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# ─── analytics.py ──────────────────────────────────────────────────────────────
import os
import json
from datetime import datetime, timedelta, timezone
from filelock import FileLock # pip install filelock
import pandas as pd # already available in HF images
# Determine data directory based on environment
# 1. Check for environment variable override
# 2. Use /data if it exists and is writable (Hugging Face Spaces with persistent storage)
# 3. Use ./data for local development
DATA_DIR = os.getenv("ANALYTICS_DATA_DIR")
if not DATA_DIR:
if os.path.exists("/data") and os.access("/data", os.W_OK):
DATA_DIR = "/data"
print("[Analytics] Using persistent storage at /data")
else:
DATA_DIR = "./data"
print("[Analytics] Using local storage at ./data")
os.makedirs(DATA_DIR, exist_ok=True)
COUNTS_FILE = os.path.join(DATA_DIR, "request_counts.json")
LOCK_FILE = os.path.join(DATA_DIR, "analytics.lock")
# ──────────────────────────────────────────────────────────────────────────────
# Storage helpers
# ──────────────────────────────────────────────────────────────────────────────
def _load_counts() -> dict:
if not os.path.exists(COUNTS_FILE):
return {}
with open(COUNTS_FILE) as f:
try:
return json.load(f)
except json.JSONDecodeError:
return {}
def _save_counts(data: dict):
with open(COUNTS_FILE, "w") as f:
json.dump(data, f)
def _normalize_counts_schema(data: dict) -> dict:
"""
Ensure data is {date: {"search": int, "fetch": int}}.
Backward compatible with old schema {date: int}.
"""
normalized = {}
for day, value in data.items():
if isinstance(value, dict):
normalized[day] = {
"search": int(value.get("search", 0)),
"fetch": int(value.get("fetch", 0)),
}
else:
# Old schema: total count as int → attribute to "search", keep fetch=0
normalized[day] = {"search": int(value or 0), "fetch": 0}
return normalized
# ──────────────────────────────────────────────────────────────────────────────
# Public API
# ──────────────────────────────────────────────────────────────────────────────
async def record_request(tool: str) -> None:
"""Increment today's counter (UTC) for the given tool: 'search' or 'fetch'."""
tool = (tool or "").strip().lower()
if tool not in {"search", "fetch"}:
# Ignore unknown tool buckets to keep charts clean
tool = "search"
today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
with FileLock(LOCK_FILE):
data = _normalize_counts_schema(_load_counts())
if today not in data:
data[today] = {"search": 0, "fetch": 0}
data[today][tool] = int(data[today].get(tool, 0)) + 1
_save_counts(data)
def last_n_days_count_df(tool: str, n: int = 30) -> pd.DataFrame:
"""Return DataFrame with a row for each of the past n days for the given tool."""
tool = (tool or "").strip().lower()
if tool not in {"search", "fetch"}:
tool = "search"
now = datetime.now(timezone.utc)
with FileLock(LOCK_FILE):
data = _normalize_counts_schema(_load_counts())
records = []
for i in range(n):
day = now - timedelta(days=n - 1 - i)
day_key = day.strftime("%Y-%m-%d")
display_date = day.strftime("%b %d")
counts = data.get(day_key, {"search": 0, "fetch": 0})
records.append(
{
"date": display_date,
"count": int(counts.get(tool, 0)),
"full_date": day_key,
}
)
return pd.DataFrame(records)
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