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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "pandas==2.2.2",
# "aiohttp",
# "python-dotenv==1.0.1",
# "huggingface-hub==0.24.3",
# "tenacity==9.0.0",
# "pyarrow==17.0.0",
# "requests",
# ]
# ///
import json
import os
import asyncio
import time
import pandas as pd
import aiohttp
import requests.utils
from dotenv import load_dotenv
from huggingface_hub import HfApi
from tenacity import retry, stop_after_attempt, wait_exponential
load_dotenv()
CACHE_DIR = ".hf_cache"
os.makedirs(CACHE_DIR, exist_ok=True)
api = HfApi()
USER_ID = api.whoami()["name"]
REPO_ID = f"{USER_ID}/hub-stats"
print(f"🚀 Hugging Face Hub Stats Collector")
print(f"📊 Dataset will be uploaded to: {REPO_ID}")
print(f"👤 User: {USER_ID}")
print("-" * 50)
ENDPOINT_CONFIGS = {
"models": {
"limit": 1000,
"params": {
"full": "true",
"config": "true",
"expand[]": [
"gguf",
"downloadsAllTime",
"transformersInfo",
"cardData",
"safetensors",
"baseModels",
"author",
"likes",
"inferenceProviderMapping",
"downloads",
"siblings",
"tags",
"pipeline_tag",
"lastModified",
"createdAt",
"config",
"library_name",
],
},
},
"datasets": {
"limit": 1000,
"params": {
"full": "true",
"expand[]": [
"author",
"cardData",
"citation",
"createdAt",
"disabled",
"description",
"downloads",
"downloadsAllTime",
"gated",
"lastModified",
"likes",
"paperswithcode_id",
"private",
"siblings",
"sha",
"tags",
"trendingScore",
],
},
},
"spaces": {"limit": 1000, "params": {"full": "true"}},
"posts": {"limit": 50, "params": {"skip": 0}},
"daily_papers": {
"limit": 50,
"params": {},
"base_url": "https://huggingface.co/api/daily_papers",
},
}
def parse_link_header(link_header):
if not link_header:
return None
links = requests.utils.parse_header_links(link_header)
for link in links:
if link.get("rel") == "next":
return link.get("url")
return None
def to_json_string(x):
return (
json.dumps(x)
if isinstance(x, (dict, list))
else str(x) if x is not None else None
)
def process_dataframe(df, endpoint):
if len(df) == 0:
return df
if endpoint == "posts":
if "author" in df.columns:
author_df = pd.json_normalize(df["author"])
author_cols = ["avatarUrl", "followerCount", "fullname", "name"]
for col in author_cols:
if col in author_df.columns:
df[col] = author_df[col]
df = df.drop("author", axis=1)
for ts_col in ["publishedAt", "updatedAt"]:
if ts_col in df.columns:
df[ts_col] = pd.to_datetime(df[ts_col]).dt.tz_localize(None)
elif endpoint == "daily_papers":
if "paper" in df.columns:
paper_df = pd.json_normalize(df["paper"], errors="ignore").add_prefix(
"paper_"
)
df = pd.concat([df.drop("paper", axis=1), paper_df], axis=1)
for ts_col in ["publishedAt", "paper_publishedAt"]:
if ts_col in df.columns:
df[ts_col] = pd.to_datetime(df[ts_col], errors="coerce").dt.tz_localize(
None
)
else:
for field in ["createdAt", "lastModified"]:
if field in df.columns:
df[field] = pd.to_datetime(df[field], errors="coerce").dt.tz_localize(
None
)
if "gated" in df.columns:
df["gated"] = df["gated"].astype(str)
for col in ["cardData", "config", "gguf"]:
if col in df.columns:
df[col] = df[col].apply(to_json_string)
return df
def save_parquet(df, output_file):
df.to_parquet(output_file, index=False, engine="pyarrow")
@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=4, max=60))
async def fetch_data_page(session, url, params=None, headers=None):
async with session.get(url, params=params, headers=headers) as response:
response.raise_for_status()
return await response.json(), response.headers.get("Link")
async def create_parquet_files(skip_upload=False):
start_time = time.time()
endpoints = ["daily_papers", "models", "spaces", "datasets", "posts"]
created_files = []
async with aiohttp.ClientSession() as session:
for endpoint in endpoints:
print(f"Processing {endpoint}...")
config = ENDPOINT_CONFIGS[endpoint]
base_url = config.get("base_url", f"https://huggingface.co/api/{endpoint}")
params = {"limit": config["limit"]}
params.update(config["params"])
headers = {"Accept": "application/json"}
all_data = []
url = base_url
page = 0
jsonl_file = None
if skip_upload:
jsonl_file = os.path.join(CACHE_DIR, f"{endpoint}_raw.jsonl")
with open(jsonl_file, "w") as f:
pass
while url:
if endpoint == "posts":
params["skip"] = page * params["limit"]
try:
data, link_header = await fetch_data_page(
session, url, params, headers
)
if skip_upload and jsonl_file:
with open(jsonl_file, "a") as f:
f.write(json.dumps(data) + "\n")
if endpoint == "posts":
items = data["socialPosts"]
total_items = data["numTotalItems"]
all_data.extend(items)
if (page + 1) * params["limit"] >= total_items:
url = None
else:
url = base_url
else:
all_data.extend(data)
url = parse_link_header(link_header)
if url:
params = {}
if len(all_data) % 10000 == 0:
print(f" {len(all_data):,} records processed")
page += 1
except Exception as e:
print(f"Error on page {page}: {e}")
await asyncio.sleep(2)
if page > 0:
url = None
else:
raise
if skip_upload and jsonl_file and os.path.exists(jsonl_file):
print(f" Raw data saved to {jsonl_file}")
df = pd.DataFrame(all_data)
df = process_dataframe(df, endpoint)
output_file = os.path.join(CACHE_DIR, f"{endpoint}.parquet")
save_parquet(df, output_file)
created_files.append(output_file)
print(f"✓ {endpoint}: {len(df):,} rows -> {output_file}")
if not skip_upload:
upload_to_hub(output_file, REPO_ID)
elapsed = time.time() - start_time
return created_files, elapsed
def recreate_from_jsonl():
endpoints = ["daily_papers", "models", "spaces", "datasets", "posts"]
for endpoint in endpoints:
jsonl_file = os.path.join(CACHE_DIR, f"{endpoint}_raw.jsonl")
if not os.path.exists(jsonl_file):
print(f"✗ {jsonl_file} not found")
continue
print(f"Recreating {endpoint} from {jsonl_file}...")
all_data = []
with open(jsonl_file, "r") as f:
for line in f:
data = json.loads(line.strip())
if endpoint == "posts":
all_data.extend(data["socialPosts"])
else:
all_data.extend(data)
df = pd.DataFrame(all_data)
df = process_dataframe(df, endpoint)
output_file = os.path.join(CACHE_DIR, f"{endpoint}.parquet")
save_parquet(df, output_file)
print(f"✓ {endpoint}: {len(df):,} rows -> {output_file}")
def upload_to_hub(file_path, repo_id):
try:
api.upload_file(
path_or_fileobj=file_path,
path_in_repo=os.path.basename(file_path),
repo_id=repo_id,
repo_type="dataset",
)
print(f"✓ Uploaded {os.path.basename(file_path)} to {repo_id}")
return True
except Exception as e:
print(f"✗ Failed to upload {os.path.basename(file_path)}: {e}")
return False
def main(skip_upload=False):
created_files, elapsed = asyncio.run(create_parquet_files(skip_upload=skip_upload))
print(f"\nCompleted in {elapsed:.2f} seconds")
print(f"Created {len(created_files)} parquet files:")
for file in created_files:
size = os.path.getsize(file)
rows = len(pd.read_parquet(file))
print(f" {os.path.basename(file)}: {rows:,} rows, {size:,} bytes")
if skip_upload:
print(f"\nRaw JSONL files saved to {CACHE_DIR}/ for recreation")
print("Use 'python app.py --recreate' to recreate parquet files from JSONL")
if __name__ == "__main__":
import sys
if "--recreate" in sys.argv:
recreate_from_jsonl()
else:
skip_upload = "--skip-upload" in sys.argv
main(skip_upload=skip_upload)
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