- Complete detailed plan for automated activity extraction from 2000+ files
- Hybrid approach: Python scripts for HTML/TXT/MD + Claude for PDF/DOC
- Includes full Python extractors with error handling and batch processing
- Template for Claude-assisted PDF/DOC processing (high-value files)
- Orchestrator script for complete automation workflow
- Estimated result: 2000+ activities indexed in 8 hours total work
Key components:
- HTML extractor for 1876 files (BeautifulSoup + pattern recognition)
- Text/MD extractor for 45 files (regex patterns + markdown parsing)
- Unified processor with progress tracking and batch saving
- Claude extraction templates with JSON import system
- Complete automation for 90% of files, manual assist for 10% high-value
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major Changes:
- Migrated from prototype to production architecture
- Implemented modular Flask app with models/services/web layers
- Added Docker containerization with docker-compose
- Switched to Pipenv for dependency management
- Built advanced parser extracting 63 real activities from INDEX_MASTER
- Implemented SQLite FTS5 full-text search
- Created minimalist, responsive web interface
- Added comprehensive documentation and deployment guides
Technical Improvements:
- Clean separation of concerns (models, services, web)
- Enhanced database schema with FTS5 indexing
- Dynamic filters populated from real data
- Production-ready configuration management
- Security best practices implementation
- Health monitoring and API endpoints
Removed Legacy Files:
- Old src/ directory structure
- Static requirements.txt (replaced by Pipfile)
- Test and debug files
- Temporary cache files
Current Status:
- 63 activities indexed across 8 categories
- Full-text search operational
- Docker deployment ready
- Production documentation complete
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
