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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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LocalFoodAI_lan...
lanfr144 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
.agents 7d59646d57 TG-6: Finalize remaining files il y a 2 mois
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. il y a 2 mois
alembic b0692b7ed4 Reduce partition chunk size to 4 to bypass persistent row size error; include initial alembic migration il y a 2 mois
docker 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
docs d53e2000e6 Sprint 6: Complete documentation and code cleanup il y a 2 mois
k8s 4112b60d71 Add untracked project files and configs il y a 2 mois
legacy_scripts d53e2000e6 Sprint 6: Complete documentation and code cleanup il y a 2 mois
taiga_wiki e78a25bf3c TG-2: Populate Sprint 2 accomplishments in Taiga Wiki il y a 2 mois
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Final_Presentation.html a2d859e15b Execute Implementation Plan 2 il y a 2 mois
PROJECT_CONTEXT.md 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
README.md d53e2000e6 Sprint 6: Complete documentation and code cleanup il y a 2 mois
alembic.ini 79e1835d2c Optimize horizontal partitioning to slice into 8-column chunks bypassing InnoDB limits il y a 2 mois
app.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
deploy.sh 942215fc72 TG-21: Update deploy.sh to include requests connectivity dependency. il y a 2 mois
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generate_taiga_wiki.py e78a25bf3c TG-2: Populate Sprint 2 accomplishments in Taiga Wiki il y a 2 mois
ingest_csv.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
init.sql ae711f7d4c TG-3: Docker Setup and DB Creation il y a 2 mois
master_trigger.sh d1c44bc989 Deployment Finalization: Vitamin schemas, Green UI, and Taiga tools il y a 2 mois
my.cnf 86c76e282d TG-1: Fix MySQL 8.0 startup crash by removing premature validate_password plugin config il y a 2 mois
myloginpath.py 4112b60d71 Add untracked project files and configs il y a 2 mois
requirements.txt 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
setup_db.py 54db47f014 Disable foreign key checks during drop il y a 2 mois
setup_logins.exp c830b35313 TG-2: Automate DB setup and mysql_config_editor passwords for CI/CD il y a 2 mois
setup_mail_forwarding.sh ab7e3b1d3a TG-2: Restructure schema for all CSV columns, async ingestion, and mail forwarding il y a 2 mois
setup_postfix.sh d1c44bc989 Deployment Finalization: Vitamin schemas, Green UI, and Taiga tools il y a 2 mois
setup_searxng.sh 2d7307f7e4 TG-20: Create setup_searxng.sh to install Docker and bind anonymous SearXNG to localhost:8080. il y a 2 mois
setup_sprint7_taiga.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
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snmp_notifier.py 342ad4bd92 Sprint 7: Zabbix and SNMPv3 Monitoring Integration il y a 2 mois
start_batch_ingest.sh 433d123181 Fix python virtual env paths il y a 2 mois
sync_taiga.py ef9531a80d TG-3: Update python sync script with correct username FrancoisLange il y a 2 mois
taiga_sync_fixer.py 4112b60d71 Add untracked project files and configs il y a 2 mois
unit_converter.py 01a685c9b1 Implement full dynamic CSV schema ingestion and unit conversion module il y a 2 mois

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)