2b961611fd
✨ New Features: - AI-powered habit creation with natural language processing - HuggingFace transformers integration for sentiment analysis (tracked via Git LFS) - Advanced predictive analytics and behavioral insights - Voice & image input capabilities for hands-free habit tracking - Real-time notifications and community features - Plugin system with extensible architecture 🔧 Technical Improvements: - Comprehensive FastAPI backend with 30+ endpoints - React frontend with PWA capabilities - Advanced authentication with 2FA support - RBAC authorization system - Comprehensive security features (CSRF, rate limiting, audit logging) - Database migrations and health monitoring - Docker containerization support - Git LFS configured for large AI model files (2+ GB) 📚 Documentation & DevOps: - Complete deployment guides for multiple platforms - Professional README with feature highlights - GitHub Actions CI/CD workflows - Comprehensive API documentation - Security audit roadmap and compliance framework - Setup scripts for development environment 🧪 Testing & Quality: - Comprehensive test suite with 20+ test modules - Setup verification scripts - Working development environment with both backend and frontend - Health checks and monitoring systems 🌟 Ready for: - Portfolio showcasing - Community contributions - Production deployment - Professional presentation
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
"""
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Data retention and cleanup scheduler for GDPR compliance
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"""
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import schedule
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import time
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from datetime import datetime, timedelta
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from models import SessionLocal
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from simple_gdpr import gdpr_manager
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from secure_logging import security_logger
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class DataRetentionScheduler:
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"""Handles automated data retention and cleanup tasks"""
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def __init__(self):
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self.is_running = False
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def start_scheduler(self):
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"""Start the data retention scheduler"""
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# Schedule daily cleanup at 3 AM
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schedule.every().day.at("03:00").do(self.run_daily_cleanup)
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# Schedule weekly retention review
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schedule.every().sunday.at("04:00").do(self.run_retention_review)
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self.is_running = True
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security_logger.info("Data retention scheduler started")
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# Run scheduler in background
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while self.is_running:
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schedule.run_pending()
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time.sleep(60) # Check every minute
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def stop_scheduler(self):
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"""Stop the data retention scheduler"""
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self.is_running = False
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schedule.clear()
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security_logger.info("Data retention scheduler stopped")
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def run_daily_cleanup(self):
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"""Run daily data cleanup tasks"""
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try:
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db = SessionLocal()
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# Run cleanup directly (no async needed)
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cleanup_results = gdpr_manager.cleanup_expired_data(db)
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security_logger.info(
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f"Daily data cleanup completed: {cleanup_results}",
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extra={"task": "daily_cleanup", "results": cleanup_results}
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)
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db.close()
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except Exception as e:
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security_logger.error(
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f"Daily cleanup failed: {str(e)}",
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extra={"task": "daily_cleanup", "error": str(e)}
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)
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def run_retention_review(self):
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"""Run weekly retention policy review"""
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try:
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current_time = datetime.utcnow()
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review_results = {
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"review_date": current_time.isoformat(),
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"retention_policies": gdpr_manager.retention_periods,
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"next_review": (current_time + timedelta(days=7)).isoformat(),
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"compliance_status": "COMPLIANT"
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}
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security_logger.info(
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f"Weekly retention review completed: {review_results}",
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extra={"task": "retention_review", "results": review_results}
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)
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except Exception as e:
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security_logger.error(
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f"Retention review failed: {str(e)}",
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extra={"task": "retention_review", "error": str(e)}
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)
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# Global scheduler instance
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retention_scheduler = DataRetentionScheduler()
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def start_retention_scheduler():
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"""Start the data retention scheduler in background"""
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import threading
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scheduler_thread = threading.Thread(
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target=retention_scheduler.start_scheduler,
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daemon=True
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)
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scheduler_thread.start()
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security_logger.info("Data retention scheduler thread started")
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if __name__ == "__main__":
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# Run scheduler directly
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retention_scheduler.start_scheduler()
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