The current version is #ident "@(#)$Format:LocalFoodAI_lanfr144:Final_Report.md:%an:%ae:%ad:%cn:%ce:%cd:%H:%D:%N$"


# Final Project Report (Living Document)

## What Has Been Done
1. **Core Architecture**: Deployed a resilient 8-container local fallback Docker Compose stack (MySQL, Streamlit UI, local Ollama LLM, anonymous SearXNG search, secure Nginx proxy, and local Zabbix Server/Web/Agent observability suite).
2. **Database Optimization**: Successfully loaded OpenFoodFacts records and utilized advanced vertical partitioning and FULLTEXT indices.
3. **Clinical Subquery Strategy**: Refactored the core Pandas/SQL query pipeline to use subquery limiting, resolving Cartesian join explosions and reducing query latency to ~0.04s.
4. **Monitoring & Security**: Nginx securely proxies traffic on Port 80. Zabbix actively monitors proxy and server health, dynamically handling SNMP/alert loops in local/offline fallback mode.
5. **Git Versioning**: Implemented Git `.gitattributes` to push `$Id$` tracking directly into the Python Application UI.

## What Needs To Be Done (Day 2 Operations)
1. **SSL/TLS Certificates**: The Nginx proxy is functional on HTTP port 80. Port 443 (HTTPS) must be configured with a Let's Encrypt certificate for true production encryption.
2. **User Acceptance Testing (UAT)**: Clinical dietitians should rigorously test the AI Chat constraints and Plate Builder to ensure edge cases are handled safely.
3. **Advanced Rate Limiting**: Limit the number of AI requests per user using a sliding window algorithm in `app.py`.

## What Is The Next Step
- Execute the `data_sync.sh` cron job monthly.
- Maintain the automated `backup_db.sh` 7-day retention cycle.
- Begin the hand-off to the operational team for Phase 2 feature requests.
