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# requires-python = ">=3.11"
# dependencies = [
# "huggingface_hub>=0.27",
# "pandas>=2.0",
# "pyarrow>=15",
# "matplotlib>=3.8",
# ]
# ///
"""Build the public-safe derived view of the Hub's agent-usage telemetry.
The source dataset id is injected via the AGENT_USAGE_SRC env var (on Jobs:
`-e AGENT_USAGE_SRC=...`), so this public script never names it.
This is the scheduled HF Job body. It:
1. fetches the private source (monthly + daily parquets)
2. keeps only the public columns (relative shares; raw counts are never
published) and only registered harness rows (the rest fold into `unknown`)
3. writes derived parquets, renders the card PNGs, and pushes everything
(data + charts + card + this script) to the public dataset repo
"""
import os
from datetime import date
from pathlib import Path
import pandas as pd
from huggingface_hub import snapshot_download
from huggingface_hub.constants import ENDPOINT
from huggingface_hub.utils import get_session
try:
import agent_usage as au
except ModuleNotFoundError:
# Scheduled Jobs run this script by URL, without the sibling module on disk:
# fetch agent_usage.py from the same dataset repo the job pushes to.
import sys
from huggingface_hub import hf_hub_download
_mod = hf_hub_download(
os.environ["AGENT_USAGE_PUSH_REPO"], "agent_usage.py", repo_type="dataset"
)
sys.path.insert(0, str(Path(_mod).parent))
import agent_usage as au
SRC = os.environ.get("AGENT_USAGE_SRC")
if not SRC:
raise SystemExit("Set AGENT_USAGE_SRC to the source dataset id (on Jobs: -e AGENT_USAGE_SRC=...)")
# Allowlist: only these columns are ever published. Anything else in the source
# (whatever it is, now or later) is dropped by construction.
KEEP = {"month", "day", "agent", "pct_requests", "pct_users"}
# Publish the library-level rows only: the CLI source is a strict subset of
# huggingface_hub (the CLI imports the library), so keeping both would double-count.
PUBLISH_SOURCE = "huggingface_hub"
ROLLOUT_MONTH = "2026-04" # skip pre-rollout monthly files (mostly empty)
# The `agent` value is extracted server-side from raw User-Agent strings, so it
# is arbitrary user-controlled input. Sanitize rows the same way KEEP sanitizes
# columns: only registered harness names are published; everything else folds
# into `unknown` (which is also what the card promises for unregistered tools).
# Fail loud if the registry is unreachable — never publish unfiltered tokens.
_resp = get_session().get(f"{ENDPOINT}/api/agent-harnesses", timeout=10)
_resp.raise_for_status()
PUBLISH_AGENTS = set(_resp.json()["harnesses"]) | {"unknown"}
HERE = Path(__file__).parent
OUT = HERE / "preview"
DATA = OUT / "data"
def derive(folder: str, key_col: str) -> pd.DataFrame:
"""Pull one granularity from the source, drop totals, write derived parquets."""
src_root = Path(
snapshot_download(SRC, repo_type="dataset", allow_patterns=f"data/{folder}/*.parquet")
)
out_dir = DATA / folder
out_dir.mkdir(parents=True, exist_ok=True)
frames = []
for parquet in sorted((src_root / "data" / folder).glob("*.parquet")):
# monthly: skip files before rollout; daily: keep all (chart filters by day)
if folder == "monthly" and parquet.stem < ROLLOUT_MONTH:
continue
df = pd.read_parquet(parquet)
if key_col not in df.columns:
df[key_col] = parquet.stem
if "source" in df.columns:
df = df[df["source"] == PUBLISH_SOURCE]
df = df[[c for c in df.columns if c in KEEP]]
# Fold unregistered tokens into `unknown`, then merge the folded rows.
df.loc[~df["agent"].isin(PUBLISH_AGENTS), "agent"] = "unknown"
group_cols = [c for c in df.columns if not c.startswith("pct_")]
df = df.groupby(group_cols, as_index=False).sum()
df.to_parquet(out_dir / parquet.name, index=False)
frames.append(df)
if not frames:
raise SystemExit(f"No {folder} parquets found")
all_df = pd.concat(frames, ignore_index=True)
# Fail loud if the published frame somehow holds a non-allowlisted column,
# or if the source schema drifted away from the shares we expect.
extras = set(all_df.columns) - KEEP
if extras:
raise SystemExit(f"Leak: non-allowlisted columns survived in {folder}: {extras}")
if not {key_col, "agent", "pct_requests"} <= set(all_df.columns):
raise SystemExit(f"Source schema drift: expected share columns missing in {folder}")
print(f"✓ {folder}: {len(frames)} parquets → {out_dir}/ ({len(all_df)} rows)")
return all_df
# ---------------------------- derive both granularities ----------------------
monthly = derive("monthly", "month")
daily = derive("daily", "day")
# pct sanity check (already 0-100 in source)
gs = monthly.groupby("month")["pct_requests"].sum()
if not ((gs > 95) & (gs < 105)).all():
print(f"⚠ monthly pct_requests group sums look off: {gs.to_dict()}")
latest = au.latest_month(monthly)
leaders = au.top_agents(monthly, n=5)
print(f"\nLatest month: {latest}")
print(au.leaderboard_table(monthly).to_string(index=False))
print(f"\nTrend cohort (top 5, {latest}): {leaders}")
# ---------------------------- render PNGs (for tweet / card embedding) -------
au.save_png(au.leaderboard_figure(monthly), OUT / "leaderboard.png")
au.save_png(au.trend_figure(daily, agents=leaders), OUT / "trend.png")
print(f"\n✓ {OUT/'leaderboard.png'}")
print(f"✓ {OUT/'trend.png'}")
# ---------------------------- push to the public dataset repo ----------------
# Only when AGENT_USAGE_PUSH_REPO is set (on Jobs: -e AGENT_USAGE_PUSH_REPO=...).
# Everything above stays local otherwise. One commit per run: derived parquets,
# chart PNGs, the card, and this script + its module (the build is auditable
# from the dataset repo itself).
PUSH_REPO = os.environ.get("AGENT_USAGE_PUSH_REPO")
if PUSH_REPO:
from huggingface_hub import CommitOperationAdd, HfApi
api = HfApi()
# Created private: review the rendered card/viewer, then flip to public in
# settings. exist_ok means later scheduled runs never touch visibility.
api.create_repo(PUSH_REPO, repo_type="dataset", private=True, exist_ok=True)
# Idempotent: the job can run more often than the source updates (a new
# source snapshot means a new monthly parquet). Nothing new -> no commit.
if api.file_exists(
PUSH_REPO, f"data/monthly/{latest}.parquet", repo_type="dataset"
) and not os.environ.get("AGENT_USAGE_FORCE"):
print(
f"\n✓ {PUSH_REPO} already has {latest} — nothing new, skipping push "
"(AGENT_USAGE_FORCE=1 overrides)"
)
raise SystemExit(0)
ops = []
for parquet in sorted(DATA.rglob("*.parquet")):
ops.append(CommitOperationAdd(str(parquet.relative_to(OUT)), str(parquet)))
for png in ("leaderboard.png", "trend.png"):
ops.append(CommitOperationAdd(f"assets/{png}", str(OUT / png)))
# The build must stay auditable and self-contained from the dataset repo
# itself: on a scheduled Job only this script exists locally, so the module
# comes from wherever it was imported and the card template from the repo.
ops.append(CommitOperationAdd("build_local.py", str(Path(__file__).resolve())))
ops.append(CommitOperationAdd("agent_usage.py", au.__file__))
card_tpl = HERE / "card" / "README.md"
if not card_tpl.exists():
from huggingface_hub import hf_hub_download
card_tpl = Path(hf_hub_download(PUSH_REPO, "card/README.md", repo_type="dataset"))
ops.append(CommitOperationAdd("card/README.md", str(card_tpl)))
# Card template → README.md with the freshness stamp filled in.
card = (
card_tpl.read_text()
.replace("{{LATEST_MONTH}}", latest)
.replace("{{GENERATED}}", str(date.today()))
)
ops.append(CommitOperationAdd("README.md", card.encode()))
commit = api.create_commit(
repo_id=PUSH_REPO,
repo_type="dataset",
operations=ops,
commit_message=f"Update derived agent-usage shares (latest month: {latest})",
)
print(f"\n✓ pushed {len(ops)} files → {commit.commit_url}")
else:
print("\n(no AGENT_USAGE_PUSH_REPO set — local build only, nothing pushed)")
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