Datasets:

huggingface
/

agent-usage

"""Chart + table helpers for the Hub agent-usage leaderboard.
Used by build_local.py (the scheduled HF Job) to render the PNGs embedded in
the dataset card. This module only ever touches the *derived* public data
(relative shares); fetching + deriving from the private source lives in
build_local.py.
"""

from __future__ import annotations

from pathlib import Path

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import rcParams
from matplotlib.figure import Figure

# --------------------------------------------------------------------------- #
# Constants
# --------------------------------------------------------------------------- #

# Published derived dataset (used when no local data dir is given).
DERIVED_REPO = "huggingface/agent-usage"

ROLLOUT_DATE = "2026-04-03"  # agent/ UA token rollout — before this is noise

# HF brand palette — https://huggingface.co/brand
YELLOW = "#FFD21E"   # primary accent — leader bar
ORANGE = "#FF9D00"   # secondary accent
SLATE = "#1F2937"    # near-black bars / first line
INK = "#111827"      # body text
MUTED = "#6B7280"    # HF gray — axes, footers
GRID = "#E5E7EB"     # light gridline
BG = "#FFFFFF"

# Stable categorical line colours (assigned by agent order).
LINE_COLORS = [
    SLATE, ORANGE, "#2563EB", "#059669", "#DC2626",
    "#7C3AED", "#0891B2", "#CA8A04",
]


def set_brand_style() -> None:
    """Apply HF-flavoured matplotlib defaults (clean sans-serif, white bg)."""
    rcParams.update(
        {
            "font.family": "sans-serif",
            "font.sans-serif": [
                "Source Sans Pro", "Source Sans 3",
                "Helvetica Neue", "Helvetica", "Arial", "DejaVu Sans",
            ],
            "font.size": 11,
            "text.color": INK,
            "savefig.facecolor": BG,
            "axes.facecolor": BG,
            "figure.facecolor": BG,
        }
    )


# --------------------------------------------------------------------------- #
# Selectors
# --------------------------------------------------------------------------- #

def available_months(monthly: pd.DataFrame) -> list[str]:
    return sorted(monthly["month"].dropna().unique().tolist())


def latest_month(monthly: pd.DataFrame) -> str:
    return max(available_months(monthly))


def leaderboard_table(
    monthly: pd.DataFrame,
    month: str | None = None,
    top_n: int = 10,
    exclude_unknown: bool = True,
) -> pd.DataFrame:
    """Ranked top-N agents for a month, ready to display."""
    month = month or latest_month(monthly)
    df = monthly.query("month == @month")
    if exclude_unknown:
        df = df[df["agent"].str.lower() != "unknown"]
    df = df.sort_values("pct_requests", ascending=False).head(top_n).reset_index(drop=True)
    out = df[["agent", "pct_requests", "pct_users"]].copy()
    out.insert(0, "rank", range(1, len(out) + 1))
    out["pct_requests"] = out["pct_requests"].round(2)
    out["pct_users"] = out["pct_users"].astype("Float64").round(2)
    return out


def top_agents(
    monthly: pd.DataFrame,
    month: str | None = None,
    n: int = 5,
    exclude_unknown: bool = True,
) -> list[str]:
    """Top-N agent names by share in the given month (default: latest)."""
    return leaderboard_table(monthly, month, n, exclude_unknown)["agent"].tolist()


# --------------------------------------------------------------------------- #
# Figures
# --------------------------------------------------------------------------- #

def leaderboard_figure(
    monthly: pd.DataFrame,
    month: str | None = None,
    top_n: int = 10,
    show_unknown: bool = True,
) -> Figure:
    """Horizontal-bar snapshot leaderboard, HF-brand register.
    Named harnesses are ranked as usual; `unknown` (unregistered tools) sits as
    a muted gray bar at the bottom — an honest part of the picture and the
    reason to register a harness, without reading as part of the race.
    """
    set_brand_style()
    month = month or latest_month(monthly)
    table = leaderboard_table(monthly, month, top_n, exclude_unknown=True)

    fig = Figure(figsize=(9.2, 9.2), facecolor=BG)
    fig.text(0.06, 0.935, "Agents calling the Hugging Face Hub",
             fontsize=22, color=INK, fontweight="bold")
    fig.text(0.06, 0.895, f"Share of agent-attributed huggingface_hub requests, {month}",
             fontsize=13, color=MUTED)

    ax = fig.add_axes((0.06, 0.10, 0.88, 0.74))
    # rows bottom-to-top: (label, pct, kind) with unknown pinned at the bottom
    rows = [(r["agent"], float(r["pct_requests"]), "named")
            for _, r in table.iloc[::-1].iterrows()]
    if show_unknown:
        unk = monthly.query("month == @month and agent == 'unknown'")
        if len(unk):
            rows.insert(0, ("unknown (unregistered)", float(unk["pct_requests"].iloc[0]), "unknown"))
    if not rows:
        ax.text(0.5, 0.5, "no data", ha="center", va="center", color=MUTED)
        return fig
    xmax = max(pct for _, pct, _ in rows)
    ax.axvline(0, color=MUTED, linewidth=0.6, zorder=1)

    top_idx = len(rows) - 1  # top *named* harness leads
    for i, (label, pct, kind) in enumerate(rows):
        is_unknown = kind == "unknown"
        is_top = i == top_idx
        bar_colour = GRID if is_unknown else (YELLOW if is_top else SLATE)
        ax.barh(i, pct, color=bar_colour, height=0.62, zorder=2)
        ax.text(-xmax * 0.022, i, label, va="center", ha="right",
                fontsize=12 if is_unknown else 14,
                fontstyle="italic" if is_unknown else "normal",
                fontweight="bold" if is_top else "normal",
                color=MUTED if is_unknown else INK)
        ax.text(pct + xmax * 0.012, i, f"{pct:.1f}%", va="center", ha="left",
                fontsize=14, fontweight="bold",
                color=MUTED if is_unknown else (ORANGE if is_top else INK))

    ax.set_yticks([]); ax.set_xticks([])
    for s in ("top", "right", "left", "bottom"):
        ax.spines[s].set_visible(False)
    ax.set_xlim(-xmax * 0.50, xmax * 1.15)
    ax.set_ylim(-0.7, len(rows) - 0.3)

    fig.text(0.06, 0.040,
             f"Source: hf.co/datasets/{DERIVED_REPO}  ·  self-declared agent/ User-Agent tokens.",
             fontsize=10, color=MUTED)
    return fig


def trend_figure(
    daily: pd.DataFrame,
    agents: list[str],
    smooth: bool = True,
    since: str = ROLLOUT_DATE,
) -> Figure:
    """Daily share-over-time lines for the given agents, HF-brand register.
    ``smooth`` applies a 7-day centred rolling mean (min_periods=3) to absorb
    weekend dips and small-denominator noise in the early days.
    """
    set_brand_style()
    df = daily[daily["day"] >= since].copy()
    df = df[df["agent"].isin(agents)]
    df["day"] = pd.to_datetime(df["day"])

    fig = Figure(figsize=(11, 6.2), facecolor=BG)
    fig.text(0.06, 0.93, "Agent share of Hub requests over time",
             fontsize=20, color=INK, fontweight="bold")
    sub = "7-day rolling mean" if smooth else "raw daily share"
    fig.text(0.06, 0.885, f"huggingface_hub  ·  {sub}  ·  since {since}",
             fontsize=12.5, color=MUTED)

    ax = fig.add_axes((0.06, 0.13, 0.80, 0.70))
    if df.empty:
        ax.text(0.5, 0.5, "no data", ha="center", va="center", color=MUTED)
        return fig

    ymax = 0.0
    labels = []  # (y_at_end, agent, colour) for de-collided right-hand labels
    last_x = None
    for idx, agent in enumerate(agents):
        s = (
            df[df["agent"] == agent]
            .sort_values("day")
            .set_index("day")["pct_requests"]
            .astype(float)
        )
        if s.empty:
            continue
        if smooth:
            s = s.rolling(7, center=True, min_periods=3).mean()
        colour = LINE_COLORS[idx % len(LINE_COLORS)]
        ax.plot(s.index, s.values, color=colour, linewidth=2.2, zorder=3)
        valid = s.dropna()
        if not valid.empty:
            labels.append([float(valid.iloc[-1]), agent, colour])
            last_x = valid.index[-1]
            ymax = max(ymax, float(valid.max()))

    ax.set_ylim(0, ymax * 1.12 if ymax else 1)

    # De-collide right-hand direct labels: nudge apart by a min vertical gap
    if labels and last_x is not None:
        gap = (ymax * 1.12) * 0.045  # ~4.5% of axis height
        labels.sort(key=lambda r: r[0])
        for prev, cur in zip(labels, labels[1:]):
            if cur[0] - prev[0] < gap:
                cur[0] = prev[0] + gap
        for y, agent, colour in labels:
            ax.annotate(
                f" {agent}",
                xy=(last_x, y), xytext=(6, 0), textcoords="offset points",
                va="center", ha="left", fontsize=11.5, color=colour,
                fontweight="bold", annotation_clip=False,
            )
    ax.yaxis.set_major_formatter(lambda v, _: f"{v:.0f}%")
    ax.grid(axis="y", color=GRID, linewidth=0.8)
    ax.set_axisbelow(True)
    for s in ("top", "right"):
        ax.spines[s].set_visible(False)
    for s in ("left", "bottom"):
        ax.spines[s].set_color(MUTED)
    ax.tick_params(colors=MUTED, labelsize=10)
    ax.xaxis.set_major_locator(mdates.AutoDateLocator())
    ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))
    # room for the right-hand direct labels
    ax.margins(x=0.02)

    fig.text(0.06, 0.035,
             f"Source: hf.co/datasets/{DERIVED_REPO}  ·  share of agent-attributed huggingface_hub requests.",
             fontsize=10, color=MUTED)
    return fig


def save_png(fig: Figure, path: str | Path) -> Path:
    path = Path(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(path, dpi=200, bbox_inches="tight", pad_inches=0.4, facecolor=BG)
    plt.close(fig)
    return path

	"""Chart + table helpers for the Hub agent-usage leaderboard.

	Used by build_local.py (the scheduled HF Job) to render the PNGs embedded in
	the dataset card. This module only ever touches the derived public data
	(relative shares); fetching + deriving from the private source lives in
	build_local.py.
	"""

	from __future__ import annotations

	from pathlib import Path

	import matplotlib.dates as mdates
	import matplotlib.pyplot as plt
	import pandas as pd
	from matplotlib import rcParams
	from matplotlib.figure import Figure

	# --------------------------------------------------------------------------- #
	# Constants
	# --------------------------------------------------------------------------- #

	# Published derived dataset (used when no local data dir is given).
	DERIVED_REPO = "huggingface/agent-usage"

	ROLLOUT_DATE = "2026-04-03" # agent/ UA token rollout — before this is noise

	# HF brand palette — https://huggingface.co/brand
	YELLOW = "#FFD21E" # primary accent — leader bar
	ORANGE = "#FF9D00" # secondary accent
	SLATE = "#1F2937" # near-black bars / first line
	INK = "#111827" # body text
	MUTED = "#6B7280" # HF gray — axes, footers
	GRID = "#E5E7EB" # light gridline
	BG = "#FFFFFF"

	# Stable categorical line colours (assigned by agent order).
	LINE_COLORS = [
	SLATE, ORANGE, "#2563EB", "#059669", "#DC2626",
	"#7C3AED", "#0891B2", "#CA8A04",
	]


	def set_brand_style() -> None:
	"""Apply HF-flavoured matplotlib defaults (clean sans-serif, white bg)."""
	rcParams.update(
	{
	"font.family": "sans-serif",
	"font.sans-serif": [
	"Source Sans Pro", "Source Sans 3",
	"Helvetica Neue", "Helvetica", "Arial", "DejaVu Sans",
	],
	"font.size": 11,
	"text.color": INK,
	"savefig.facecolor": BG,
	"axes.facecolor": BG,
	"figure.facecolor": BG,
	}
	)


	# --------------------------------------------------------------------------- #
	# Selectors
	# --------------------------------------------------------------------------- #

	def available_months(monthly: pd.DataFrame) -> list[str]:
	return sorted(monthly["month"].dropna().unique().tolist())


	def latest_month(monthly: pd.DataFrame) -> str:
	return max(available_months(monthly))


	def leaderboard_table(
	monthly: pd.DataFrame,
	month: str \| None = None,
	top_n: int = 10,
	exclude_unknown: bool = True,
	) -> pd.DataFrame:
	"""Ranked top-N agents for a month, ready to display."""
	month = month or latest_month(monthly)
	df = monthly.query("month == @month")
	if exclude_unknown:
	df = df[df["agent"].str.lower() != "unknown"]
	df = df.sort_values("pct_requests", ascending=False).head(top_n).reset_index(drop=True)
	out = df[["agent", "pct_requests", "pct_users"]].copy()
	out.insert(0, "rank", range(1, len(out) + 1))
	out["pct_requests"] = out["pct_requests"].round(2)
	out["pct_users"] = out["pct_users"].astype("Float64").round(2)
	return out


	def top_agents(
	monthly: pd.DataFrame,
	month: str \| None = None,
	n: int = 5,
	exclude_unknown: bool = True,
	) -> list[str]:
	"""Top-N agent names by share in the given month (default: latest)."""
	return leaderboard_table(monthly, month, n, exclude_unknown)["agent"].tolist()


	# --------------------------------------------------------------------------- #
	# Figures
	# --------------------------------------------------------------------------- #

	def leaderboard_figure(
	monthly: pd.DataFrame,
	month: str \| None = None,
	top_n: int = 10,
	show_unknown: bool = True,
	) -> Figure:
	"""Horizontal-bar snapshot leaderboard, HF-brand register.

	Named harnesses are ranked as usual; `unknown` (unregistered tools) sits as
	a muted gray bar at the bottom — an honest part of the picture and the
	reason to register a harness, without reading as part of the race.
	"""
	set_brand_style()
	month = month or latest_month(monthly)
	table = leaderboard_table(monthly, month, top_n, exclude_unknown=True)

	fig = Figure(figsize=(9.2, 9.2), facecolor=BG)
	fig.text(0.06, 0.935, "Agents calling the Hugging Face Hub",
	fontsize=22, color=INK, fontweight="bold")
	fig.text(0.06, 0.895, f"Share of agent-attributed huggingface_hub requests, {month}",
	fontsize=13, color=MUTED)

	ax = fig.add_axes((0.06, 0.10, 0.88, 0.74))
	# rows bottom-to-top: (label, pct, kind) with unknown pinned at the bottom
	rows = [(r["agent"], float(r["pct_requests"]), "named")
	for _, r in table.iloc[::-1].iterrows()]
	if show_unknown:
	unk = monthly.query("month == @month and agent == 'unknown'")
	if len(unk):
	rows.insert(0, ("unknown (unregistered)", float(unk["pct_requests"].iloc[0]), "unknown"))
	if not rows:
	ax.text(0.5, 0.5, "no data", ha="center", va="center", color=MUTED)
	return fig
	xmax = max(pct for _, pct, _ in rows)
	ax.axvline(0, color=MUTED, linewidth=0.6, zorder=1)

	top_idx = len(rows) - 1 # top named harness leads
	for i, (label, pct, kind) in enumerate(rows):
	is_unknown = kind == "unknown"
	is_top = i == top_idx
	bar_colour = GRID if is_unknown else (YELLOW if is_top else SLATE)
	ax.barh(i, pct, color=bar_colour, height=0.62, zorder=2)
	ax.text(-xmax * 0.022, i, label, va="center", ha="right",
	fontsize=12 if is_unknown else 14,
	fontstyle="italic" if is_unknown else "normal",
	fontweight="bold" if is_top else "normal",
	color=MUTED if is_unknown else INK)
	ax.text(pct + xmax * 0.012, i, f"{pct:.1f}%", va="center", ha="left",
	fontsize=14, fontweight="bold",
	color=MUTED if is_unknown else (ORANGE if is_top else INK))

	ax.set_yticks([]); ax.set_xticks([])
	for s in ("top", "right", "left", "bottom"):
	ax.spines[s].set_visible(False)
	ax.set_xlim(-xmax * 0.50, xmax * 1.15)
	ax.set_ylim(-0.7, len(rows) - 0.3)

	fig.text(0.06, 0.040,
	f"Source: hf.co/datasets/{DERIVED_REPO} · self-declared agent/ User-Agent tokens.",
	fontsize=10, color=MUTED)
	return fig


	def trend_figure(
	daily: pd.DataFrame,
	agents: list[str],
	smooth: bool = True,
	since: str = ROLLOUT_DATE,
	) -> Figure:
	"""Daily share-over-time lines for the given agents, HF-brand register.

	``smooth`` applies a 7-day centred rolling mean (min_periods=3) to absorb
	weekend dips and small-denominator noise in the early days.
	"""
	set_brand_style()
	df = daily[daily["day"] >= since].copy()
	df = df[df["agent"].isin(agents)]
	df["day"] = pd.to_datetime(df["day"])

	fig = Figure(figsize=(11, 6.2), facecolor=BG)
	fig.text(0.06, 0.93, "Agent share of Hub requests over time",
	fontsize=20, color=INK, fontweight="bold")
	sub = "7-day rolling mean" if smooth else "raw daily share"
	fig.text(0.06, 0.885, f"huggingface_hub · {sub} · since {since}",
	fontsize=12.5, color=MUTED)

	ax = fig.add_axes((0.06, 0.13, 0.80, 0.70))
	if df.empty:
	ax.text(0.5, 0.5, "no data", ha="center", va="center", color=MUTED)
	return fig

	ymax = 0.0
	labels = [] # (y_at_end, agent, colour) for de-collided right-hand labels
	last_x = None
	for idx, agent in enumerate(agents):
	s = (
	df[df["agent"] == agent]
	.sort_values("day")
	.set_index("day")["pct_requests"]
	.astype(float)
	)
	if s.empty:
	continue
	if smooth:
	s = s.rolling(7, center=True, min_periods=3).mean()
	colour = LINE_COLORS[idx % len(LINE_COLORS)]
	ax.plot(s.index, s.values, color=colour, linewidth=2.2, zorder=3)
	valid = s.dropna()
	if not valid.empty:
	labels.append([float(valid.iloc[-1]), agent, colour])
	last_x = valid.index[-1]
	ymax = max(ymax, float(valid.max()))

	ax.set_ylim(0, ymax * 1.12 if ymax else 1)

	# De-collide right-hand direct labels: nudge apart by a min vertical gap
	if labels and last_x is not None:
	gap = (ymax * 1.12) * 0.045 # ~4.5% of axis height
	labels.sort(key=lambda r: r[0])
	for prev, cur in zip(labels, labels[1:]):
	if cur[0] - prev[0] < gap:
	cur[0] = prev[0] + gap
	for y, agent, colour in labels:
	ax.annotate(
	f" {agent}",
	xy=(last_x, y), xytext=(6, 0), textcoords="offset points",
	va="center", ha="left", fontsize=11.5, color=colour,
	fontweight="bold", annotation_clip=False,
	)
	ax.yaxis.set_major_formatter(lambda v, _: f"{v:.0f}%")
	ax.grid(axis="y", color=GRID, linewidth=0.8)
	ax.set_axisbelow(True)
	for s in ("top", "right"):
	ax.spines[s].set_visible(False)
	for s in ("left", "bottom"):
	ax.spines[s].set_color(MUTED)
	ax.tick_params(colors=MUTED, labelsize=10)
	ax.xaxis.set_major_locator(mdates.AutoDateLocator())
	ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))
	# room for the right-hand direct labels
	ax.margins(x=0.02)

	fig.text(0.06, 0.035,
	f"Source: hf.co/datasets/{DERIVED_REPO} · share of agent-attributed huggingface_hub requests.",
	fontsize=10, color=MUTED)
	return fig


	def save_png(fig: Figure, path: str \| Path) -> Path:
	path = Path(path)
	path.parent.mkdir(parents=True, exist_ok=True)
	fig.savefig(path, dpi=200, bbox_inches="tight", pad_inches=0.4, facecolor=BG)
	plt.close(fig)
	return path