Repository used for the DOPRO project dealing with food AI.
This repository contains:
a full Taiga export plus all other documents that are part of your project planning, including any project presentation materials.
the full final product, including all files, documentation and presentation materials.
Lange François
45bdb66384
[TG-131] Purge database passwords from tracked files and format application versioning
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README.md
The current version is #ident "@(#)$Format:LocalFoodAI:app.py:%an:%ae:%ad:%cn:%ce:%cd:%H:%D:%N$"
Local Food AI 🍔
A strictly local, privacy-first AI Medical Dietitian and Food Explorer. This project leverages the OpenFoodFacts dataset and local LLMs (Ollama) to provide medically sound dietary advice, recipe parsing, and menu planning without sending any user data to the cloud.
Features
- Dynamic Medical Profiling: Configure your health profile (e.g., Kidney issues, pregnancy, vegan). The AI dynamically adjusts all responses, recommendations, and warnings based on these exact medical needs.
- RAG Architecture: The AI is connected to a massively partitioned local MySQL database. When you ask a question or request a meal plan, the AI executes SQL queries autonomously to fetch precise nutritional data.
- SearXNG Web Integration: When the local Database lacks culinary heuristics, the AI securely queries a local, private instance of SearXNG to answer questions without compromising patient privacy.
- Plate Builder & Unit Conversion: Input culinary recipes (e.g., "1.5 cups of flour") and the system converts them to metric standard weights based on the product's density.
- Distributed Microservice Topology: Supports decoupling across VirtualBox, Hyper-V, and WSL2 using Bridged Networking and SNMP container telemetry for Zabbix.
Documentation (Capstone Deliverables)
Please refer to the docs/ folder for detailed guides:
- Architecture Map
- Distributed Deployment Procedure (PoC)
- Disaster Recovery & Backup Plan
- Zabbix Telemetry Guide
- Agile Retro Planning
- Taiga Final Audit Report
- Historical Taiga Agile Export (included strictly for documentation and project history purposes)
Tech Stack
- Frontend: Streamlit
- Database: MySQL 8.0
- AI Engine: Ollama (Llama 3.2:3B)
- Web Search: SearXNG
- Monitoring: Zabbix (SNMPv2c)
- Deployment: Native Ubuntu, Docker Compose, Hyper-V / VirtualBox
- Project Management: Taiga (Synced dynamically via Python)
AI Skills & Governance
This project leverages specialized AI skills to maintain code quality, documentation, and strict governance:
- Code Review: Automatically reviews code changes for correctness, edge cases, style, and performance.
- Doc Writer: Ensures all documentation and inline comments stay perfectly synchronized with source code changes.
- Expert Coach: Acts as a principal engineer, enforcing optimal code, modularity, and a mandatory Identity Tag in file headers.
- Git Commit: Enforces strict Git governance, Taiga tracking (
TG-123), and a singlemainbranch workflow. For every commit, a task in Taiga must be associated. If the task does not exist, it must be created and added to a user story and a sprint. - Refactor Coach: Refactors code to improve readability, performance, and modularity without changing external behavior.
- SQL Optimizer: Enforces DBA standards for MySQL, Oracle, and PostgreSQL, ensuring proper indexing, transaction management, and secure access.
- Test Generator: Generates comprehensive unit and integration tests focusing on boundary conditions and logical coverage.