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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.

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

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)