analytics.py

# ─── 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")
TIMES_FILE = os.path.join(DATA_DIR, "request_times.json")
LOCK_FILE   = os.path.join(DATA_DIR, "analytics.lock")

def _load() -> dict:
    if not os.path.exists(COUNTS_FILE):
        return {}
    with open(COUNTS_FILE) as f:
        return json.load(f)

def _save(data: dict):
    with open(COUNTS_FILE, "w") as f:
        json.dump(data, f)

def _load_times() -> dict:
    if not os.path.exists(TIMES_FILE):
        return {}
    with open(TIMES_FILE) as f:
        return json.load(f)

def _save_times(data: dict):
    with open(TIMES_FILE, "w") as f:
        json.dump(data, f)

async def record_request(duration: float = None) -> None:
    """Increment today's counter (UTC) atomically and optionally record request duration."""
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    with FileLock(LOCK_FILE):
        # Update counts
        data = _load()
        data[today] = data.get(today, 0) + 1
        _save(data)
        
        # Update times if duration provided
        if duration is not None:
            times = _load_times()
            if today not in times:
                times[today] = []
            times[today].append(round(duration, 2))
            _save_times(times)

def last_n_days_df(n: int = 30) -> pd.DataFrame:
    """Return a DataFrame with a row for each of the past *n* days."""
    now = datetime.now(timezone.utc)
    with FileLock(LOCK_FILE):
        data = _load()
    records = []
    for i in range(n):
        day = (now - timedelta(days=n - 1 - i))
        day_str = day.strftime("%Y-%m-%d")
        # Format date for display (MMM DD)
        display_date = day.strftime("%b %d")
        records.append({
            "date": display_date, 
            "count": data.get(day_str, 0),
            "full_date": day_str  # Keep full date for tooltip
        })
    return pd.DataFrame(records)

def last_n_days_avg_time_df(n: int = 30) -> pd.DataFrame:
    """Return a DataFrame with average request time for each of the past *n* days."""
    now = datetime.now(timezone.utc)
    with FileLock(LOCK_FILE):
        times = _load_times()
    records = []
    for i in range(n):
        day = (now - timedelta(days=n - 1 - i))
        day_str = day.strftime("%Y-%m-%d")
        # Format date for display (MMM DD)
        display_date = day.strftime("%b %d")
        
        # Calculate average time for the day
        day_times = times.get(day_str, [])
        avg_time = round(sum(day_times) / len(day_times), 2) if day_times else 0
        
        records.append({
            "date": display_date, 
            "avg_time": avg_time,
            "request_count": len(day_times),
            "full_date": day_str  # Keep full date for tooltip
        })
    return pd.DataFrame(records)