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.

lanfr144 e05a5fed1f increase mysql healthcheck retries 4 dagen geleden
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README.md c812444386 Sprint 6: Complete documentation and code cleanup 2 weken geleden
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app.py bf72dab527 fix streamlit nested button state bug for clinical AI eval 4 dagen geleden
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configure_zabbix_email.py 097b99bd50 fix: update Zabbix auth payload for v7.0 and add debug logs 1 week geleden
configure_zabbix_teams.py db8b1a54d3 feat: integrate Zabbix with Microsoft Teams webhook 1 week geleden
create_bookmarks_page.py 50d8206bd3 TG-85: finalize missing Nginx proxy and bookmarks scripts 1 week geleden
create_taiga_task.py 2a4f8d488f feat: Containerize components, configure Zabbix rules, sync Taiga 1 week geleden
create_zabbix_dashboard.py ba0c30a5c1 TG-86: finalize zabbix dashboard generation script 4 dagen geleden
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taiga_wiki_bookmarks.py 8cfa56c45a TG-154: Phase 3 Overhaul - Fix pandas limit, Plate Builder, Chat, Meal Planner JSON 2 weken geleden
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update_bookmarks.py e1d0132353 TG-85: Extreme performance tuning via Pre-fetch Context RAG and UI token streaming 1 week geleden
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README.md

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.
  • 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.
  • High-Performance Database: Implements Grouped Vertical Partitioning to bypass InnoDB limits, featuring FULLTEXT indexing for lightning-fast search capabilities across millions of foods.

Documentation

Please refer to the docs/ folder for detailed guides:

Tech Stack

  • Frontend: Streamlit
  • Database: MySQL 8.0
  • AI Engine: Ollama (Mistral / Llama3)
  • Deployment: Native Ubuntu, Docker, Kubernetes
  • Project Management: Taiga (Synced dynamically via Python)