# /// script # 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)")