host;\n```\n\n", "heading1": "Editing your Nginx configuration file", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Before you launch your Gradio app, you'll need to set the `root_path` to be the same as the subpath that you specified in your nginx configuration. This is necessary for Gradio to run on any subpath besides the root of the domain.\n\n *Note:* Instead of a subpath, you can also provide a complete URL for `root_path` (beginning with `http` or `https`) in which case the `root_path` is treated as an absolute URL instead of a URL suffix (but in this case, you'll need to update the `root_path` if the domain changes).\n\nHere's a simple example of a Gradio app with a custom `root_path` corresponding to the Nginx configuration above.\n\n```python\nimport gradio as gr\nimport time\n\ndef test(x):\ntime.sleep(4)\nreturn x\n\ngr.Interface(test, \"textbox\", \"textbox\").queue().launch(root_path=\"/gradio-demo\")\n```\n\n2. Start a `tmux` session by typing `tmux` and pressing enter (optional)\n\nIt's recommended that you run your Gradio app in a `tmux` session so that you can keep it running in the background easily\n\n3. Then, start your Gradio app. Simply type in `python` followed by the name of your Gradio python file. By default, your app will run on `localhost:7860`, but if it starts on a different port, you will need to update the nginx configuration file above.\n\n", "heading1": "Run your Gradio app on your web server", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. If you are in a tmux session, exit by typing CTRL+B (or CMD+B), followed by the \"D\" key.\n\n2. Finally, restart nginx by running `sudo systemctl restart nginx`.\n\nAnd that's it! If you visit `https://example.com/gradio-demo` on your browser, you should see your Gradio app running there\n\n", "heading1": "Restart Nginx", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "The Hugging Face Hub is a central platform that has hundreds of thousands of [models](https://huggingface.co/models), [datasets](https://huggingface.co/datasets) and [demos](https://huggingface.co/spaces) (also known as Spaces). \n\nGradio has multiple features that make it extremely easy to leverage existing models and Spaces on the Hub. This guide walks through these features.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face has a service called [Serverless Inference Endpoints](https://huggingface.co/docs/api-inference/index), which allows you to send HTTP requests to models on the Hub. The API includes a generous free tier, and you can switch to [dedicated Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated) when you want to use it in production. Gradio integrates directly with Serverless Inference Endpoints so that you can create a demo simply by specifying a model's name (e.g. `Helsinki-NLP/opus-mt-en-es`), like this:\n\n```python\nimport gradio as gr\n\ndemo = gr.load(\"Helsinki-NLP/opus-mt-en-es\", src=\"models\")\n\ndemo.launch()\n```\n\nFor any Hugging Face model supported in Inference Endpoints, Gradio automatically infers the expected input and output and make the underlying server calls, so you don't have to worry about defining the prediction function. \n\nNotice that we just put specify the model name and state that the `src` should be `models` (Hugging Face's Model Hub). There is no need to install any dependencies (except `gradio`) since you are not loading the model on your computer.\n\nYou might notice that the first inference takes a little bit longer. This happens since the Inference Endpoints is loading the model in the server. You get some benefits afterward:\n\n- The inference will be much faster.\n- The server caches your requests.\n- You get built-in automatic scaling.\n\n", "heading1": "Demos with the Hugging Face Inference Endpoints", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "[Hugging Face Spaces](https://hf.co/spaces) allows anyone to host their Gradio demos freely, and uploading your Gradio demos take a couple of minutes. You can head to [hf.co/new-space](https://huggingface.co/new-space), select the Gradio SDK, create an `app.py` file, and voila! You have a demo you can share with anyone else. To learn more, read [this guide how to host on Hugging Face Spaces using the website](https://huggingface.co/blog/gradio-spaces).\n\nAlternatively, you can create a Space programmatically, making use of the [huggingface_hub client library](https://huggingface.co/docs/huggingface_hub/index) library. Here's an example:\n\n```python\nfrom huggingface_hub import (\n create_repo,\n get_full_repo_name,\n upload_file,\n)\ncreate_repo(name=target_space_name, token=hf_token, repo_type=\"space\", space_sdk=\"gradio\")\nrepo_name = get_full_repo_name(model_id=target_space_name, token=hf_token)\nfile_url = upload_file(\n path_or_fileobj=\"file.txt\",\n path_in_repo=\"app.py\",\n repo_id=repo_name,\n repo_type=\"space\",\n token=hf_token,\n)\n```\n\nHere, `create_repo` creates a gradio repo with the target name under a specific account using that account's Write Token. `repo_name` gets the full repo name of the related repo. Finally `upload_file` uploads a file inside the repo with the name `app.py`.\n\n\n", "heading1": "Hosting your Gradio demos on Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "You can also use and remix existing Gradio demos on Hugging Face Spaces. For example, you could take two existing Gradio demos on Spaces and put them as separate tabs and create a new demo. You can run this new demo locally, or upload it to Spaces, allowing endless possibilities to remix and create new demos!\n\nHere's an example that does exactly that:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n with gr.Tab(\"Translate to Spanish\"):\n gr.load(\"gradio/en2es\", src=\"spaces\")\n with gr.Tab(\"Translate to French\"):\n gr.load(\"abidlabs/en2fr\", src=\"spaces\")\n\ndemo.launch()\n```\n\nNotice that we use `gr.load()`, the same method we used to load models using Inference Endpoints. However, here we specify that the `src` is `spaces` (Hugging Face Spaces). \n\nNote: loading a Space in this way may result in slight differences from the original Space. In particular, any attributes that apply to the entire Blocks, such as the theme or custom CSS/JS, will not be loaded. You can copy these properties from the Space you are loading into your own `Blocks` object. \n\n", "heading1": "Loading demos from Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face's popular `transformers` library has a very easy-to-use abstraction, [`pipeline()`](https://huggingface.co/docs/transformers/v4.16.2/en/main_classes/pipelinestransformers.pipeline) that handles most of the complex code to offer a simple API for common tasks. By specifying the task and an (optional) model, you can build a demo around an existing model with few lines of Python:\n\n```python\nimport gradio as gr\n\nfrom transformers import pipeline\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndef predict(text):\n return pipe(text)[0][\"translation_text\"]\n\ndemo = gr.Interface(\n fn=predict,\n inputs='text',\n outputs='text',\n)\n\ndemo.launch()\n```\n\nBut `gradio` actually makes it even easier to convert a `pipeline` to a demo, simply by using the `gradio.Interface.from_pipeline` methods, which skips the need to specify the input and output components:\n\n```python\nfrom transformers import pipeline\nimport gradio as gr\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndemo = gr.Interface.from_pipeline(pipe)\ndemo.launch()\n```\n\nThe previous code produces the following interface, which you can try right here in your browser:\n\n\n\n\n", "heading1": "Demos with the `Pipeline` in `transformers`", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "That's it! Let's recap the various ways Gradio and Hugging Face work together:\n\n1. You can build a demo around Inference Endpoints without having to load the model, by using `gr.load()`.\n2. You host your Gradio demo on Hugging Face Spaces, either using the GUI or entirely in Python.\n3. You can load demos from Hugging Face Spaces to remix and create new Gradio demos using `gr.load()`.\n4. You can convert a `transformers` pipeline into a Gradio demo using `from_pipeline()`.\n\n\ud83e\udd17\n", "heading1": "Recap", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Let\u2019s start with a simple example of integrating a C++ program into a Gradio app. Suppose we have the following C++ program that adds two numbers:\n\n```cpp\n// add.cpp\ninclude \n\nint main() {\n double a, b;\n std::cin >> a >> b;\n std::cout << a + b << std::endl;\n return 0;\n}\n```\n\nThis program reads two numbers from standard input, adds them, and outputs the result.\n\nWe can build a Gradio interface around this C++ program using Python's `subprocess` module. Here\u2019s the corresponding Python code:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef add_numbers(a, b):\n process = subprocess.Popen(\n ['./add'], \n stdin=subprocess.PIPE, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n output, error = process.communicate(input=f\"{a} {b}\\n\".encode())\n \n if error:\n return f\"Error: {error.decode()}\"\n return float(output.decode().strip())\n\ndemo = gr.Interface(\n fn=add_numbers, \n inputs=[gr.Number(label=\"Number 1\"), gr.Number(label=\"Number 2\")], \n outputs=gr.Textbox(label=\"Result\")\n)\n\ndemo.launch()\n```\n\nHere, `subprocess.Popen` is used to execute the compiled C++ program (`add`), pass the input values, and capture the output. You can compile the C++ program by running:\n\n```bash\ng++ -o add add.cpp\n```\n\nThis example shows how easy it is to call C++ from Python using `subprocess` and build a Gradio interface around it.\n\n", "heading1": "Using Gradio with C++", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Now, let\u2019s move to another example: calling a Rust program to apply a sepia filter to an image. The Rust code could look something like this:\n\n```rust\n// sepia.rs\nextern crate image;\n\nuse image::{GenericImageView, ImageBuffer, Rgba};\n\nfn sepia_filter(input: &str, output: &str) {\n let img = image::open(input).unwrap();\n let (width, height) = img.dimensions();\n let mut img_buf = ImageBuffer::new(width, height);\n\n for (x, y, pixel) in img.pixels() {\n let (r, g, b, a) = (pixel[0] as f32, pixel[1] as f32, pixel[2] as f32, pixel[3]);\n let tr = (0.393 * r + 0.769 * g + 0.189 * b).min(255.0);\n let tg = (0.349 * r + 0.686 * g + 0.168 * b).min(255.0);\n let tb = (0.272 * r + 0.534 * g + 0.131 * b).min(255.0);\n img_buf.put_pixel(x, y, Rgba([tr as u8, tg as u8, tb as u8, a]));\n }\n\n img_buf.save(output).unwrap();\n}\n\nfn main() {\n let args: Vec = std::env::args().collect();\n if args.len() != 3 {\n eprintln!(\"Usage: sepia \");\n return;\n }\n sepia_filter(&args[1], &args[2]);\n}\n```\n\nThis Rust program applies a sepia filter to an image. It takes two command-line arguments: the input image path and the output image path. You can compile this program using:\n\n```bash\ncargo build --release\n```\n\nNow, we can call this Rust program from Python and use Gradio to build the interface:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef apply_sepia(input_path):\n output_path = \"output.png\"\n \n process = subprocess.Popen(\n ['./target/release/sepia', input_path, output_path], \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n process.wait()\n \n return output_path\n\ndemo = gr.Interface(\n fn=apply_sepia, \n inputs=gr.Image(type=\"filepath\", label=\"Input Image\"), \n outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and re", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "nput Image\"), \n outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and returns the sepia-filtered output to Gradio.\n\nThis setup showcases how you can integrate performance-critical or specialized code written in Rust into a Gradio interface.\n\n", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Integrating Gradio with R is particularly straightforward thanks to the `reticulate` package, which allows you to run Python code directly in R. Let\u2019s walk through an example of using Gradio in R. \n\n**Installation**\n\nFirst, you need to install the `reticulate` package in R:\n\n```r\ninstall.packages(\"reticulate\")\n```\n\n\nOnce installed, you can use the package to run Gradio directly from within an R script.\n\n\n```r\nlibrary(reticulate)\n\npy_install(\"gradio\", pip = TRUE)\n\ngr <- import(\"gradio\") import gradio as gr\n```\n\n**Building a Gradio Application**\n\nWith gradio installed and imported, we now have access to gradio's app building methods. Let's build a simple app for an R function that returns a greeting\n\n```r\ngreeting <- \\(name) paste(\"Hello\", name)\n\napp <- gr$Interface(\n fn = greeting,\n inputs = gr$Text(label = \"Name\"),\n outputs = gr$Text(label = \"Greeting\"),\n title = \"Hello! &128515 &128075\"\n)\n\napp$launch(server_name = \"localhost\", \n server_port = as.integer(3000))\n```\n\nCredit to [@IfeanyiIdiaye](https://github.com/Ifeanyi55) for contributing this section. You can see more examples [here](https://github.com/Ifeanyi55/Gradio-in-R/tree/main/Code), including using Gradio Blocks to build a machine learning application in R.\n", "heading1": "Using Gradio with R (via `reticulate`)", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Building a dashboard from a public Google Sheet is very easy, thanks to the [`pandas` library](https://pandas.pydata.org/):\n\n1\\. Get the URL of the Google Sheets that you want to use. To do this, simply go to the Google Sheets, click on the \"Share\" button in the top-right corner, and then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Now, let's modify this URL and then use it to read the data from the Google Sheets into a Pandas DataFrame. (In the code below, replace the `URL` variable with the URL of your public Google Sheet):\n\n```python\nimport pandas as pd\n\nURL = \"https://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\"\ncsv_url = URL.replace('/editgid=', '/export?format=csv&gid=')\n\ndef get_data():\n return pd.read_csv(csv_url)\n```\n\n3\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) you would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n with gr.Row():\n with gr.Column():\n gr.DataFrame(get_data, every=gr.Timer(5))\n with gr.Column():\n gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch() Run the demo with queuing enabled\n```\n\nAnd that's it! You have a dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Public Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "For private Google Sheets, the process requires a little more work, but not that much! The key difference is that now, you must authenticate yourself to authorize access to the private Google Sheets.\n\nAuthentication\n\nTo authenticate yourself, obtain credentials from Google Cloud. Here's [how to set up google cloud credentials](https://developers.google.com/workspace/guides/create-credentials):\n\n1\\. First, log in to your Google Cloud account and go to the Google Cloud Console (https://console.cloud.google.com/)\n\n2\\. In the Cloud Console, click on the hamburger menu in the top-left corner and select \"APIs & Services\" from the menu. If you do not have an existing project, you will need to create one.\n\n3\\. Then, click the \"+ Enabled APIs & services\" button, which allows you to enable specific services for your project. Search for \"Google Sheets API\", click on it, and click the \"Enable\" button. If you see the \"Manage\" button, then Google Sheets is already enabled, and you're all set.\n\n4\\. In the APIs & Services menu, click on the \"Credentials\" tab and then click on the \"Create credentials\" button.\n\n5\\. In the \"Create credentials\" dialog, select \"Service account key\" as the type of credentials to create, and give it a name. **Note down the email of the service account**\n\n6\\. After selecting the service account, select the \"JSON\" key type and then click on the \"Create\" button. This will download the JSON key file containing your credentials to your computer. It will look something like this:\n\n```json\n{\n\t\"type\": \"service_account\",\n\t\"project_id\": \"your project\",\n\t\"private_key_id\": \"your private key id\",\n\t\"private_key\": \"private key\",\n\t\"client_email\": \"email\",\n\t\"client_id\": \"client id\",\n\t\"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n\t\"token_uri\": \"https://accounts.google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\nQuerying\n\nOnce you have the credentials `.json` file, you can use the following steps to query your Google Sheet:\n\n1\\. Click on the \"Share\" button in the top-right corner of the Google Sheet. Share the Google Sheets with the email address of the service from Step 5 of authentication subsection (this step is important!). Then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Install the [`gspread` library](https://docs.gspread.org/en/v5.7.0/), which makes it easy to work with the [Google Sheets API](https://developers.google.com/sheets/api/guides/concepts) in Python by running in the terminal: `pip install gspread`\n\n3\\. Write a function to load the data from the Google Sheet, like this (replace the `URL` variable with the URL of your private Google Sheet):\n\n```python\nimport gspread\nimport pandas as pd\n\nAuthenticate with Google and get the sheet\nURL = 'https://docs.google.com/spreadsheets/d/1_91Vps76SKOdDQ8cFxZQdgjTJiz23375sAT7vPvaj4k/editgid=0'\n\ngc = gspread.service_account(\"path/to/key.json\")\nsh = gc.open_by_url(URL)\nworksheet = sh.sheet1\n\ndef get_data():\n values = worksheet.get_all_values()\n df = pd.DataFrame(values[1:], columns=values[0])\n return df\n\n```\n\n4\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio cod", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": ". To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n with gr.Row():\n with gr.Column():\n gr.DataFrame(get_data, every=gr.Timer(5))\n with gr.Column():\n gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch() Run the demo with queuing enabled\n```\n\nYou now have a Dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "And that's all there is to it! With just a few lines of code, you can use `gradio` and other libraries to read data from a public or private Google Sheet and then display and plot the data in a real-time dashboard.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "This guide explains how you can use Gradio to plot geographical data on a map using the `gradio.Plot` component. The Gradio `Plot` component works with Matplotlib, Bokeh and Plotly. Plotly is what we will be working with in this guide. Plotly allows developers to easily create all sorts of maps with their geographical data. Take a look [here](https://plotly.com/python/maps/) for some examples.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will be using the New York City Airbnb dataset, which is hosted on kaggle [here](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data). I've uploaded it to the Hugging Face Hub as a dataset [here](https://huggingface.co/datasets/gradio/NYC-Airbnb-Open-Data) for easier use and download. Using this data we will plot Airbnb locations on a map output and allow filtering based on price and location. Below is the demo that we will be building. \u26a1\ufe0f\n\n$demo_map_airbnb\n\n", "heading1": "Overview", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Let's start by loading the Airbnb NYC data from the Hugging Face Hub.\n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\ndf = dataset.to_pandas()\n\ndef filter_map(min_price, max_price, boroughs):\n new_df = df[(df['neighbourhood_group'].isin(boroughs)) &\n (df['price'] > min_price) & (df['price'] < max_price)]\n names = new_df[\"name\"].tolist()\n prices = new_df[\"price\"].tolist()\n text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n```\n\nIn the code above, we first load the csv data into a pandas dataframe. Let's begin by defining a function that we will use as the prediction function for the gradio app. This function will accept the minimum price and maximum price range as well as the list of boroughs to filter the resulting map. We can use the passed in values (`min_price`, `max_price`, and list of `boroughs`) to filter the dataframe and create `new_df`. Next we will create `text_list` of the names and prices of each Airbnb to use as labels on the map.\n\n", "heading1": "Step 1 - Loading CSV data \ud83d\udcbe", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Plotly makes it easy to work with maps. Let's take a look below how we can create a map figure.\n\n```python\nimport plotly.graph_objects as go\n\nfig = go.Figure(go.Scattermapbox(\n customdata=text_list,\n lat=new_df['latitude'].tolist(),\n lon=new_df['longitude'].tolist(),\n mode='markers',\n marker=go.scattermapbox.Marker(\n size=6\n ),\n hoverinfo=\"text\",\n hovertemplate='Name: %{customdata[0]}
Price: $%{customdata[1]}'\n ))\n\nfig.update_layout(\n mapbox_style=\"open-street-map\",\n hovermode='closest',\n mapbox=dict(\n bearing=0,\n center=go.layout.mapbox.Center(\n lat=40.67,\n lon=-73.90\n ),\n pitch=0,\n zoom=9\n ),\n)\n```\n\nAbove, we create a scatter plot on mapbox by passing it our list of latitudes and longitudes to plot markers. We also pass in our custom data of names and prices for additional info to appear on every marker we hover over. Next we use `update_layout` to specify other map settings such as zoom, and centering.\n\nMore info [here](https://plotly.com/python/scattermapbox/) on scatter plots using Mapbox and Plotly.\n\n", "heading1": "Step 2 - Map Figure \ud83c\udf10", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will use two `gr.Number` components and a `gr.CheckboxGroup` to allow users of our app to specify price ranges and borough locations. We will then use the `gr.Plot` component as an output for our Plotly + Mapbox map we created earlier.\n\n```python\nwith gr.Blocks() as demo:\n with gr.Column():\n with gr.Row():\n min_price = gr.Number(value=250, label=\"Minimum Price\")\n max_price = gr.Number(value=1000, label=\"Maximum Price\")\n boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n btn = gr.Button(value=\"Update Filter\")\n map = gr.Plot()\n demo.load(filter_map, [min_price, max_price, boroughs], map)\n btn.click(filter_map, [min_price, max_price, boroughs], map)\n```\n\nWe layout these components using the `gr.Column` and `gr.Row` and we'll also add event triggers for when the demo first loads and when our \"Update Filter\" button is clicked in order to trigger the map to update with our new filters.\n\nThis is what the full demo code looks like:\n\n$code_map_airbnb\n\n", "heading1": "Step 3 - Gradio App \u26a1\ufe0f", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "If you run the code above, your app will start running locally.\nYou can even get a temporary shareable link by passing the `share=True` parameter to `launch`.\n\nBut what if you want to a permanent deployment solution?\nLet's deploy our Gradio app to the free HuggingFace Spaces platform.\n\nIf you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).\n\n", "heading1": "Step 4 - Deployment \ud83e\udd17", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "And you're all done! That's all the code you need to build a map demo.\n\nHere's a link to the demo [Map demo](https://huggingface.co/spaces/gradio/map_airbnb) and [complete code](https://huggingface.co/spaces/gradio/map_airbnb/blob/main/run.py) (on Hugging Face Spaces)\n", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Gradio features [blocks](https://www.gradio.app/docs/blocks) to easily layout applications. To use this feature, you need to stack or nest layout components and create a hierarchy with them. This isn't difficult to implement and maintain for small projects, but after the project gets more complex, this component hierarchy becomes difficult to maintain and reuse.\n\nIn this guide, we are going to explore how we can wrap the layout classes to create more maintainable and easy-to-read applications without sacrificing flexibility.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "We are going to follow the implementation from this Huggingface Space example:\n\n\n\n\n", "heading1": "Example", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "The wrapping utility has two important classes. The first one is the ```LayoutBase``` class and the other one is the ```Application``` class.\n\nWe are going to look at the ```render``` and ```attach_event``` functions of them for brevity. You can look at the full implementation from [the example code](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n\nSo let's start with the ```LayoutBase``` class.\n\nLayoutBase Class\n\n1. Render Function\n\n Let's look at the ```render``` function in the ```LayoutBase``` class:\n\n```python\nother LayoutBase implementations\n\ndef render(self) -> None:\n with self.main_layout:\n for renderable in self.renderables:\n renderable.render()\n\n self.main_layout.render()\n```\nThis is a little confusing at first but if you consider the default implementation you can understand it easily.\nLet's look at an example:\n\nIn the default implementation, this is what we're doing:\n\n```python\nwith Row():\n left_textbox = Textbox(value=\"left_textbox\")\n right_textbox = Textbox(value=\"right_textbox\")\n```\n\nNow, pay attention to the Textbox variables. These variables' ```render``` parameter is true by default. So as we use the ```with``` syntax and create these variables, they are calling the ```render``` function under the ```with``` syntax.\n\nWe know the render function is called in the constructor with the implementation from the ```gradio.blocks.Block``` class:\n\n```python\nclass Block:\n constructor parameters are omitted for brevity\n def __init__(self, ...):\n other assign functions \n\n if render:\n self.render()\n```\n\nSo our implementation looks like this:\n\n```python\nself.main_layout -> Row()\nwith self.main_layout:\n left_textbox.render()\n right_textbox.render()\n```\n\nWhat this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see ho", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "at this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see how the outer one works. For this, let's expand our example with the ```Tab``` component:\n\n```python\nwith Tab():\n with Row():\n first_textbox = Textbox(value=\"first_textbox\")\n second_textbox = Textbox(value=\"second_textbox\")\n```\n\nPay attention to the Row and Tab components this time. We have created the Textbox variables above and added them to Row with the ```with``` syntax. Now we need to add the Row component to the Tab component. You can see that the Row component is created with default parameters, so its render parameter is true, that's why the render function is going to be executed under the Tab component's ```with``` syntax.\n\nTo mimic this implementation, we need to call the ```render``` function of the ```main_layout``` variable after the ```with``` syntax of the ```main_layout``` variable.\n\nSo the implementation looks like this:\n\n```python\nwith tab_main_layout:\n with row_main_layout:\n first_textbox.render()\n second_textbox.render()\n\n row_main_layout.render()\n\ntab_main_layout.render()\n```\n\nThe default implementation and our implementation are the same, but we are using the render function ourselves. So it requires a little work.\n\nNow, let's take a look at the ```attach_event``` function.\n\n2. Attach Event Function\n\n The function is left as not implemented because it is specific to the class, so each class has to implement its `attach_event` function.\n\n```python\n other LayoutBase implementations\n\n def attach_event(self, block_dict: Dict[str, Block]) -> None:\n raise NotImplementedError\n```\n\nCheck out the ```block_dict``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n other Application implementations\n\n def _render(self):\n ", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "ct``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n other Application implementations\n\n def _render(self):\n with self.app:\n for child in self.children:\n child.render()\n\n self.app.render()\n```\n\nFrom the explanation of the ```LayoutBase``` class's ```render``` function, we can understand the ```child.render``` part.\n\nSo let's look at the bottom part, why are we calling the ```app``` variable's ```render``` function? It's important to call this function because if we look at the implementation in the ```gradio.blocks.Blocks``` class, we can see that it is adding the components and event functions into the root component. To put it another way, it is creating and structuring the gradio application.\n\n2. Attach Event Function\n\n Let's see how we can attach events to components:\n\n```python\n other Application implementations\n\n def _attach_event(self):\n block_dict: Dict[str, Block] = {}\n\n for child in self.children:\n block_dict.update(child.global_children_dict)\n\n with self.app:\n for child in self.children:\n try:\n child.attach_event(block_dict=block_dict)\n except NotImplementedError:\n print(f\"{child.name}'s attach_event is not implemented\")\n```\n\nYou can see why the ```global_children_list``` is used in the ```LayoutBase``` class from the example code. With this, all the components in the application are gathered into one dictionary, so the component can access all the components with their names.\n\nThe ```with``` syntax is used here again to attach events to components. If we look at the ```__exit__``` function in the ```gradio.blocks.Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```_", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```__exit__``` function.\n\nOf course, we can call ```attach_load_events``` without using the ```with``` syntax, but the function needs a ```Context.root_block```, and it is set in the ```__enter__``` function. So we used the ```with``` syntax here rather than calling the function ourselves.\n\n", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "In this guide, we saw\n\n- How we can wrap the layouts\n- How components are rendered\n- How we can structure our application with wrapped layout classes\n\nBecause the classes used in this guide are used for demonstration purposes, they may still not be totally optimized or modular. But that would make the guide much longer!\n\nI hope this guide helps you gain another view of the layout classes and gives you an idea about how you can use them for your needs. See the full implementation of our example [here](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "When you demo a machine learning model, you might want to collect data from users who try the model, particularly data points in which the model is not behaving as expected. Capturing these \"hard\" data points is valuable because it allows you to improve your machine learning model and make it more reliable and robust.\n\nGradio simplifies the collection of this data by including a **Flag** button with every `Interface`. This allows a user or tester to easily send data back to the machine where the demo is running. In this Guide, we discuss more about how to use the flagging feature, both with `gradio.Interface` as well as with `gradio.Blocks`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Flagging with Gradio's `Interface` is especially easy. By default, underneath the output components, there is a button marked **Flag**. When a user testing your model sees input with interesting output, they can click the flag button to send the input and output data back to the machine where the demo is running. The sample is saved to a CSV log file (by default). If the demo involves images, audio, video, or other types of files, these are saved separately in a parallel directory and the paths to these files are saved in the CSV file.\n\nThere are [four parameters](https://gradio.app/docs/interfaceinitialization) in `gradio.Interface` that control how flagging works. We will go over them in greater detail.\n\n- `flagging_mode`: this parameter can be set to either `\"manual\"` (default), `\"auto\"`, or `\"never\"`.\n - `manual`: users will see a button to flag, and samples are only flagged when the button is clicked.\n - `auto`: users will not see a button to flag, but every sample will be flagged automatically.\n - `never`: users will not see a button to flag, and no sample will be flagged.\n- `flagging_options`: this parameter can be either `None` (default) or a list of strings.\n - If `None`, then the user simply clicks on the **Flag** button and no additional options are shown.\n - If a list of strings are provided, then the user sees several buttons, corresponding to each of the strings that are provided. For example, if the value of this parameter is `[\"Incorrect\", \"Ambiguous\"]`, then buttons labeled **Flag as Incorrect** and **Flag as Ambiguous** appear. This only applies if `flagging_mode` is `\"manual\"`.\n - The chosen option is then logged along with the input and output.\n- `flagging_dir`: this parameter takes a string.\n - It represents what to name the directory where flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n - Using this parameter allows you to write custom code that gets run whe", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n - Using this parameter allows you to write custom code that gets run when the flag button is clicked\n - By default, this is set to an instance of `gr.JSONLogger`\n\n", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Within the directory provided by the `flagging_dir` argument, a JSON file will log the flagged data.\n\nHere's an example: The code below creates the calculator interface embedded below it:\n\n```python\nimport gradio as gr\n\n\ndef calculator(num1, operation, num2):\n if operation == \"add\":\n return num1 + num2\n elif operation == \"subtract\":\n return num1 - num2\n elif operation == \"multiply\":\n return num1 * num2\n elif operation == \"divide\":\n return num1 / num2\n\n\niface = gr.Interface(\n calculator,\n [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n \"number\",\n flagging_mode=\"manual\"\n)\n\niface.launch()\n```\n\n\n\nWhen you click the flag button above, the directory where the interface was launched will include a new flagged subfolder, with a csv file inside it. This csv file includes all the data that was flagged.\n\n```directory\n+-- flagged/\n| +-- logs.csv\n```\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,timestamp\n5,add,7,12,2022-01-31 11:40:51.093412\n6,subtract,1.5,4.5,2022-01-31 03:25:32.023542\n```\n\nIf the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well. For example an `image` input to `image` output interface will create the following structure.\n\n```directory\n+-- flagged/\n| +-- logs.csv\n| +-- image/\n| | +-- 0.png\n| | +-- 1.png\n| +-- Output/\n| | +-- 0.png\n| | +-- 1.png\n```\n\n_flagged/logs.csv_\n\n```csv\nim,Output timestamp\nim/0.png,Output/0.png,2022-02-04 19:49:58.026963\nim/1.png,Output/1.png,2022-02-02 10:40:51.093412\n```\n\nIf you wish for the user to provide a reason for flagging, you can pass a list of strings to the `flagging_options` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the fo", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the following code will create the interface embedded below it.\n\n```python\niface = gr.Interface(\n calculator,\n [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n \"number\",\n flagging_mode=\"manual\",\n flagging_options=[\"wrong sign\", \"off by one\", \"other\"]\n)\n\niface.launch()\n```\n\n\n\nWhen users click the flag button, the csv file will now include a column indicating the selected option.\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,flag,timestamp\n5,add,7,-12,wrong sign,2022-02-04 11:40:51.093412\n6,subtract,1.5,3.5,off by one,2022-02-04 11:42:32.062512\n```\n\n", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "What about if you are using `gradio.Blocks`? On one hand, you have even more flexibility\nwith Blocks -- you can write whatever Python code you want to run when a button is clicked,\nand assign that using the built-in events in Blocks.\n\nAt the same time, you might want to use an existing `FlaggingCallback` to avoid writing extra code.\nThis requires two steps:\n\n1. You have to run your callback's `.setup()` somewhere in the code prior to the\n first time you flag data\n2. When the flagging button is clicked, then you trigger the callback's `.flag()` method,\n making sure to collect the arguments correctly and disabling the typical preprocessing.\n\nHere is an example with an image sepia filter Blocks demo that lets you flag\ndata using the default `CSVLogger`:\n\n$code_blocks_flag\n$demo_blocks_flag\n\n", "heading1": "Flagging with Blocks", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Important Note: please make sure your users understand when the data they submit is being saved, and what you plan on doing with it. This is especially important when you use `flagging_mode=auto` (when all of the data submitted through the demo is being flagged)\n\nThat's all! Happy building :)\n", "heading1": "Privacy", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "In this guide we will demonstrate some of the ways you can use Gradio with Comet. We will cover the basics of using Comet with Gradio and show you some of the ways that you can leverage Gradio's advanced features such as [Embedding with iFrames](https://www.gradio.app/guides/sharing-your-app/embedding-with-iframes) and [State](https://www.gradio.app/docs/state) to build some amazing model evaluation workflows.\n\nHere is a list of the topics covered in this guide.\n\n1. Logging Gradio UI's to your Comet Experiments\n2. Embedding Gradio Applications directly into your Comet Projects\n3. Embedding Hugging Face Spaces directly into your Comet Projects\n4. Logging Model Inferences from your Gradio Application to Comet\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[Comet](https://www.comet.com?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) is an MLOps Platform that is designed to help Data Scientists and Teams build better models faster! Comet provides tooling to Track, Explain, Manage, and Monitor your models in a single place! It works with Jupyter Notebooks and Scripts and most importantly it's 100% free!\n\n", "heading1": "What is Comet?", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "First, install the dependencies needed to run these examples\n\n```shell\npip install comet_ml torch torchvision transformers gradio shap requests Pillow\n```\n\nNext, you will need to [sign up for a Comet Account](https://www.comet.com/signup?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs). Once you have your account set up, [grab your API Key](https://www.comet.com/docs/v2/guides/getting-started/quickstart/get-an-api-key?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) and configure your Comet credentials\n\nIf you're running these examples as a script, you can either export your credentials as environment variables\n\n```shell\nexport COMET_API_KEY=\"\"\nexport COMET_WORKSPACE=\"\"\nexport COMET_PROJECT_NAME=\"\"\n```\n\nor set them in a `.comet.config` file in your working directory. You file should be formatted in the following way.\n\n```shell\n[comet]\napi_key=\nworkspace=\nproject_name=\n```\n\nIf you are using the provided Colab Notebooks to run these examples, please run the cell with the following snippet before starting the Gradio UI. Running this cell allows you to interactively add your API key to the notebook.\n\n```python\nimport comet_ml\ncomet_ml.init()\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Gradio_and_Comet.ipynb)\n\nIn this example, we will go over how to log your Gradio Applications to Comet and interact with them using the Gradio Custom Panel.\n\nLet's start by building a simple Image Classification example using `resnet18`.\n\n```python\nimport comet_ml\n\nimport requests\nimport torch\nfrom PIL import Image\nfrom torchvision import transforms\n\ntorch.hub.download_url_to_file(\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\n\nif torch.cuda.is_available():\n device = \"cuda\"\nelse:\n device = \"cpu\"\n\nmodel = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\nmodel = model.to(device)\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\n\ndef predict(inp):\n inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n inp = transforms.ToTensor()(inp).unsqueeze(0)\n with torch.no_grad():\n prediction = torch.nn.functional.softmax(model(inp.to(device))[0], dim=0)\n return {labels[i]: float(prediction[i]) for i in range(1000)}\n\n\ninputs = gr.Image()\noutputs = gr.Label(num_top_classes=3)\n\nio = gr.Interface(\n fn=predict, inputs=inputs, outputs=outputs, examples=[\"dog.jpg\"]\n)\nio.launch(inline=False, share=True)\n\nexperiment = comet_ml.Experiment()\nexperiment.add_tag(\"image-classifier\")\n\nio.integrate(comet_ml=experiment)\n```\n\nThe last line in this snippet will log the URL of the Gradio Application to your Comet Experiment. You can find the URL in the Text Tab of your Experiment.\n\n\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n\n\n", "heading1": "1. Logging Gradio UI's to your Comet Experiments", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "\n\nIf you are permanently hosting your Gradio application, you can embed the UI using the Gradio Panel Extended custom Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page.\n\n\"adding-panels\"\n\nNext, search for Gradio Panel Extended in the Public Panels section and click `Add`.\n\n\"gradio-panel-extended\"\n\nOnce you have added your Panel, click `Edit` to access to the Panel Options page and paste in the URL of your Gradio application.\n\n![Edit-Gradio-Panel-Options](https://user-images.githubusercontent.com/7529846/214573001-23814b5a-ca65-4ace-a8a5-b27cdda70f7a.gif)\n\n\"Edit-Gradio-Panel-URL\"\n\n", "heading1": "2. Embedding Gradio Applications directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "\n\nYou can also embed Gradio Applications that are hosted on Hugging Faces Spaces into your Comet Projects using the Hugging Face Spaces Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page. Next, search for the Hugging Face Spaces Panel in the Public Panels section and click `Add`.\n\n\"huggingface-spaces-panel\"\n\nOnce you have added your Panel, click Edit to access to the Panel Options page and paste in the path of your Hugging Face Space e.g. `pytorch/ResNet`\n\n\"Edit-HF-Space\"\n\n", "heading1": "3. Embedding Hugging Face Spaces directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Logging_Model_Inferences_with_Comet_and_Gradio.ipynb)\n\nIn the previous examples, we demonstrated the various ways in which you can interact with a Gradio application through the Comet UI. Additionally, you can also log model inferences, such as SHAP plots, from your Gradio application to Comet.\n\nIn the following snippet, we're going to log inferences from a Text Generation model. We can persist an Experiment across multiple inference calls using Gradio's [State](https://www.gradio.app/docs/state) object. This will allow you to log multiple inferences from a model to a single Experiment.\n\n```python\nimport comet_ml\nimport gradio as gr\nimport shap\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nif torch.cuda.is_available():\n device = \"cuda\"\nelse:\n device = \"cpu\"\n\nMODEL_NAME = \"gpt2\"\n\nmodel = AutoModelForCausalLM.from_pretrained(MODEL_NAME)\n\nset model decoder to true\nmodel.config.is_decoder = True\nset text-generation params under task_specific_params\nmodel.config.task_specific_params[\"text-generation\"] = {\n \"do_sample\": True,\n \"max_length\": 50,\n \"temperature\": 0.7,\n \"top_k\": 50,\n \"no_repeat_ngram_size\": 2,\n}\nmodel = model.to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\nexplainer = shap.Explainer(model, tokenizer)\n\n\ndef start_experiment():\n \"\"\"Returns an APIExperiment object that is thread safe\n and can be used to log inferences to a single Experiment\n \"\"\"\n try:\n api = comet_ml.API()\n workspace = api.get_default_", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": " \"\"\"Returns an APIExperiment object that is thread safe\n and can be used to log inferences to a single Experiment\n \"\"\"\n try:\n api = comet_ml.API()\n workspace = api.get_default_workspace()\n project_name = comet_ml.config.get_config()[\"comet.project_name\"]\n\n experiment = comet_ml.APIExperiment(\n workspace=workspace, project_name=project_name\n )\n experiment.log_other(\"Created from\", \"gradio-inference\")\n\n message = f\"Started Experiment: [{experiment.name}]({experiment.url})\"\n\n return (experiment, message)\n\n except Exception as e:\n return None, None\n\n\ndef predict(text, state, message):\n experiment = state\n\n shap_values = explainer([text])\n plot = shap.plots.text(shap_values, display=False)\n\n if experiment is not None:\n experiment.log_other(\"message\", message)\n experiment.log_html(plot)\n\n return plot\n\n\nwith gr.Blocks() as demo:\n start_experiment_btn = gr.Button(\"Start New Experiment\")\n experiment_status = gr.Markdown()\n\n Log a message to the Experiment to provide more context\n experiment_message = gr.Textbox(label=\"Experiment Message\")\n experiment = gr.State()\n\n input_text = gr.Textbox(label=\"Input Text\", lines=5, interactive=True)\n submit_btn = gr.Button(\"Submit\")\n\n output = gr.HTML(interactive=True)\n\n start_experiment_btn.click(\n start_experiment, outputs=[experiment, experiment_status]\n )\n submit_btn.click(\n predict, inputs=[input_text, experiment, experiment_message], outputs=[output]\n )\n```\n\nInferences from this snippet will be saved in the HTML tab of your experiment.\n\n\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "887c-065aca14dd30.mp4\">\n\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "We hope you found this guide useful and that it provides some inspiration to help you build awesome model evaluation workflows with Comet and Gradio.\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- Create an account on Hugging Face [here](https://huggingface.co/join).\n- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.\n- Request to join the Comet organization [here](https://huggingface.co/Comet).\n\n", "heading1": "How to contribute Gradio demos on HF spaces on the Comet organization", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- [Comet Documentation](https://www.comet.com/docs/v2/?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs)\n", "heading1": "Additional Resources", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "By default, every Gradio demo includes a built-in queuing system that scales to thousands of requests. When a user of your app submits a request (i.e. submits an input to your function), Gradio adds the request to the queue, and requests are processed in order, generally speaking (this is not exactly true, as discussed below). When the user's request has finished processing, the Gradio server returns the result back to the user using server-side events (SSE). The SSE protocol has several advantages over simply using HTTP POST requests: \n\n(1) They do not time out -- most browsers raise a timeout error if they do not get a response to a POST request after a short period of time (e.g. 1 min). This can be a problem if your inference function takes longer than 1 minute to run or if many people are trying out your demo at the same time, resulting in increased latency.\n\n(2) They allow the server to send multiple updates to the frontend. This means, for example, that the server can send a real-time ETA of how long your prediction will take to complete.\n\nTo configure the queue, simply call the `.queue()` method before launching an `Interface`, `TabbedInterface`, `ChatInterface` or any `Blocks`. Here's an example:\n\n```py\nimport gradio as gr\n\napp = gr.Interface(lambda x:x, \"image\", \"image\")\napp.queue() <-- Sets up a queue with default parameters\napp.launch()\n```\n\n**How Requests are Processed from the Queue**\n\nWhen a Gradio server is launched, a pool of threads is used to execute requests from the queue. By default, the maximum size of this thread pool is `40` (which is the default inherited from FastAPI, on which the Gradio server is based). However, this does *not* mean that 40 requests are always processed in parallel from the queue. \n\nInstead, Gradio uses a **single-function-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do not see, for example, out-of-memory errors, due to multiple workers calling a machine learning model at the same time. Suppose you have 3 functions in your Gradio app: A, B, and C. And you see the following sequence of 7 requests come in from users using your app:\n\n```\n1 2 3 4 5 6 7\n-------------\nA B A A C B A\n```\n\nInitially, 3 workers will get dispatched to handle requests 1, 2, and 5 (corresponding to functions: A, B, C). As soon as any of these workers finish, they will start processing the next function in the queue of the same function type, e.g. the worker that finished processing request 1 will start processing request 3, and so on.\n\nIf you want to change this behavior, there are several parameters that can be used to configure the queue and help reduce latency. Let's go through them one-by-one.\n\n\nThe `default_concurrency_limit` parameter in `queue()`\n\nThe first parameter we will explore is the `default_concurrency_limit` parameter in `queue()`. This controls how many workers can execute the same event. By default, this is set to `1`, but you can set it to a higher integer: `2`, `10`, or even `None` (in the last case, there is no limit besides the total number of available workers). \n\nThis is useful, for example, if your Gradio app does not call any resource-intensive functions. If your app only queries external APIs, then you can set the `default_concurrency_limit` much higher. Increasing this parameter can **linearly multiply the capacity of your server to handle requests**.\n\nSo why not set this parameter much higher all the time? Keep in mind that since requests are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrenc", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "sts are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrency_limit` too high. You may also start to get diminishing returns if the `default_concurrency_limit` is too high because of costs of switching between different worker threads.\n\n**Recommendation**: Increase the `default_concurrency_limit` parameter as high as you can while you continue to see performance gains or until you hit memory limits on your machine. You can [read about Hugging Face Spaces machine specs here](https://huggingface.co/docs/hub/spaces-overview).\n\n\nThe `concurrency_limit` parameter in events\n\nYou can also set the number of requests that can be processed in parallel for each event individually. These take priority over the `default_concurrency_limit` parameter described previously.\n\nTo do this, set the `concurrency_limit` parameter of any event listener, e.g. `btn.click(..., concurrency_limit=20)` or in the `Interface` or `ChatInterface` classes: e.g. `gr.Interface(..., concurrency_limit=20)`. By default, this parameter is set to the global `default_concurrency_limit`.\n\n\nThe `max_threads` parameter in `launch()`\n\nIf your demo uses non-async functions, e.g. `def` instead of `async def`, they will be run in a threadpool. This threadpool has a size of 40 meaning that only 40 threads can be created to run your non-async functions. If you are running into this limit, you can increase the threadpool size with `max_threads`. The default value is 40.\n\nTip: You should use async functions whenever possible to increase the number of concurrent requests your app can handle. Quick functions that are not CPU-bound are good candidates to be written as `async`. This [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many pe", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "is [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many people from joining the queue in the first place. You can set the maximum number of requests that the queue processes using the `max_size` parameter of `queue()`. If a request arrives when the queue is already of the maximum size, it will not be allowed to join the queue and instead, the user will receive an error saying that the queue is full and to try again. By default, `max_size=None`, meaning that there is no limit to the number of users that can join the queue.\n\nParadoxically, setting a `max_size` can often improve user experience because it prevents users from being dissuaded by very long queue wait times. Users who are more interested and invested in your demo will keep trying to join the queue, and will be able to get their results faster.\n\n**Recommendation**: For a better user experience, set a `max_size` that is reasonable given your expectations of how long users might be willing to wait for a prediction.\n\nThe `max_batch_size` parameter in events\n\nAnother way to increase the parallelism of your Gradio demo is to write your function so that it can accept **batches** of inputs. Most deep learning models can process batches of samples more efficiently than processing individual samples.\n\nIf you write your function to process a batch of samples, Gradio will automatically batch incoming requests together and pass them into your function as a batch of samples. You need to set `batch` to `True` (by default it is `False`) and set a `max_batch_size` (by default it is `4`) based on the maximum number of samples your function is able to handle. These two parameters can be passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than set", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "e passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than setting `default_concurrency_limit` for deep learning models. The downside is that you might need to adapt your function a little bit to accept batches of samples instead of individual samples.\n\nHere's an example of a function that does _not_ accept a batch of inputs -- it processes a single input at a time:\n\n```py\nimport time\n\ndef trim_words(word, length):\n return word[:int(length)]\n\n```\n\nHere's the same function rewritten to take in a batch of samples:\n\n```py\nimport time\n\ndef trim_words(words, lengths):\n trimmed_words = []\n for w, l in zip(words, lengths):\n trimmed_words.append(w[:int(l)])\n return [trimmed_words]\n\n```\n\nThe second function can be used with `batch=True` and an appropriate `max_batch_size` parameter.\n\n**Recommendation**: If possible, write your function to accept batches of samples, and then set `batch` to `True` and the `max_batch_size` as high as possible based on your machine's memory limits.\n\n", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "If you have done everything above, and your demo is still not fast enough, you can upgrade the hardware that your model is running on. Changing the model from running on CPUs to running on GPUs will usually provide a 10x-50x increase in inference time for deep learning models.\n\nIt is particularly straightforward to upgrade your Hardware on Hugging Face Spaces. Simply click on the \"Settings\" tab in your Space and choose the Space Hardware you'd like.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/spaces-gpu-settings.png)\n\nWhile you might need to adapt portions of your machine learning inference code to run on a GPU (here's a [handy guide](https://cnvrg.io/pytorch-cuda/) if you are using PyTorch), Gradio is completely agnostic to the choice of hardware and will work completely fine if you use it with CPUs, GPUs, TPUs, or any other hardware!\n\nNote: your GPU memory is different than your CPU memory, so if you upgrade your hardware,\nyou might need to adjust the value of the `default_concurrency_limit` parameter described above.\n\n", "heading1": "Upgrading your Hardware (GPUs, TPUs, etc.)", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "Congratulations! You know how to set up a Gradio demo for maximum performance. Good luck on your next viral demo!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "In this Guide, we'll walk you through:\n\n- Introduction of Gradio, and Hugging Face Spaces, and Wandb\n- How to setup a Gradio demo using the Wandb integration for JoJoGAN\n- How to contribute your own Gradio demos after tracking your experiments on wandb to the Wandb organization on Hugging Face\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Weights and Biases (W&B) allows data scientists and machine learning scientists to track their machine learning experiments at every stage, from training to production. Any metric can be aggregated over samples and shown in panels in a customizable and searchable dashboard, like below:\n\n\"Screen\n\n", "heading1": "What is Wandb?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Gradio\n\nGradio lets users demo their machine learning models as a web app, all in a few lines of Python. Gradio wraps any Python function (such as a machine learning model's inference function) into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.\n\nGet started [here](https://gradio.app/getting_started)\n\nHugging Face Spaces\n\nHugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces [here](https://huggingface.co/spaces/launch).\n\n", "heading1": "What are Hugging Face Spaces & Gradio?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Now, let's walk you through how to do this on your own. We'll make the assumption that you're new to W&B and Gradio for the purposes of this tutorial.\n\nLet's get started!\n\n1. Create a W&B account\n\n Follow [these quick instructions](https://app.wandb.ai/login) to create your free account if you don\u2019t have one already. It shouldn't take more than a couple minutes. Once you're done (or if you've already got an account), next, we'll run a quick colab.\n\n2. Open Colab Install Gradio and W&B\n\n We'll be following along with the colab provided in the JoJoGAN repo with some minor modifications to use Wandb and Gradio more effectively.\n\n [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/JoJoGAN/blob/main/stylize.ipynb)\n\n Install Gradio and Wandb at the top:\n\n ```sh\n pip install gradio wandb\n ```\n\n3. Finetune StyleGAN and W&B experiment tracking\n\n This next step will open a W&B dashboard to track your experiments and a gradio panel showing pretrained models to choose from a drop down menu from a Gradio Demo hosted on Huggingface Spaces. Here's the code you need for that:\n\n ```python\n alpha = 1.0\n alpha = 1-alpha\n\n preserve_color = True\n num_iter = 100\n log_interval = 50\n\n samples = []\n column_names = [\"Reference (y)\", \"Style Code(w)\", \"Real Face Image(x)\"]\n\n wandb.init(project=\"JoJoGAN\")\n config = wandb.config\n config.num_iter = num_iter\n config.preserve_color = preserve_color\n wandb.log(\n {\"Style reference\": [wandb.Image(transforms.ToPILImage()(target_im))]},\n step=0)\n\n load discriminator for perceptual loss\n discriminator = Discriminator(1024, 2).eval().to(device)\n ckpt = torch.load('models/stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage)\n discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n reset generator\n del generator\n generator = deepcopy(original_generator)\n\n g_optim = optim.Adam(generator.parameters(),", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ": storage)\n discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n reset generator\n del generator\n generator = deepcopy(original_generator)\n\n g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))\n\n Which layers to swap for generating a family of plausible real images -> fake image\n if preserve_color:\n id_swap = [9,11,15,16,17]\n else:\n id_swap = list(range(7, generator.n_latent))\n\n for idx in tqdm(range(num_iter)):\n mean_w = generator.get_latent(torch.randn([latents.size(0), latent_dim]).to(device)).unsqueeze(1).repeat(1, generator.n_latent, 1)\n in_latent = latents.clone()\n in_latent[:, id_swap] = alpha*latents[:, id_swap] + (1-alpha)*mean_w[:, id_swap]\n\n img = generator(in_latent, input_is_latent=True)\n\n with torch.no_grad():\n real_feat = discriminator(targets)\n fake_feat = discriminator(img)\n\n loss = sum([F.l1_loss(a, b) for a, b in zip(fake_feat, real_feat)])/len(fake_feat)\n\n wandb.log({\"loss\": loss}, step=idx)\n if idx % log_interval == 0:\n generator.eval()\n my_sample = generator(my_w, input_is_latent=True)\n generator.train()\n my_sample = transforms.ToPILImage()(utils.make_grid(my_sample, normalize=True, range=(-1, 1)))\n wandb.log(\n {\"Current stylization\": [wandb.Image(my_sample)]},\n step=idx)\n table_data = [\n wandb.Image(transforms.ToPILImage()(target_im)),\n wandb.Image(img),\n wandb.Image(my_sample),\n ]\n samples.append(table_data)\n\n g_optim.zero_grad()\n loss.backward()\n g_optim.step()\n\n out_table = wandb.Table(data=samples, columns=column_names)\n wandb.log({\"Current Samples\": out_table})\n ```\n4. Save, Download, and Load Model\n\n Here's how to save and download your model.\n\n ```python\n from PIL import Image\n import torch\n torch.backends.cudnn.benchmark = True\n from torchvision impor", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "ave, Download, and Load Model\n\n Here's how to save and download your model.\n\n ```python\n from PIL import Image\n import torch\n torch.backends.cudnn.benchmark = True\n from torchvision import transforms, utils\n from util import *\n import math\n import random\n import numpy as np\n from torch import nn, autograd, optim\n from torch.nn import functional as F\n from tqdm import tqdm\n import lpips\n from model import *\n from e4e_projection import projection as e4e_projection\n \n from copy import deepcopy\n import imageio\n \n import os\n import sys\n import torchvision.transforms as transforms\n from argparse import Namespace\n from e4e.models.psp import pSp\n from util import *\n from huggingface_hub import hf_hub_download\n from google.colab import files\n \n torch.save({\"g\": generator.state_dict()}, \"your-model-name.pt\")\n \n files.download('your-model-name.pt')\n \n latent_dim = 512\n device=\"cuda\"\n model_path_s = hf_hub_download(repo_id=\"akhaliq/jojogan-stylegan2-ffhq-config-f\", filename=\"stylegan2-ffhq-config-f.pt\")\n original_generator = Generator(1024, latent_dim, 8, 2).to(device)\n ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)\n original_generator.load_state_dict(ckpt[\"g_ema\"], strict=False)\n mean_latent = original_generator.mean_latent(10000)\n \n generator = deepcopy(original_generator)\n \n ckpt = torch.load(\"/content/JoJoGAN/your-model-name.pt\", map_location=lambda storage, loc: storage)\n generator.load_state_dict(ckpt[\"g\"], strict=False)\n generator.eval()\n \n plt.rcParams['figure.dpi'] = 150\n \n transform = transforms.Compose(\n [\n transforms.Resize((1024, 1024)),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]\n )\n \n def inference(img):\n img.save('out.jpg')\n aligned_face = align_face('out.jpg')\n \n my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ".5, 0.5)),\n ]\n )\n \n def inference(img):\n img.save('out.jpg')\n aligned_face = align_face('out.jpg')\n \n my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)\n with torch.no_grad():\n my_sample = generator(my_w, input_is_latent=True)\n \n npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()\n imageio.imwrite('filename.jpeg', npimage)\n return 'filename.jpeg'\n ````\n\n5. Build a Gradio Demo\n\n ```python\n import gradio as gr\n \n title = \"JoJoGAN\"\n description = \"Gradio Demo for JoJoGAN: One Shot Face Stylization. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\"\n \n demo = gr.Interface(\n inference,\n gr.Image(type=\"pil\"),\n gr.Image(type=\"file\"),\n title=title,\n description=description\n )\n \n demo.launch(share=True)\n ```\n\n6. Integrate Gradio into your W&B Dashboard\n\n The last step\u2014integrating your Gradio demo with your W&B dashboard\u2014is just one extra line:\n\n ```python\n demo.integrate(wandb=wandb)\n ```\n\n Once you call integrate, a demo will be created and you can integrate it into your dashboard or report.\n\n Outside of W&B with Web components, using the `gradio-app` tags, anyone can embed Gradio demos on HF spaces directly into their blogs, websites, documentation, etc.:\n \n ```html\n \n ```\n\n7. (Optional) Embed W&B plots in your Gradio App\n\n It's also possible to embed W&B plots within Gradio apps. To do so, you can create a W&B Report of your plots and\n embed them within your Gradio app within a `gr.HTML` block.\n\n The Report will need to be public and you will need to wrap the URL within an iFrame like this:\n\n ```python\n import gradio as gr\n \n def wandb_report(url):\n iframe = f'\n```\n\nAgain, you can find the `src=` attribute to your Space's embed URL, which you can find in the \"Embed this Space\" button.\n\nNote: if you use IFrames, you'll probably want to add a fixed `height` attribute and set `style=\"border:0;\"` to remove the border. In addition, if your app requires permissions such as access to the webcam or the microphone, you'll need to provide that as well using the `allow` attribute.\n\n", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "You can use almost any Gradio app as an API! In the footer of a Gradio app [like this one](https://huggingface.co/spaces/gradio/hello_world), you'll see a \"Use via API\" link.\n\n![Use via API](https://github.com/gradio-app/gradio/blob/main/guides/assets/use_via_api.png?raw=true)\n\nThis is a page that lists the endpoints that can be used to query the Gradio app, via our supported clients: either [the Python client](https://gradio.app/guides/getting-started-with-the-python-client/), or [the JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). For each endpoint, Gradio automatically generates the parameters and their types, as well as example inputs, like this.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe endpoints are automatically created when you launch a Gradio application. If you are using Gradio `Blocks`, you can also name each event listener, such as\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\nThis will add and document the endpoint `/addition/` to the automatically generated API page. Read more about the [API page here](./view-api-page).\n\n", "heading1": "API Page", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "When a user makes a prediction to your app, you may need the underlying network request, in order to get the request headers (e.g. for advanced authentication), log the client's IP address, getting the query parameters, or for other reasons. Gradio supports this in a similar manner to FastAPI: simply add a function parameter whose type hint is `gr.Request` and Gradio will pass in the network request as that parameter. Here is an example:\n\n```python\nimport gradio as gr\n\ndef echo(text, request: gr.Request):\n if request:\n print(\"Request headers dictionary:\", request.headers)\n print(\"IP address:\", request.client.host)\n print(\"Query parameters:\", dict(request.query_params))\n return text\n\nio = gr.Interface(echo, \"textbox\", \"textbox\").launch()\n```\n\nNote: if your function is called directly instead of through the UI (this happens, for\nexample, when examples are cached, or when the Gradio app is called via API), then `request` will be `None`.\nYou should handle this case explicitly to ensure that your app does not throw any errors. That is why\nwe have the explicit check `if request`.\n\n", "heading1": "Accessing the Network Request Directly", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "In some cases, you might have an existing FastAPI app, and you'd like to add a path for a Gradio demo.\nYou can easily do this with `gradio.mount_gradio_app()`.\n\nHere's a complete example:\n\n$code_custom_path\n\nNote that this approach also allows you run your Gradio apps on custom paths (`http://localhost:8000/gradio` in the example above).\n\n\n", "heading1": "Mounting Within Another FastAPI App", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Password-protected app\n\nYou may wish to put an authentication page in front of your app to limit who can open your app. With the `auth=` keyword argument in the `launch()` method, you can provide a tuple with a username and password, or a list of acceptable username/password tuples; Here's an example that provides password-based authentication for a single user named \"admin\":\n\n```python\ndemo.launch(auth=(\"admin\", \"pass1234\"))\n```\n\nFor more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns `True` to allow access, `False` otherwise.\n\nHere's an example of a function that accepts any login where the username and password are the same:\n\n```python\ndef same_auth(username, password):\n return username == password\ndemo.launch(auth=same_auth)\n```\n\nIf you have multiple users, you may wish to customize the content that is shown depending on the user that is logged in. You can retrieve the logged in user by [accessing the network request directly](accessing-the-network-request-directly) as discussed above, and then reading the `.username` attribute of the request. Here's an example:\n\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n m = gr.Markdown()\n demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Abubakar\", \"Abubakar\"), (\"Ali\", \"Ali\")])\n```\n\nNote: For authentication to work properly, third party cookies must be enabled in your browser. This is not the case by default for Safari or for Chrome Incognito Mode.\n\nIf users visit the `/logout` page of your Gradio app, they will automatically be logged out and session cookies deleted. This allows you to add logout functionality to your Gradio app as well. Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as ", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": " Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n m = gr.Markdown()\n logout_button = gr.Button(\"Logout\", link=\"/logout\")\n demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Pete\", \"Pete\"), (\"Dawood\", \"Dawood\")])\n```\nBy default, visiting `/logout` logs the user out from **all sessions** (e.g. if they are logged in from multiple browsers or devices, all will be signed out). If you want to log out only from the **current session**, add the query parameter `all_session=false` (i.e. `/logout?all_session=false`).\n\nNote: Gradio's built-in authentication provides a straightforward and basic layer of access control but does not offer robust security features for applications that require stringent access controls (e.g. multi-factor authentication, rate limiting, or automatic lockout policies).\n\nOAuth (Login via Hugging Face)\n\nGradio natively supports OAuth login via Hugging Face. In other words, you can easily add a _\"Sign in with Hugging Face\"_ button to your demo, which allows you to get a user's HF username as well as other information from their HF profile. Check out [this Space](https://huggingface.co/spaces/Wauplin/gradio-oauth-demo) for a live demo.\n\nTo enable OAuth, you must set `hf_oauth: true` as a Space metadata in your README.md file. This will register your Space\nas an OAuth application on Hugging Face. Next, you can use `gr.LoginButton` to add a login button to\nyour Gradio app. Once a user is logged in with their HF account, you can retrieve their profile by adding a parameter of type\n`gr.OAuthProfile` to any Gradio function. The user profile will be automatically injected as a parameter value. If you want\nto perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user\ntoken by adding a parameter of type `gr.OAuthToken`. You must def", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "e. If you want\nto perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user\ntoken by adding a parameter of type `gr.OAuthToken`. You must define which scopes you will use in your Space metadata\n(see [documentation](https://huggingface.co/docs/hub/spaces-oauthscopes) for more details).\n\nHere is a short example:\n\n$code_login_with_huggingface\n\nWhen the user clicks on the login button, they get redirected in a new page to authorize your Space.\n\n
\n\n
\n\nUsers can revoke access to their profile at any time in their [settings](https://huggingface.co/settings/connected-applications).\n\nAs seen above, OAuth features are available only when your app runs in a Space. However, you often need to test your app\nlocally before deploying it. To test OAuth features locally, your machine must be logged in to Hugging Face. Please run `huggingface-cli login` or set `HF_TOKEN` as environment variable with one of your access token. You can generate a new token in your settings page (https://huggingface.co/settings/tokens). Then, clicking on the `gr.LoginButton` will log in to your local Hugging Face profile, allowing you to debug your app with your Hugging Face account before deploying it to a Space.\n\n**Security Note**: It is important to note that adding a `gr.LoginButton` does not restrict users from using your app, in the same way that adding [username-password authentication](/guides/sharing-your-apppassword-protected-app) does. This means that users of your app who have not logged in with Hugging Face can still access and run events in your Gradio app -- the difference is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.\n\n\nOAuth (with external providers)\n\nIt is also possible to authenticate with external OAuth pr", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "erence is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.\n\n\nOAuth (with external providers)\n\nIt is also possible to authenticate with external OAuth providers (e.g. Google OAuth) in your Gradio apps. To do this, first mount your Gradio app within a FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. This function should be passed to the `auth_dependency` parameter in `gr.mount_gradio_app`.\n\nSimilar to [FastAPI dependency functions](https://fastapi.tiangolo.com/tutorial/dependencies/), the function specified by `auth_dependency` will run before any Gradio-related route in your FastAPI app. The function should accept a single parameter: the FastAPI `Request` and return either a string (representing a user's username) or `None`. If a string is returned, the user will be able to access the Gradio-related routes in your FastAPI app.\n\nFirst, let's show a simplistic example to illustrate the `auth_dependency` parameter:\n\n```python\nfrom fastapi import FastAPI, Request\nimport gradio as gr\n\napp = FastAPI()\n\ndef get_user(request: Request):\n return request.headers.get(\"user\")\n\ndemo = gr.Interface(lambda s: f\"Hello {s}!\", \"textbox\", \"textbox\")\n\napp = gr.mount_gradio_app(app, demo, path=\"/demo\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n uvicorn.run(app)\n```\n\nIn this example, only requests that include a \"user\" header will be allowed to access the Gradio app. Of course, this does not add much security, since any user can add this header in their request.\n\nHere's a more complete example showing how to add Google OAuth to a Gradio app (assuming you've already created OAuth Credentials on the [Google Developer Console](https://console.cloud.google.com/project)):\n\n```python\nimport os\nfrom authlib.integrations.starlette_client import OAuth, OAuthError\nfrom fastapi import FastA", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "entials on the [Google Developer Console](https://console.cloud.google.com/project)):\n\n```python\nimport os\nfrom authlib.integrations.starlette_client import OAuth, OAuthError\nfrom fastapi import FastAPI, Depends, Request\nfrom starlette.config import Config\nfrom starlette.responses import RedirectResponse\nfrom starlette.middleware.sessions import SessionMiddleware\nimport uvicorn\nimport gradio as gr\n\napp = FastAPI()\n\nReplace these with your own OAuth settings\nGOOGLE_CLIENT_ID = \"...\"\nGOOGLE_CLIENT_SECRET = \"...\"\nSECRET_KEY = \"...\"\n\nconfig_data = {'GOOGLE_CLIENT_ID': GOOGLE_CLIENT_ID, 'GOOGLE_CLIENT_SECRET': GOOGLE_CLIENT_SECRET}\nstarlette_config = Config(environ=config_data)\noauth = OAuth(starlette_config)\noauth.register(\n name='google',\n server_metadata_url='https://accounts.google.com/.well-known/openid-configuration',\n client_kwargs={'scope': 'openid email profile'},\n)\n\nSECRET_KEY = os.environ.get('SECRET_KEY') or \"a_very_secret_key\"\napp.add_middleware(SessionMiddleware, secret_key=SECRET_KEY)\n\nDependency to get the current user\ndef get_user(request: Request):\n user = request.session.get('user')\n if user:\n return user['name']\n return None\n\n@app.get('/')\ndef public(user: dict = Depends(get_user)):\n if user:\n return RedirectResponse(url='/gradio')\n else:\n return RedirectResponse(url='/login-demo')\n\n@app.route('/logout')\nasync def logout(request: Request):\n request.session.pop('user', None)\n return RedirectResponse(url='/')\n\n@app.route('/login')\nasync def login(request: Request):\n redirect_uri = request.url_for('auth')\n If your app is running on https, you should ensure that the\n `redirect_uri` is https, e.g. uncomment the following lines:\n \n from urllib.parse import urlparse, urlunparse\n redirect_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))\n return await oauth.google.authorize_redirect(request, redirect_uri)\n\n@app.route('/auth')\nasync def auth(request: Reque", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "direct_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))\n return await oauth.google.authorize_redirect(request, redirect_uri)\n\n@app.route('/auth')\nasync def auth(request: Request):\n try:\n access_token = await oauth.google.authorize_access_token(request)\n except OAuthError:\n return RedirectResponse(url='/')\n request.session['user'] = dict(access_token)[\"userinfo\"]\n return RedirectResponse(url='/')\n\nwith gr.Blocks() as login_demo:\n gr.Button(\"Login\", link=\"/login\")\n\napp = gr.mount_gradio_app(app, login_demo, path=\"/login-demo\")\n\ndef greet(request: gr.Request):\n return f\"Welcome to Gradio, {request.username}\"\n\nwith gr.Blocks() as main_demo:\n m = gr.Markdown(\"Welcome to Gradio!\")\n gr.Button(\"Logout\", link=\"/logout\")\n main_demo.load(greet, None, m)\n\napp = gr.mount_gradio_app(app, main_demo, path=\"/gradio\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n uvicorn.run(app)\n```\n\nThere are actually two separate Gradio apps in this example! One that simply displays a log in button (this demo is accessible to any user), while the other main demo is only accessible to users that are logged in. You can try this example out on [this Space](https://huggingface.co/spaces/gradio/oauth-example).\n\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Gradio apps can function as MCP (Model Context Protocol) servers, allowing LLMs to use your app's functions as tools. By simply setting `mcp_server=True` in the `.launch()` method, Gradio automatically converts your app's functions into MCP tools that can be called by MCP clients like Claude Desktop, Cursor, or Cline. The server exposes tools based on your function names, docstrings, and type hints, and can handle file uploads, authentication headers, and progress updates. You can also create MCP-only functions using `gr.api` and expose resources and prompts using decorators. For a comprehensive guide on building MCP servers with Gradio, see [Building an MCP Server with Gradio](https://www.gradio.app/guides/building-mcp-server-with-gradio).\n\n", "heading1": "MCP Servers", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "When publishing your app publicly, and making it available via API or via MCP server, you might want to set rate limits to prevent users from abusing your app. You can identify users using their IP address (using the `gr.Request` object [as discussed above](accessing-the-network-request-directly)) or, if they are logged in via Hugging Face OAuth, using their username. To see a complete example of how to set rate limits, please see [this Gradio app](https://github.com/gradio-app/gradio/blob/main/demo/rate_limit/run.py).\n\n", "heading1": "Rate Limits", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "By default, Gradio collects certain analytics to help us better understand the usage of the `gradio` library. This includes the following information:\n\n* What environment the Gradio app is running on (e.g. Colab Notebook, Hugging Face Spaces)\n* What input/output components are being used in the Gradio app\n* Whether the Gradio app is utilizing certain advanced features, such as `auth` or `show_error`\n* The IP address which is used solely to measure the number of unique developers using Gradio\n* The version of Gradio that is running\n\nNo information is collected from _users_ of your Gradio app. If you'd like to disable analytics altogether, you can do so by setting the `analytics_enabled` parameter to `False` in `gr.Blocks`, `gr.Interface`, or `gr.ChatInterface`. Or, you can set the GRADIO_ANALYTICS_ENABLED environment variable to `\"False\"` to apply this to all Gradio apps created across your system.\n\n*Note*: this reflects the analytics policy as of `gradio>=4.32.0`.\n\n", "heading1": "Analytics", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "[Progressive Web Apps (PWAs)](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps) are web applications that are regular web pages or websites, but can appear to the user like installable platform-specific applications.\n\nGradio apps can be easily served as PWAs by setting the `pwa=True` parameter in the `launch()` method. Here's an example:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\ndemo.launch(pwa=True) Launch your app as a PWA\n```\n\nThis will generate a PWA that can be installed on your device. Here's how it looks:\n\n![Installing PWA](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/install-pwa.gif)\n\nWhen you specify `favicon_path` in the `launch()` method, the icon will be used as the app's icon. Here's an example:\n\n```python\ndemo.launch(pwa=True, favicon_path=\"./hf-logo.svg\") Use a custom icon for your PWA\n```\n\n![Custom PWA Icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/pwa-favicon.png)\n", "heading1": "Progressive Web App (PWA)", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "**API endpoint names**\n\nWhen you create a Gradio application, the API endpoint names are automatically generated based on the function names. You can change this by using the `api_name` parameter in `gr.Interface` or `gr.ChatInterface`. If you are using Gradio `Blocks`, you can name each event listener, like this:\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\n**Controlling API endpoint visibility**\n\nWhen building a complex Gradio app, you might want to control how API endpoints appear or behave. Use the `api_visibility` parameter in any `Blocks` event listener to control this:\n\n- `\"public\"` (default): The endpoint is shown in API docs and accessible to all\n- `\"undocumented\"`: The endpoint is hidden from API docs but still accessible to downstream apps\n- `\"private\"`: The endpoint is hidden from API docs and not callable by the Gradio client libraries (e.g. `gradio_client` or `@gradio/client`). Note: this does **not** block direct HTTP requests to the endpoint \u2014 it should not be relied upon as a security measure.\n\nTo hide an API endpoint from the documentation while still allowing programmatic access:\n\n```python\nbtn.click(add, [num1, num2], output, api_visibility=\"undocumented\")\n```\n\n**Hiding endpoints from client libraries**\n\nIf you want to hide an API endpoint from the API docs and prevent it from being called by the Gradio client libraries, set `api_visibility=\"private\"`:\n\n```python\nbtn.click(add, [num1, num2], output, api_visibility=\"private\")\n```\n\nNote: setting `api_visibility=\"private\"` also means that downstream apps will not be able to load your Gradio app using `gr.load()` as this function uses the Gradio API under the hood. However, the underlying HTTP endpoint is still accessible \u2014 this setting should not be relied upon for security.\n\n**Adding API endpoints**\n\nYou can also add new API routes to your Gradio application that do not correspond to events in your UI.\n\nFor example, in this Gradio application, we add a new route that ad", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "\n\n**Adding API endpoints**\n\nYou can also add new API routes to your Gradio application that do not correspond to events in your UI.\n\nFor example, in this Gradio application, we add a new route that adds numbers and slices a list:\n\n```py\nimport gradio as gr\nwith gr.Blocks() as demo:\n with gr.Row():\n input = gr.Textbox()\n button = gr.Button(\"Submit\")\n output = gr.Textbox()\n def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:\n return a + b, c[a:b]\n gr.api(fn, api_name=\"add_and_slice\")\n\n_, url, _ = demo.launch()\n```\n\nThis will create a new route `/add_and_slice` which will show up in the \"view API\" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(url)\nresult = client.predict(\n a=3,\n b=5,\n c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n api_name=\"/add_and_slice\"\n)\nprint(result)\n```\n\n", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "This API page not only lists all of the endpoints that can be used to query the Gradio app, but also shows the usage of both [the Gradio Python client](https://gradio.app/guides/getting-started-with-the-python-client/), and [the Gradio JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). \n\nFor each endpoint, Gradio automatically generates a complete code snippet with the parameters and their types, as well as example inputs, allowing you to immediately test an endpoint. Here's an example showing an image file input and `str` output:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-snippet.png)\n\n\n", "heading1": "The Clients", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Instead of reading through the view API page, you can also use Gradio's built-in API recorder to generate the relevant code snippet. Simply click on the \"API Recorder\" button, use your Gradio app via the UI as you would normally, and then the API Recorder will generate the code using the Clients to recreate your all of your interactions programmatically.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/api-recorder.gif)\n\n", "heading1": "The API Recorder \ud83e\ude84", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Next to the \"Use via API\" link, the footer has a **Runs** link, which opens a page at `/gradio_api/runs` listing the runs made from this browser, grouped by endpoint. Each run shows its inputs, its outputs, how long the function took, and whether it succeeded. Clicking **Load run** puts a saved run's values back onto the page without calling the function again, which is a quick way to get back to an input you liked or to compare two results side by side.\n\nThe run history covers the same endpoints as this API page. An event listener with `api_visibility=\"undocumented\"` or `\"private\"` is not recorded, and neither is anything Gradio wires up on your behalf, such as loading an example.\n\nBy default, runs are saved in the browser's local storage and are never sent to the server, so each visitor only sees their own. If your app uses `auth`, this browser history is also scoped to the logged-in user. The most recent 100 browser runs are kept per running app.\n\nThe **History storage** control on this page can instead connect a private Hugging Face bucket. After connecting, future runs are stored in that bucket and can be opened from any browser that has access to it. Existing browser runs are not migrated, and switching back to **This browser** shows them again. On Spaces this requires Hugging Face OAuth; when running directly on localhost, Gradio uses the token from `hf auth login`. Bucket records are also scoped to the current app instance, because restarting an app may change its endpoints or input and output schemas.\n\nValues held in `gr.State` live on the server, so they are neither shown nor restored from either storage destination.\n\nThe link appears once the browser has saved its first run. To hide the link but keep recording, list the footer links you do want:\n\n```py\ndemo.launch(footer_links=[\"api\", \"gradio\", \"settings\"])\n```\n\nTo turn the feature off completely, set `run_history=False`. Nothing is recorded, the run history page returns a 404, and an", "heading1": "Run History", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "o want:\n\n```py\ndemo.launch(footer_links=[\"api\", \"gradio\", \"settings\"])\n```\n\nTo turn the feature off completely, set `run_history=False`. Nothing is recorded, the run history page returns a 404, and any runs this app had already saved are cleared from the browser the next time someone opens it:\n\n```py\ndemo.launch(run_history=False)\n```\n\nThis can also be set with the `GRADIO_RUN_HISTORY` environment variable, which is handy for a Space whose code you would rather not edit.\n\nRuns made through the clients\n\nCalls made with the JavaScript client are recorded in the same way whenever that client runs in a browser, which is how a `gr.Server` app builds up a run history despite having no UI of its own. Pass `record_history: false` to opt a single client out:\n\n```js\nconst app = await Client.connect(\"abidlabs/my-app\", { record_history: false });\n```\n\nNothing is recorded when the JavaScript client runs in Node, since there is no browser-selected history destination, and the Python client does not record runs at all. `run_history=False` on the app takes precedence over either client.\n\n", "heading1": "Run History", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "The API page also includes instructions on how to use the Gradio app as an Model Context Protocol (MCP) server, which is a standardized way to expose functions as tools so that they can be used by LLMs. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\nFor the MCP sever, each tool, its description, and its parameters are listed, along with instructions on how to integrate with popular MCP Clients. Read more about Gradio's [MCP integration here](https://www.gradio.app/guides/building-mcp-server-with-gradio).\n\n", "heading1": "MCP Server", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "You can access the complete OpenAPI (formerly Swagger) specification of your Gradio app's API at the endpoint `/gradio_api/openapi.json`. The OpenAPI specification is a standardized, language-agnostic interface description for REST APIs that enables both humans and computers to discover and understand the capabilities of your service.\n", "heading1": "OpenAPI Specification", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "You can initialize the `I18n` class with multiple language dictionaries to add custom translations:\n\n```python\nimport gradio as gr\n\nCreate an I18n instance with translations for multiple languages\ni18n = gr.I18n(\n en={\"greeting\": \"Hello, welcome to my app!\", \"submit\": \"Submit\"},\n es={\"greeting\": \"\u00a1Hola, bienvenido a mi aplicaci\u00f3n!\", \"submit\": \"Enviar\"},\n fr={\"greeting\": \"Bonjour, bienvenue dans mon application!\", \"submit\": \"Soumettre\"}\n)\n\nwith gr.Blocks() as demo:\n Use the i18n method to translate the greeting\n gr.Markdown(i18n(\"greeting\"))\n with gr.Row():\n input_text = gr.Textbox(label=\"Input\")\n output_text = gr.Textbox(label=\"Output\")\n \n submit_btn = gr.Button(i18n(\"submit\"))\n\nPass the i18n instance to the launch method\ndemo.launch(i18n=i18n)\n```\n\n", "heading1": "Setting Up Translations", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "When you use the `i18n` instance with a translation key, Gradio will show the corresponding translation to users based on their browser's language settings or the language they've selected in your app.\n\nIf a translation isn't available for the user's locale, the system will fall back to English (if available) or display the key itself.\n\n", "heading1": "How It Works", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Locale codes should follow the BCP 47 format (e.g., 'en', 'en-US', 'zh-CN'). The `I18n` class will warn you if you use an invalid locale code.\n\n", "heading1": "Valid Locale Codes", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "The following component properties typically support internationalization:\n\n- `description`\n- `info`\n- `title`\n- `placeholder`\n- `value`\n- `label`\n\nNote that support may vary depending on the component, and some properties might have exceptions where internationalization is not applicable. You can check this by referring to the typehint for the parameter and if it contains `I18nData`, then it supports internationalization.", "heading1": "Supported Component Properties", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Gradio can stream audio and video directly from your generator function.\nThis lets your user hear your audio or see your video nearly as soon as it's `yielded` by your function.\nAll you have to do is \n\n1. Set `streaming=True` in your `gr.Audio` or `gr.Video` output component.\n2. Write a python generator that yields the next \"chunk\" of audio or video.\n3. Set `autoplay=True` so that the media starts playing automatically.\n\nFor audio, the next \"chunk\" can be either an `.mp3` or `.wav` file or a `bytes` sequence of audio.\nFor video, the next \"chunk\" has to be either `.mp4` file or a file with `h.264` codec with a `.ts` extension.\nFor smooth playback, make sure chunks are consistent lengths and larger than 1 second.\n\nWe'll finish with some simple examples illustrating these points.\n\nStreaming Audio\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(audio_file):\n for _ in range(10):\n sleep(0.5)\n yield audio_file\n\ngr.Interface(keep_repeating,\n gr.Audio(sources=[\"microphone\"], type=\"filepath\"),\n gr.Audio(streaming=True, autoplay=True)\n).launch()\n```\n\nStreaming Video\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(video_file):\n for _ in range(10):\n sleep(0.5)\n yield video_file\n\ngr.Interface(keep_repeating,\n gr.Video(sources=[\"webcam\"], format=\"mp4\"),\n gr.Video(streaming=True, autoplay=True)\n).launch()\n```\n\n", "heading1": "Streaming Media", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "For an end-to-end example of streaming media, see the object detection from video [guide](/main/guides/object-detection-from-video) or the streaming AI-generated audio with [transformers](https://huggingface.co/docs/transformers/index) [guide](/main/guides/streaming-ai-generated-audio).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "- **1. Static files**. You can designate static files or directories using the `gr.set_static_paths` function. Static files are not be copied to the Gradio cache (see below) and will be served directly from your computer. This can help save disk space and reduce the time your app takes to launch but be mindful of possible security implications as any static files are accessible to all useres of your Gradio app.\n\n- **2. Files in the `allowed_paths` parameter in `launch()`**. This parameter allows you to pass in a list of additional directories or exact filepaths you'd like to allow users to have access to. (By default, this parameter is an empty list).\n\n- **3. Files in Gradio's cache**. After you launch your Gradio app, Gradio copies certain files into a temporary cache and makes these files accessible to users. Let's unpack this in more detail below.\n\n\n", "heading1": "Files Gradio allows users to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "First, it's important to understand why Gradio has a cache at all. Gradio copies files to a cache directory before returning them to the frontend. This prevents files from being overwritten by one user while they are still needed by another user of your application. For example, if your prediction function returns a video file, then Gradio will move that video to the cache after your prediction function runs and returns a URL the frontend can use to show the video. Any file in the cache is available via URL to all users of your running application.\n\nTip: You can customize the location of the cache by setting the `GRADIO_TEMP_DIR` environment variable to an absolute path, such as `/home/usr/scripts/project/temp/`. \n\nFiles Gradio moves to the cache\n\nGradio moves three kinds of files into the cache\n\n1. Files specified by the developer before runtime, e.g. cached examples, default values of components, or files passed into parameters such as the `avatar_images` of `gr.Chatbot`\n\n2. File paths returned by a prediction function in your Gradio application, if they ALSO meet one of the conditions below:\n\n* It is in the `allowed_paths` parameter of the `Blocks.launch` method.\n* It is in the current working directory of the python interpreter.\n* It is in the temp directory obtained by `tempfile.gettempdir()`.\n\n**Note:** files in the current working directory whose name starts with a period (`.`) will not be moved to the cache, even if they are returned from a prediction function, since they often contain sensitive information. \n\nIf none of these criteria are met, the prediction function that is returning that file will raise an exception instead of moving the file to cache. Gradio performs this check so that arbitrary files on your machine cannot be accessed.\n\n3. Files uploaded by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` p", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "d by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` parameter.\n\n", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "While running, Gradio apps will NOT ALLOW users to access:\n\n- **Files that you explicitly block via the `blocked_paths` parameter in `launch()`**. You can pass in a list of additional directories or exact filepaths to the `blocked_paths` parameter in `launch()`. This parameter takes precedence over the files that Gradio exposes by default, or by the `allowed_paths` parameter or the `gr.set_static_paths` function.\n\n- **Any other paths on the host machine**. Users should NOT be able to access other arbitrary paths on the host.\n\n", "heading1": "The files Gradio will not allow others to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Sharing your Gradio application will also allow users to upload files to your computer or server. You can set a maximum file size for uploads to prevent abuse and to preserve disk space. You can do this with the `max_file_size` parameter of `.launch`. For example, the following two code snippets limit file uploads to 5 megabytes per file.\n\n```python\nimport gradio as gr\n\ndemo = gr.Interface(lambda x: x, \"image\", \"image\")\n\ndemo.launch(max_file_size=\"5mb\")\nor\ndemo.launch(max_file_size=5 * gr.FileSize.MB)\n```\n\n", "heading1": "Uploading Files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "* Set a `max_file_size` for your application.\n* Do not return arbitrary user input from a function that is connected to a file-based output component (`gr.Image`, `gr.File`, etc.). For example, the following interface would allow anyone to move an arbitrary file in your local directory to the cache: `gr.Interface(lambda s: s, \"text\", \"file\")`. This is because the user input is treated as an arbitrary file path. \n* Make `allowed_paths` as small as possible. If a path in `allowed_paths` is a directory, any file within that directory can be accessed. Make sure the entires of `allowed_paths` only contains files related to your application.\n* Run your gradio application from the same directory the application file is located in. This will narrow the scope of files Gradio will be allowed to move into the cache. For example, prefer `python app.py` to `python Users/sources/project/app.py`.\n\n\n", "heading1": "Best Practices", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Both `gr.set_static_paths` and the `allowed_paths` parameter in launch expect absolute paths. Below is a minimal example to display a local `.png` image file in an HTML block.\n\n```txt\n\u251c\u2500\u2500 assets\n\u2502 \u2514\u2500\u2500 logo.png\n\u2514\u2500\u2500 app.py\n```\nFor the example directory structure, `logo.png` and any other files in the `assets` folder can be accessed from your Gradio app in `app.py` as follows:\n\n```python\nfrom pathlib import Path\n\nimport gradio as gr\n\ngr.set_static_paths(paths=[Path.cwd().absolute()/\"assets\"])\n\nwith gr.Blocks() as demo:\n gr.HTML(\"\")\n\ndemo.launch()\n```\n", "heading1": "Example: Accessing local files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "When a user closes their browser tab, Gradio will automatically delete any `gr.State` variables associated with that user session after 60 minutes. If the user connects again within those 60 minutes, no state will be deleted.\n\nYou can control the deletion behavior further with the following two parameters of `gr.State`:\n\n1. `delete_callback` - An arbitrary function that will be called when the variable is deleted. This function must take the state value as input. This function is useful for deleting variables from GPU memory.\n2. `time_to_live` - The number of seconds the state should be stored for after it is created or updated. This will delete variables before the session is closed, so it's useful for clearing state for potentially long running sessions.\n\n", "heading1": "Automatic deletion of `gr.State`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Your Gradio application will save uploaded and generated files to a special directory called the cache directory. Gradio uses a hashing scheme to ensure that duplicate files are not saved to the cache but over time the size of the cache will grow (especially if your app goes viral \ud83d\ude09).\n\nGradio can periodically clean up the cache for you if you specify the `delete_cache` parameter of `gr.Blocks()`, `gr.Interface()`, or `gr.ChatInterface()`. \nThis parameter is a tuple of the form `[frequency, age]` both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. \nFor example, setting this to (86400, 86400) will delete temporary files every day if they are older than a day old.\nAdditionally, the cache will be deleted entirely when the server restarts.\n\n", "heading1": "Automatic cache cleanup via `delete_cache`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Additionally, Gradio now includes a `Blocks.unload()` event, allowing you to run arbitrary cleanup functions when users disconnect (this does not have a 60 minute delay).\nUnlike other gradio events, this event does not accept inputs or outptus.\nYou can think of the `unload` event as the opposite of the `load` event.\n\n", "heading1": "The `unload` event", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "The following demo uses all of these features. When a user visits the page, a special unique directory is created for that user.\nAs the user interacts with the app, images are saved to disk in that special directory.\nWhen the user closes the page, the images created in that session are deleted via the `unload` event.\nThe state and files in the cache are cleaned up automatically as well.\n\n$code_state_cleanup\n$demo_state_cleanup", "heading1": "Putting it all together", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "1. `GRADIO_SERVER_PORT`\n\n- **Description**: Specifies the port on which the Gradio app will run.\n- **Default**: `7860`\n- **Example**:\n ```bash\n export GRADIO_SERVER_PORT=8000\n ```\n\n2. `GRADIO_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio server. To make Gradio accessible from any IP address, set this to `\"0.0.0.0\"`\n- **Default**: `\"127.0.0.1\"` \n- **Example**:\n ```bash\n export GRADIO_SERVER_NAME=\"0.0.0.0\"\n ```\n\n3. `GRADIO_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio server.\n- **Default**: `100`\n- **Example**:\n ```bash\n export GRADIO_NUM_PORTS=200\n ```\n\n4. `GRADIO_ANALYTICS_ENABLED`\n\n- **Description**: Whether Gradio should provide \n- **Default**: `\"True\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_ANALYTICS_ENABLED=\"True\"\n ```\n\n5. `GRADIO_DEBUG`\n\n- **Description**: Enables or disables debug mode in Gradio. If debug mode is enabled, the main thread does not terminate allowing error messages to be printed in environments such as Google Colab.\n- **Default**: `0`\n- **Example**:\n ```sh\n export GRADIO_DEBUG=1\n ```\n\n6. `GRADIO_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag inputs/outputs in the Gradio interface. See [the Guide on flagging](/guides/using-flagging) for more details.\n- **Default**: `\"manual\"`\n- **Options**: `\"never\"`, `\"manual\"`, `\"auto\"`\n- **Example**:\n ```sh\n export GRADIO_FLAGGING_MODE=\"never\"\n ```\n\n7. `GRADIO_TEMP_DIR`\n\n- **Description**: Specifies the directory where temporary files created by Gradio are stored.\n- **Default**: System default temporary directory\n- **Example**:\n ```sh\n export GRADIO_TEMP_DIR=\"/path/to/temp\"\n ```\n\n8. `GRADIO_ROOT_PATH`\n\n- **Description**: Sets the root path for the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n ```sh\n export GRADIO_ROOT_PATH=", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "r the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n ```sh\n export GRADIO_ROOT_PATH=\"/myapp\"\n ```\n\n9. `GRADIO_SHARE`\n\n- **Description**: Enables or disables sharing the Gradio app.\n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_SHARE=\"True\"\n ```\n\n10. `GRADIO_ALLOWED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is allowed to serve. Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n ```sh\n export GRADIO_ALLOWED_PATHS=\"/mnt/sda1,/mnt/sda2\"\n ```\n\n11. `GRADIO_BLOCKED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n ```sh\n export GRADIO_BLOCKED_PATHS=\"/users/x/gradio_app/admin,/users/x/gradio_app/keys\"\n ```\n\n12. `FORWARDED_ALLOW_IPS`\n\n- **Description**: This is not a Gradio-specific environment variable, but rather one used in server configurations, specifically `uvicorn` which is used by Gradio internally. This environment variable is useful when deploying applications behind a reverse proxy. It defines a list of IP addresses that are trusted to forward traffic to your application. When set, the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [objec", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": " the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [object's](https://www.gradio.app/docs/gradio/request) `client.host` property, it will correctly get the user's IP address instead of the IP address of the reverse proxy server. Note that only trusted IP addresses (i.e. the IP addresses of your reverse proxy servers) should be added, as any server with these IP addresses can modify the `X-Forwarded-For` header and spoof the client's IP address.\n- **Default**: `\"127.0.0.1\"`\n- **Example**:\n ```sh\n export FORWARDED_ALLOW_IPS=\"127.0.0.1,192.168.1.100\"\n ```\n\n13. `GRADIO_CACHE_EXAMPLES`\n\n- **Description**: Whether or not to cache examples by default in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()` when no explicit argument is passed for the `cache_examples` parameter. You can set this environment variable to either the string \"true\" or \"false\".\n- **Default**: `\"false\"`\n- **Example**:\n ```sh\n export GRADIO_CACHE_EXAMPLES=\"true\"\n ```\n\n\n14. `GRADIO_CACHE_MODE`\n\n- **Description**: How to cache examples. Only applies if `cache_examples` is set to `True` either via enviornment variable or by an explicit parameter, AND no no explicit argument is passed for the `cache_mode` parameter in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`. Can be set to either the strings \"lazy\" or \"eager.\" If \"lazy\", examples are cached after their first use for all users of the app. If \"eager\", all examples are cached at app launch.\n\n- **Default**: `\"eager\"`\n- **Example**:\n ```sh\n export GRADIO_CACHE_MODE=\"lazy\"\n ```\n\n\n15. `GRADIO_EXAMPLES_CACHE`\n\n- **Description**: If you set `cache_examples=True` in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your app's working directory. You can customize the location of cached example files created by Gradio by setting the environment variable `GRADIO_EXAMPLES_CACHE` to an absolute path or a path relative to your working directory.\n- **Default**: `\".gradio/cached_examples/\"`\n- **Example**:\n ```sh\n export GRADIO_EXAMPLES_CACHE=\"custom_cached_examples/\"\n ```\n\n\n16. `GRADIO_SSR_MODE`\n\n- **Description**: Controls whether server-side rendering (SSR) is enabled. When enabled, the initial HTML is rendered on the server rather than the client, which can improve initial page load performance and SEO.\n\n- **Default**: `\"False\"` (except on Hugging Face Spaces, where this environment variable sets it to `True`)\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_SSR_MODE=\"True\"\n ```\n\n17. `GRADIO_NODE_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `GRADIO_SERVER_NAME` if it is set, otherwise `\"127.0.0.1\"`\n- **Example**:\n ```sh\n export GRADIO_NODE_SERVER_NAME=\"0.0.0.0\"\n ```\n\n18. `GRADIO_NODE_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `100`\n- **Example**:\n ```sh\n export GRADIO_NODE_NUM_PORTS=200\n ```\n\n19. `GRADIO_RESET_EXAMPLES_CACHE`\n\n- **Description**: If set to \"True\", Gradio will delete and recreate the examples cache directory when the app starts instead of reusing the cached example if they already exist. \n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag messages in `gr.ChatInterface` applications. Similar to `GRADIO_FLAGGING_MODE` but specifically for chat interfaces.\n- **Default**: `\"never\"`\n- **Options**: `\"never\"`, `\"manual\"`\n- **Example**:\n ```sh\n export GRADIO_CHAT_FLAGGING_MODE=\"manual\"\n ```\n\n21. `GRADIO_WATCH_DIRS`\n\n- **Description**: Specifies directories to watch for file changes when running Gradio in development mode. When files in these directories change, the Gradio app will automatically reload. Multiple directories can be specified by separating them with commas. This is primarily used by the `gradio` CLI command for development workflows.\n- **Default**: `\"\"`\n- **Example**:\n ```sh\n export GRADIO_WATCH_DIRS=\"/path/to/src,/path/to/templates\"\n ```\n\n22. `GRADIO_VIBE_MODE`\n\n- **Description**: Enables the Vibe editor mode, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. When enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use with extreme caution in production environments.\n- **Default**: `\"\"`\n- **Options**: Any non-empty string enables the mode\n- **Example**:\n ```sh\n export GRADIO_VIBE_MODE=\"1\"\n ```\n\n23. `GRADIO_MCP_SERVER`\n\n- **Description**: Enables the MCP (Model Context Protocol) server functionality in Gradio. When enabled, the Gradio app will be set up as an MCP server and documented functions will be added as MCP tools that can be used by LLMs. This allows LLMs to interact with your Gradio app's functionality through the MCP protocol.\n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_MCP_SERVER=\"True\"\n ```\n\n\n24. `GRADIO_NUM_WORKERS`\n\n- **Description**: Number of multiple workers to launch in the background", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "*Options**: `\"True\"`, `\"False\"`\n- **Example**:\n ```sh\n export GRADIO_MCP_SERVER=\"True\"\n ```\n\n\n24. `GRADIO_NUM_WORKERS`\n\n- **Description**: Number of multiple workers to launch in the background to offload traffic for file I/O and static assets from the main Gradio server. Only works when SSR mode is set.\n- **Default**: not set.\n- **Options**: Any positive integer.\n- **Example**:\n ```sh\n export GRADIO_NUM_WORKERS=4\n ```\n\n25. `GRADIO_HEARTBEAT_INTERVAL`\n\n- **Description**: Sets the interval, in seconds, between heartbeats that keep a client session alive. When a client disconnects, this heartbeat is used to trigger `unload` events and clean up session state. Lowering this value can help detect disconnections faster in environments such as Kubernetes, where the default interval can delay session cleanup.\n- **Default**: `15`\n- **Example**:\n ```sh\n export GRADIO_HEARTBEAT_INTERVAL=5\n ```\n\n", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "To set environment variables in your terminal, use the `export` command followed by the variable name and its value. For example:\n\n```sh\nexport GRADIO_SERVER_PORT=8000\n```\n\nIf you're using a `.env` file to manage your environment variables, you can add them like this:\n\n```sh\nGRADIO_SERVER_PORT=8000\nGRADIO_SERVER_NAME=\"localhost\"\n```\n\nThen, use a tool like `dotenv` to load these variables when running your application.\n\n\n\n", "heading1": "How to Set Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "To add custom buttons to a component, pass a list of `gr.Button()` instances to the `buttons` parameter:\n\n```python\nimport gradio as gr\n\nrefresh_btn = gr.Button(\"Refresh\", variant=\"secondary\", size=\"sm\")\nclear_btn = gr.Button(\"Clear\", variant=\"secondary\", size=\"sm\")\n\ntextbox = gr.Textbox(\n value=\"Sample text\",\n label=\"Text Input\",\n buttons=[refresh_btn, clear_btn]\n)\n```\n\nYou can also mix built-in buttons (as strings) with custom buttons:\n\n```python\ncode = gr.Code(\n value=\"print('Hello')\",\n language=\"python\",\n buttons=[\"copy\", \"download\", refresh_btn, export_btn]\n)\n```\n\n", "heading1": "Basic Usage", "source_page_url": "https://gradio.app/guides/custom-buttons", "source_page_title": "Additional Features - Custom Buttons Guide"}, {"text": "Custom buttons work just like regular `gr.Button` components. You can connect them to Python functions or JavaScript functions using the `.click()` method:\n\nPython Functions\n\n```python\ndef refresh_data():\n import random\n return f\"Refreshed: {random.randint(1000, 9999)}\"\n\nrefresh_btn.click(refresh_data, outputs=textbox)\n```\n\nJavaScript Functions\n\n```python\nclear_btn.click(\n None,\n inputs=[],\n outputs=textbox,\n js=\"() => ''\"\n)\n```\n\nCombined Python and JavaScript\n\nYou can use the same button for both Python and JavaScript logic:\n\n```python\nalert_btn.click(\n None,\n inputs=textbox,\n outputs=[],\n js=\"(text) => { alert('Text: ' + text); return []; }\"\n)\n```\n\n", "heading1": "Connecting Button Events", "source_page_url": "https://gradio.app/guides/custom-buttons", "source_page_title": "Additional Features - Custom Buttons Guide"}, {"text": "Here's a complete example showing custom buttons with both Python and JavaScript functions:\n\n$code_textbox_custom_buttons\n\n\n", "heading1": "Complete Example", "source_page_url": "https://gradio.app/guides/custom-buttons", "source_page_title": "Additional Features - Custom Buttons Guide"}, {"text": "- Custom buttons appear in the component's toolbar, typically in the top-right corner\n- Only the `value` of the Button is used, other attributes like `icon` are not used.\n- Buttons are rendered in the order they appear in the `buttons` list\n- Built-in buttons (like \"copy\", \"download\") can be hidden by omitting them from the list\n- Custom buttons work with component events in the same way as as regular buttons\n", "heading1": "Notes", "source_page_url": "https://gradio.app/guides/custom-buttons", "source_page_title": "Additional Features - Custom Buttons Guide"}, {"text": "Let's create a demo where a user can choose a filter to apply to their webcam stream. Users can choose from an edge-detection filter, a cartoon filter, or simply flipping the stream vertically.\n\n$code_streaming_filter\n$demo_streaming_filter\n\nYou will notice that if you change the filter value it will immediately take effect in the output stream. That is an important difference of stream events in comparison to other Gradio events. The input values of the stream can be changed while the stream is being processed. \n\nTip: We set the \"streaming\" parameter of the image output component to be \"True\". Doing so lets the server automatically convert our output images into base64 format, a format that is efficient for streaming.\n\n", "heading1": "A Realistic Image Demo", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For some image streaming demos, like the one above, we don't need to display separate input and output components. Our app would look cleaner if we could just display the modified output stream.\n\nWe can do so by just specifying the input image component as the output of the stream event.\n\n$code_streaming_filter_unified\n$demo_streaming_filter_unified\n\n", "heading1": "Unified Image Demos", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "Your streaming function should be stateless. It should take the current input and return its corresponding output. However, there are cases where you may want to keep track of past inputs or outputs. For example, you may want to keep a buffer of the previous `k` inputs to improve the accuracy of your transcription demo. You can do this with Gradio's `gr.State()` component.\n\nLet's showcase this with a sample demo:\n\n```python\ndef transcribe_handler(current_audio, state, transcript):\n next_text = transcribe(current_audio, history=state)\n state.append(current_audio)\n state = state[-3:]\n return state, transcript + next_text\n\nwith gr.Blocks() as demo:\n with gr.Row():\n with gr.Column():\n mic = gr.Audio(sources=\"microphone\")\n state = gr.State(value=[])\n with gr.Column():\n transcript = gr.Textbox(label=\"Transcript\")\n mic.stream(transcribe_handler, [mic, state, transcript], [state, transcript],\n time_limit=10, stream_every=1)\n\n\ndemo.launch()\n```\n\n", "heading1": "Keeping track of past inputs or outputs", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For an end-to-end example of streaming from the webcam, see the object detection from webcam [guide](/main/guides/object-detection-from-webcam-with-webrtc).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "The simplest possible Workflow app:\n\n```python\nimport gradio as gr\n\ngr.Workflow().launch()\n```\n\nOpen the app, drag Spaces, models, and datasets from the sidebar onto the canvas, connect their ports, and hit **Run**. As you add, remove, or change nodes and edges, a `workflow.json` file will automatically be created next to the Python script that created the Workflow. Pass `graph=` if you want to save it somewhere else. You can also use a coding agent to write or edit this file, allowing you to create workflows programmatically.\n\n`gr.Workflow` is already a complete Gradio app and must be created at the top level. It cannot be nested inside a `gr.Blocks` context.\n\nWhen running locally, `launch()` prints a private write-access URL. Open that URL to edit and save the workflow; the ordinary local URL and share URL are run-only. Keep the write-access URL private because edits affect the workflow seen by every visitor.\n\n", "heading1": "Quickstart", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Pass your own Python functions via `bind=` and they appear as callable nodes on the canvas. Gradio inspects the function signature to auto-generate input/output ports.\n\n```python\nimport gradio as gr\n\ndef summarize(text: str) -> str:\n return text[:200]\n\ngr.Workflow(bind=[summarize]).launch()\n```\n\nUse a dict to give nodes explicit names:\n\n```python\ngr.Workflow(bind={\"My Summarizer\": summarize}).launch()\n```\n\nSignature inference is intentionally simple. Parameters annotated as `int` or `float` become `number` ports, `bool` becomes `boolean`, and strings, unannotated parameters, and other annotations default to `text`. Gradio initially generates one output port for each bound function. For media ports or multiple outputs, define the function node's ports explicitly in the workflow JSON.\n\n", "heading1": "Binding Python functions", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "For pipelines you want to ship with a fixed topology, declare edges programmatically:\n\n```python\nimport gradio as gr\n\ndef clean(text: str) -> str:\n return text.strip().lower()\n\ndef tag(text: str) -> str:\n return f\"[processed] {text}\"\n\ngr.Workflow(\n bind=[clean, tag],\n edges=[(\"clean\", \"tag\")],\n).launch()\n```\n\nEach edge is a `(from_fn, to_fn)` tuple referring to functions in `bind=`. Use `\"fn_name.port_label\"` to target a specific port when a node has multiple inputs or outputs; otherwise, the first port is used. Ensure the connected ports have compatible types.\n\n> **Note:** `edges=` only connects bound Python functions while generating a new workflow. It cannot create edges to Space, model, or dataset nodes, and it is ignored when the workflow file already exists. Delete the file to regenerate the initial topology from `bind` and `edges`.\n\n", "heading1": "Defining edges in code", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Pass a `graph=` path to load a saved workflow topology. The canvas reads from the file on each page load and autosaves back to it when you add, remove, or change nodes and edges.\n\n```python\ngr.Workflow(graph=\"workflow.json\").launch()\n```\n\nIf the file doesn't exist yet, it's created on the first authorized edit. `bind=` does not automatically add or wire functions into an existing graph. To combine an existing graph with bound functions, either add the functions from the canvas's **Functions** menu or include an operator with `\"kind\": \"fn\"` whose `\"fn\"` value exactly matches a key in `bind`.\n\n", "heading1": "Loading from a JSON file", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "A workflow is a JSON file with three node collections:\n\n```json\n{\n \"schema_version\": \"2\",\n \"name\": \"My Pipeline\",\n \"references\": [\n {\n \"id\": \"ref_prompt\", \"label\": \"Prompt\", \"role\": \"reference\",\n \"asset_type\": \"text\",\n \"inputs\": [{\"id\": \"in\", \"label\": \"Text\", \"type\": \"text\"}],\n \"outputs\": [{\"id\": \"out\", \"label\": \"Text\", \"type\": \"text\"}]\n }\n ],\n \"operators\": [\n {\n \"id\": \"op_flux\", \"label\": \"FLUX.1\", \"role\": \"operator\",\n \"kind\": \"model\",\n \"model_id\": \"black-forest-labs/FLUX.1-schnell\",\n \"endpoint\": \"text_to_image\",\n \"pipeline_tag\": \"text-to-image\",\n \"inputs\": [{\"id\": \"prompt\", \"label\": \"Prompt\", \"type\": \"text\", \"required\": true}],\n \"outputs\": [{\"id\": \"out_0\", \"label\": \"Image\", \"type\": \"image\", \"output_index\": 0}]\n }\n ],\n \"subjects\": [\n {\n \"id\": \"sub_img\", \"label\": \"Output Image\", \"role\": \"subject\",\n \"asset_type\": \"image\",\n \"inputs\": [{\"id\": \"in\", \"label\": \"Image\", \"type\": \"image\"}],\n \"outputs\": [{\"id\": \"out\", \"label\": \"Image\", \"type\": \"image\"}]\n }\n ],\n \"edges\": [\n {\n \"id\": \"e1\",\n \"from_node_id\": \"ref_prompt\", \"from_port_id\": \"out\",\n \"to_node_id\": \"op_flux\", \"to_port_id\": \"prompt\",\n \"type\": \"text\"\n },\n {\n \"id\": \"e2\",\n \"from_node_id\": \"op_flux\", \"from_port_id\": \"out_0\",\n \"to_node_id\": \"sub_img\", \"to_port_id\": \"in\",\n \"type\": \"image\"\n }\n ]\n}\n```\n\nNode `data` may be omitted, and so may geometry. Include `x` and `y` on every node to control how the graph is arranged when someone opens it for the first time; leave them out and the canvas auto-arranges it. Either way the arrangement is only a starting point: each visitor is free to drag and resize cards, that arrangement is saved in their own browser rather than in the file, and `workflow.json` is never rewritten with it. `height` is measured from the rendered card. `width` is the default every viewer starts from \u2014 anyone can resize a card locally, and a write", "heading1": "Workflow JSON format", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "n the file, and `workflow.json` is never rewritten with it. `height` is measured from the rendered card. `width` is the default every viewer starts from \u2014 anyone can resize a card locally, and a writer's resize becomes the new default the next time an edit is saved.\n\n| Collection | Role |\n|---|---|\n| `references` | Inputs \u2014 uploaded files, editable text, literal values |\n| `operators` | Processing steps \u2014 Spaces, models, datasets, Python functions |\n| `subjects` | Outputs \u2014 the results being created |\n\nOperator kinds\n\n| `kind` | What it calls |\n|---|---|\n| `\"space\"` | A Gradio Space on the Hub via `gradio_client`; set `space_id` and `endpoint` |\n| `\"model\"` | A Hugging Face model via `InferenceClient`; set `model_id` and a supported `endpoint` such as `text_to_image`. `pipeline_tag` is also stored for discovery and compatibility with older graphs |\n| `\"dataset\"` | One row from a Hub dataset per run, selected by the `row_index` input; set `dataset_id`, `dataset_config`, and `dataset_split` |\n| `\"fn\"` | A Python function whose `fn` value matches a key passed via `bind=` |\n\n", "heading1": "Workflow JSON format", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Ports are typed so the canvas can validate connections. Supported types:\n\n`image` \u00b7 `audio` \u00b7 `video` \u00b7 `text` \u00b7 `number` \u00b7 `boolean` \u00b7 `gallery` \u00b7 `file` \u00b7 `json` \u00b7 `model3d` \u00b7 `any`\n\n`any` is a compatibility fallback that can connect to every port type. `file` and `any` usually come from API schema inference and are not offered as reference or subject templates in the canvas picker.\n\n", "heading1": "Port types", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "One reference can feed multiple operators simultaneously. When you run the workflow in the interactive canvas, operators at the same dependency depth run in parallel:\n\n```python\nworkflow.json excerpt \u2014 one product photo \u2192 4 FLUX Kontext branches\n\"edges\": [\n {\"from_node_id\": \"ref_product\", ..., \"to_node_id\": \"op_kontext_0\", ...},\n {\"from_node_id\": \"ref_product\", ..., \"to_node_id\": \"op_kontext_1\", ...},\n {\"from_node_id\": \"ref_product\", ..., \"to_node_id\": \"op_kontext_2\", ...},\n {\"from_node_id\": \"ref_product\", ..., \"to_node_id\": \"op_kontext_3\", ...}\n]\n```\n\nWhen the same workflow is invoked through its generated Gradio API, the server currently executes these branches sequentially.\n\n", "heading1": "Fan-out pipelines", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "A Workflow app is a standard Gradio app \u2014 deploy it to Hugging Face Spaces exactly like any other, by uploading the code to a Space, or by simply running in your terminal:\n\n```\ngradio deploy\n```\n\nSet `hf_oauth: true` [in your Space](https://huggingface.co/docs/hub/en/spaces-oauth) so the owner can authenticate for editing. The owning user, or an organization member with `write` or `admin` access, can edit and save the workflow. Other visitors get a read-only canvas and can run the pipeline using their OAuth identity or a Hugging Face access token. Without OAuth enabled, the Space cannot identify its owner, so the deployed workflow remains run-only.\n\n", "heading1": "Deploying to Spaces", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Two different things in a Workflow app can spend GPU time, and each is set up its own way:\n\n- **Space, model, and dataset nodes** call out to someone else's hardware. The workflow app itself stays on CPU; what matters is *whose* quota each call is billed to.\n- **`kind: \"fn\"` nodes** run inside the workflow app. If one of them does GPU work, the workflow app is itself a ZeroGPU Space, and `@spaces.GPU` applies exactly as in any other Gradio app, plus a few canvas-specific details covered below.\n\nError reference\n\nWhere each ZeroGPU failure comes from, by the text it reports:\n\n| Message | Cause | Fix |\n|---|---|---|\n| `ZeroGPU illegal duration` \u2014 \"The requested GPU duration (270s) is larger than the maximum allowed\" | The *multiplied* duration exceeds the caller's maximum. The number quoted is yours \u00d7 the hardware factor, so it won't match your source. | [Sizing `duration`](sizing-duration-on-bound-functions) |\n| `ZeroGPU quota exceeded` \u2014 \"Space app has reached its GPU limit.\" | The request carried no visitor token, so it was billed to the Space's shared anonymous allowance. | [Whose quota pays](whose-quota-pays) \u2014 enable `hf_oauth` |\n| `ZeroGPU quota exceeded` \u2014 \"You have exceeded your ZeroGPU runs limit.\" / \"\u2026quota (Ns requested vs. Ms left)\" | The signed-in caller is out of quota. | [Whose quota pays](whose-quota-pays) |\n| `ZeroGPU pending credits exceeded` \u2014 \"You have too many ZeroGPU credits allocated to running tasks.\" | Too much duration booked at once. Chained nodes each hold their own slot, so an over-sized `duration` multiplies across the chain. | [Sizing `duration`](sizing-duration-on-bound-functions) |\n| `ZeroGPU duration` \u2014 \"GPU task is exceeding its requested duration and might be aborted\" | `duration` is too small for the work the node actually does. | [Sizing `duration`](sizing-duration-on-bound-functions) |\n| `ZeroGPU client warning` \u2014 \"GPU device not used\" | The decorated function never touched the GPU \u2014 usually the model was moved to `cuda`", "heading1": "gr.Workflow on ZeroGPU", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": ". | [Sizing `duration`](sizing-duration-on-bound-functions) |\n| `ZeroGPU client warning` \u2014 \"GPU device not used\" | The decorated function never touched the GPU \u2014 usually the model was moved to `cuda` somewhere the decorator can't see. | [Load models at module scope](load-models-at-module-scope) |\n| `RuntimeError: CUDA has been initialized before importing the` `spaces` `package.` | An import that initializes CUDA ran before `import spaces`. | [Load models at module scope](load-models-at-module-scope) |\n| A node renders a path or `{...}` as text where an image was expected | The function returned a `PIL.Image` or a bare path instead of a file dict, or the output port isn't typed `image`. | [Media in and out](media-in-and-out-of-a-bound-function) |\n\n`ZeroGPU queue timeout` \u2014 \"No GPU was available after 60s\" is contention, not a configuration problem; retry.\n\nWhose quota pays\n\nMost Space and model nodes run on [ZeroGPU](https://huggingface.co/docs/hub/spaces-zerogpu) or a Hugging Face inference provider, so every run of a workflow spends somebody's GPU quota. Which account pays depends on the token each node resolves, in this order: a token entered on the node itself, otherwise the visitor's OAuth token. When you run the workflow locally, it uses your own saved Hugging Face token. Visitors who aren't signed in fall back to the anonymous tier, which is a couple of minutes of GPU time a day, so they'll hit quota errors quickly.\n\nThis makes `hf_oauth: true` load-bearing for two separate reasons. Without it the Space cannot identify its owner, so nobody can edit the workflow \u2014 *and* every visitor runs anonymously on a shared IP-based allowance, so the pipeline starts failing with quota errors almost immediately. Enable it even for a workflow you never intend to let anyone edit:\n\n```yaml\nREADME.md of your Space\nhf_oauth: true\n```\n\nThe workflow app itself does not need GPU hardware. It orchestrates calls to other Spaces, so CPU basic is the right choice unless a fu", "heading1": "gr.Workflow on ZeroGPU", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "one edit:\n\n```yaml\nREADME.md of your Space\nhf_oauth: true\n```\n\nThe workflow app itself does not need GPU hardware. It orchestrates calls to other Spaces, so CPU basic is the right choice unless a function you passed to `bind=` does its own GPU work.\n\nSizing `duration` on bound functions\n\nThe number you write in `@spaces.GPU(duration=N)` is **not** the number checked against the caller's quota. ZeroGPU multiplies it by a factor that depends on the GPU the Space landed on:\n\n| Hardware | Duration factor |\n|---|---|\n| NVIDIA H200 | 1.0 |\n| NVIDIA RTX PRO 6000 Blackwell | 1.5 |\n\nSo `@spaces.GPU(duration=180)` on Blackwell hardware books **270s**, and that 270s is what the quota gate compares against the caller's allowance \u2014 which is why a function can be rejected as exceeding the maximum allowed duration while the number in your source is comfortably under it. The error message quotes the multiplied figure, not yours.\n\nTwo things change the arithmetic:\n\n- Passing `size=\"xlarge\"` skips the multiplier entirely, so `duration=180` books exactly 180s. Don't read that as cheaper: when an xlarge call is rejected for quota, ZeroGPU reports the figure doubled, so an xlarge slot appears to count twice over against the allowance.\n- Omitting `duration` requests 60s before multiplication, not an unlimited slot.\n\n**Size `duration` per node, never per pipeline.** Each `fn` node is a separate call that books and releases its own GPU slot, so a chain of three 60s nodes is three 60s bookings rather than one 180s one. There is no arithmetic in the workflow executor that sums durations across chained nodes \u2014 each node is accounted for on its own.\n\nLoad models at module scope\n\nLoad weights once at import and move them to `cuda` there, not inside the decorated function:\n\n```python\nimport spaces must be imported before anything initializes CUDA\nimport torch\nfrom diffusers import AutoPipelineForText2Image\n\npipe = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/sdxl-turb", "heading1": "gr.Workflow on ZeroGPU", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "t spaces must be imported before anything initializes CUDA\nimport torch\nfrom diffusers import AutoPipelineForText2Image\n\npipe = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/sdxl-turbo\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\n\n@spaces.GPU(duration=60)\ndef illustrate(prompt: str) -> dict:\n return save(pipe(prompt=prompt, num_inference_steps=2).images[0])\n```\n\nOn ZeroGPU the `spaces` package intercepts that module-scope `.to(\"cuda\")`, packs the weights, and transfers them when a GPU is actually allocated \u2014 so it costs no GPU time at import. Moving the model inside the function instead repeats the transfer on every call, which is charged against the `duration` you booked.\n\n`import spaces` must come before any import that initializes CUDA, or it raises at startup. On ZeroGPU, `spaces` forces `torch.cuda.is_available()` to return `True`, so the usual `cuda` / `mps` / `cpu` ternary resolves to `cuda` there and still lets the same file run locally.\n\nMedia in and out of a bound function\n\nA bound function's arguments and return value are passed through **as-is** \u2014 none of the file coercion that Space and model nodes get runs for `fn` nodes. Three consequences:\n\n**Media ports must be declared explicitly in `workflow.json`.** Signature inference gives every bound function `text`/`number`/`boolean` ports and a single output, so an image-producing node has to be typed by hand:\n\n```json\n{\n \"id\": \"op_illustrate\", \"label\": \"Illustrate\", \"role\": \"operator\",\n \"kind\": \"fn\", \"fn\": \"illustrate\",\n \"inputs\": [{\"id\": \"prompt\", \"label\": \"Prompt\", \"type\": \"text\", \"required\": true}],\n \"outputs\": [{\"id\": \"out_0\", \"label\": \"Illustration\", \"type\": \"image\"}]\n}\n```\n\n**Media arguments arrive as dicts, not paths.** An image uploaded in the current tab arrives as `{\"path\": ..., \"url\": ...}`, but one chained from an upstream node arrives as `{\"url\": \"/gradio_api/file=\"}` with no `path` key at all. Normalize both before opening the file", "heading1": "gr.Workflow on ZeroGPU", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": " as `{\"path\": ..., \"url\": ...}`, but one chained from an upstream node arrives as `{\"url\": \"/gradio_api/file=\"}` with no `path` key at all. Normalize both before opening the file:\n\n```python\nfrom urllib.parse import unquote\n\ndef local_path(value):\n if isinstance(value, str):\n return value\n if path := value.get(\"path\"):\n return path\n url = value.get(\"url\") or \"\"\n return unquote(url.split(\"/gradio_api/file=\", 1)[-1])\n```\n\n**Media returns must be a file dict.** Returning a `PIL.Image` fails to serialize, and returning a bare path renders as a string. The canvas renders media only from a dict carrying a `url`:\n\n```python\nfrom gradio_client import utils as client_utils\n\nreturn {\n \"path\": path,\n \"url\": f\"/gradio_api/file={client_utils.encode_file_path(path)}\",\n \"is_file\": True,\n}\n```\n\n`launch()` already adds the system temp directory to `allowed_paths`, so writing to `tempfile.gettempdir()` works with no extra configuration. A file written anywhere else \u2014 a `./outputs` folder, say \u2014 needs its directory passed explicitly, or the URL 403s:\n\n```python\ngr.Workflow(graph=\"workflow.json\", bind=[illustrate]).launch(allowed_paths=[\"outputs\"])\n```\n\nReference app\n\nA complete chained-GPU workflow: an uploaded photo is captioned by one GPU function node, and the caption it produces is the prompt for a second. It shows module-scope loading, per-node `duration` sizing, explicitly typed media ports, and both media conversions above in one file.\n\n$demo_workflow_zerogpu_chain\n\nThe source is [on the Space](https://huggingface.co/spaces/gradio/workflow_zerogpu_chain/tree/main).\n\n", "heading1": "gr.Workflow on ZeroGPU", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Every Workflow app also renders as an ordinary Gradio app. Each independent pipeline is laid out like a `gr.Interface` \u2014 its unconnected inputs and a **Run** button on the left, its outputs on the right \u2014 and workflows with more than one pipeline get one tab each. Reference nodes that already hold a value show up as the starting value of their input component.\n\nAn **App / Workflow** toggle in the corner switches between the two views. Visitors with write access land on the canvas; everyone else lands on the app and can open the canvas read-only from the toggle. Append `?ui=app` or `?ui=canvas` to the URL to force a view, which is useful when sharing a link to a deployed workflow.\n\nThe app view is built from the same components that back the workflow's API endpoints, so it stays in sync: saving an edit on the canvas rebuilds both.\n\n", "heading1": "App view", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "Every Workflow app is a Gradio app, meaning that it exposes its connected pipelines through the standard Gradio REST API. Each disconnected pipeline containing one or more output (subject) nodes gets one endpoint. Its name is derived from the first subject's label \u2014 for example, a pipeline whose first subject is labelled \"Output Image\" becomes `/output_image`.\n\nUncomputed reference nodes feeding that pipeline become the endpoint's parameters. If the pipeline has multiple subjects, the endpoint returns all of them in subject declaration order rather than creating one endpoint per subject. Use `client.view_api()` to see the exact endpoint names, parameters, and return values:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"your-username/my-workflow\")\nclient.view_api() lists available endpoints and their parameters\n\nresult = client.predict(\"a sunset over mountains\", api_name=\"/output_image\")\n```\n\nThis also means that you can reuse your workflows within larger workflows, making it possible to build modular and complex applications with Gradio Workflows!\n", "heading1": "API access", "source_page_url": "https://gradio.app/guides/workflows", "source_page_title": "Additional Features - Workflows Guide"}, {"text": "By default, each event listener has its own queue, which handles one request at a time. This can be configured via two arguments:\n\n- `concurrency_limit`: This sets the maximum number of concurrent executions for an event listener. By default, the limit is 1 unless configured otherwise in `Blocks.queue()`. You can also set it to `None` for no limit (i.e., an unlimited number of concurrent executions). For example:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n prompt = gr.Textbox()\n image = gr.Image()\n generate_btn = gr.Button(\"Generate Image\")\n generate_btn.click(image_gen, prompt, image, concurrency_limit=5)\n```\n\nIn the code above, up to 5 requests can be processed simultaneously for this event listener. Additional requests will be queued until a slot becomes available.\n\nIf you want to manage multiple event listeners using a shared queue, you can use the `concurrency_id` argument:\n\n- `concurrency_id`: This allows event listeners to share a queue by assigning them the same ID. For example, if your setup has only 2 GPUs but multiple functions require GPU access, you can create a shared queue for all those functions. Here's how that might look:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n prompt = gr.Textbox()\n image = gr.Image()\n generate_btn_1 = gr.Button(\"Generate Image via model 1\")\n generate_btn_2 = gr.Button(\"Generate Image via model 2\")\n generate_btn_3 = gr.Button(\"Generate Image via model 3\")\n generate_btn_1.click(image_gen_1, prompt, image, concurrency_limit=2, concurrency_id=\"gpu_queue\")\n generate_btn_2.click(image_gen_2, prompt, image, concurrency_id=\"gpu_queue\")\n generate_btn_3.click(image_gen_3, prompt, image, concurrency_id=\"gpu_queue\")\n```\n\nIn this example, all three event listeners share a queue identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, se", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": " identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, set `concurrency_limit=None`. This is useful if your function is calling e.g. an external API which handles the rate limiting of requests itself.\n- The default concurrency limit for all queues can be set globally using the `default_concurrency_limit` parameter in `Blocks.queue()`. \n\nThese configurations make it easy to manage the queuing behavior of your Gradio app.\n", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": "Time plots need a datetime column on the x-axis. Here's a simple example with some flight data:\n\n$code_plot_guide_temporal\n$demo_plot_guide_temporal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You may wish to bin data by time buckets. Use `x_bin` to do so, using a string suffix with \"s\", \"m\", \"h\" or \"d\", such as \"15m\" or \"1d\".\n\n$code_plot_guide_aggregate_temporal\n$demo_plot_guide_aggregate_temporal\n\n", "heading1": "Aggregating by Time", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You can use `gr.DateTime` to accept input datetime data. This works well with plots for defining the x-axis range for the data.\n\n$code_plot_guide_datetime\n$demo_plot_guide_datetime\n\nNote how `gr.DateTime` can accept a full datetime string, or a shorthand using `now - [0-9]+[smhd]` format to refer to a past time.\n\nYou will often have many time plots in which case you'd like to keep the x-axes in sync. The `DateTimeRange` custom component keeps a set of datetime plots in sync, and also uses the `.select` listener of plots to allow you to zoom into plots while keeping plots in sync. \n\nBecause it is a custom component, you first need to `pip install gradio_datetimerange`. Then run the following:\n\n$code_plot_guide_datetimerange\n$demo_plot_guide_datetimerange\n\nTry zooming around in the plots and see how DateTimeRange updates. All the plots updates their `x_lim` in sync. You also have a \"Back\" link in the component to allow you to quickly zoom in and out.\n\n", "heading1": "DateTime Components", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "In many cases, you're working with live, realtime date, not a static dataframe. In this case, you'd update the plot regularly with a `gr.Timer()`. Assuming there's a `get_data` method that gets the latest dataframe:\n\n```python\nwith gr.Blocks() as demo:\n timer = gr.Timer(5)\n plot1 = gr.BarPlot(x=\"time\", y=\"price\")\n plot2 = gr.BarPlot(x=\"time\", y=\"price\", color=\"origin\")\n\n timer.tick(lambda: [get_data(), get_data()], outputs=[plot1, plot2])\n```\n\nYou can also use the `every` shorthand to attach a `Timer` to a component that has a function value:\n\n```python\nwith gr.Blocks() as demo:\n timer = gr.Timer(5)\n plot1 = gr.BarPlot(get_data, x=\"time\", y=\"price\", every=timer)\n plot2 = gr.BarPlot(get_data, x=\"time\", y=\"price\", color=\"origin\", every=timer)\n```\n\n\n", "heading1": "RealTime Data", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n gr.LinePlot(pd.read_sql_query(\"SELECT time, price from flight_info;\", engine), x=\"time\", y=\"price\")\n```\n\nLet's see a a more interactive plot involving filters that modify your SQL query:\n\n```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n origin = gr.Dropdown([\"DFW\", \"DAL\", \"HOU\"], value=\"DFW\", label=\"Origin\")\n\n gr.LinePlot(lambda origin: pd.read_sql_query(f\"SELECT time, price from flight_info WHERE origin = {origin};\", engine), inputs=origin, x=\"time\", y=\"price\")\n```\n\n", "heading1": "SQLite", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "If you're using a different database format, all you have to do is swap out the engine, e.g.\n\n```python\nengine = create_engine('postgresql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('mysql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('oracle://username:password@host:port/database_name')\n```", "heading1": "Postgres, mySQL, and other databases", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "Plots accept a pandas Dataframe as their value. The plot also takes `x` and `y` which represent the names of the columns that represent the x and y axes respectively. Here's a simple example:\n\n$code_plot_guide_line\n$demo_plot_guide_line\n\nAll plots have the same API, so you could swap this out with a `gr.ScatterPlot`:\n\n$code_plot_guide_scatter\n$demo_plot_guide_scatter\n\nThe y axis column in the dataframe should have a numeric type, but the x axis column can be anything from strings, numbers, categories, or datetimes.\n\n$code_plot_guide_scatter_nominal\n$demo_plot_guide_scatter_nominal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can break out your plot into series using the `color` argument.\n\n$code_plot_guide_series_nominal\n$demo_plot_guide_series_nominal\n\nIf you wish to assign series specific colors, use the `color_map` arg, e.g. `gr.ScatterPlot(..., color_map={'white': 'FF9988', 'asian': '88EEAA', 'black': '333388'})`\n\nThe color column can be numeric type as well.\n\n$code_plot_guide_series_quantitative\n$demo_plot_guide_series_quantitative\n\n", "heading1": "Breaking out Series by Color", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can aggregate values into groups using the `x_bin` and `y_aggregate` arguments. If your x-axis is numeric, providing an `x_bin` will create a histogram-style binning:\n\n$code_plot_guide_aggregate_quantitative\n$demo_plot_guide_aggregate_quantitative\n\nIf your x-axis is a string type instead, they will act as the category bins automatically:\n\n$code_plot_guide_aggregate_nominal\n$demo_plot_guide_aggregate_nominal\n\n", "heading1": "Aggregating Values", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can use the `.select` listener to select regions of a plot. Click and drag on the plot below to select part of the plot.\n\n$code_plot_guide_selection\n$demo_plot_guide_selection\n\nYou can combine this and the `.double_click` listener to create some zoom in/out effects by changing `x_lim` which sets the bounds of the x-axis:\n\n$code_plot_guide_zoom\n$demo_plot_guide_zoom\n\nIf you had multiple plots with the same x column, your event listeners could target the x limits of all other plots so that the x-axes stay in sync.\n\n$code_plot_guide_zoom_sync\n$demo_plot_guide_zoom_sync\n\n", "heading1": "Selecting Regions", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "Take a look how you can have an interactive dashboard where the plots are functions of other Components.\n\n$code_plot_guide_interactive\n$demo_plot_guide_interactive\n\nIt's that simple to filter and control the data presented in your visualization!", "heading1": "Making an Interactive Dashboard", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "Use any of the standard Gradio form components to filter your data. You can do this via event listeners or function-as-value syntax. Let's look at the event listener approach first:\n\n$code_plot_guide_filters_events\n$demo_plot_guide_filters_events\n\nAnd this would be the function-as-value approach for the same demo.\n\n$code_plot_guide_filters\n\n", "heading1": "Filters", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}, {"text": "Add `gr.DataFrame` and `gr.Label` to your dashboard for some hard numbers.\n\n$code_plot_guide_tables_stats\n$demo_plot_guide_tables_stats\n", "heading1": "Tables and Stats", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}]