backend/tasks/reporting_tasks.py

import logging
import time

logger = logging.getLogger(__name__)

def generate_scheduled_report(user_id: str, email: str, dataset_id: str):
    """
    Background task to autonomously analyze a dataset and email a report.
    This simulates a heavy ML task running outside the web request cycle.
    """
    try:
        logger.info(f"🚀 Starting background reporting task for {email}")
        
        # 1. Fetch user data from DB or S3 (Simulated)
        time.sleep(2)  
        logger.info(f"📂 Fetched dataset {dataset_id} for user {user_id}")
        
        # 2. Run Data Janitor (Simulated heavy ETL)
        time.sleep(3)
        logger.info("🧹 Auto-ETL completed.")
        
        # 3. Run LLM Analyst
        time.sleep(4)
        logger.info("🧠 LLM Insights generated.")
        
        # 4. Generate PDF & Email (Simulated)
        time.sleep(2)
        logger.info(f"📧 Emailed final AI report to {email}")
        
        return {"status": "success", "user": user_id, "email": email}
        
    except Exception as e:
        logger.error(f"Task failed for user {user_id}: {e}")
        return {"status": "failed", "error": str(e)}