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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.

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.agents 7d59646d57 TG-6: Finalize remaining files vor 2 Monaten
AI_History a9a1aa8f56 TG-29 TG-31 TG-32 TG-33: Implement EAV Architecture, Dynamic Medical CRUD UI, DataFrame Alert Engine, and Email Resets. TG-30: Fix Windows utf8 Encoding in Ingestion Engine. vor 2 Monaten
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PROJECT_CONTEXT.md 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration vor 2 Monaten
README.md d53e2000e6 Sprint 6: Complete documentation and code cleanup vor 2 Monaten
alembic.ini 79e1835d2c Optimize horizontal partitioning to slice into 8-column chunks bypassing InnoDB limits vor 2 Monaten
app.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration vor 2 Monaten
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ingest_csv.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration vor 2 Monaten
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my.cnf 86c76e282d TG-1: Fix MySQL 8.0 startup crash by removing premature validate_password plugin config vor 2 Monaten
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requirements.txt 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration vor 2 Monaten
setup_db.py 54db47f014 Disable foreign key checks during drop vor 2 Monaten
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snmp_notifier.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration vor 2 Monaten
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unit_converter.py 01a685c9b1 Implement full dynamic CSV schema ingestion and unit conversion module vor 2 Monaten

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)