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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 12b1760ccf Add Taiga Wiki Sync 2 hete
.agents 7d59646d57 TG-6: Finalize remaining files 1 hónapja
AI_History f851d49f92 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. 3 hete
alembic 0fd29e16de Reduce partition chunk size to 4 to bypass persistent row size error; include initial alembic migration 3 hete
docker bfda8a610b Add snmp to Streamlit container for traps 2 hete
docs c3fc1ef4c0 Add Sprint 8 Documentation 2 hete
k8s 4655c26f1f Add untracked project files and configs 2 hete
legacy_scripts c812444386 Sprint 6: Complete documentation and code cleanup 2 hete
taiga_wiki e78a25bf3c TG-2: Populate Sprint 2 accomplishments in Taiga Wiki 4 hete
.gitignore 4655c26f1f Add untracked project files and configs 2 hete
Final_Presentation.html 1558f08eca Execute Implementation Plan 2 3 hete
PROJECT_CONTEXT.md e3f96b1f33 Sprint 7: Zabbix and SNMPv3 Monitoring Integration 2 hete
README.md c812444386 Sprint 6: Complete documentation and code cleanup 2 hete
alembic.ini 73f7a04cd0 Optimize horizontal partitioning to slice into 8-column chunks bypassing InnoDB limits 3 hete
app.py 4f7322e4da Strip username to prevent space errors 2 hete
check_users.py 7766898050 Add check users script 2 hete
deploy.sh a54dc25344 TG-21: Update deploy.sh to include requests connectivity dependency. 3 hete
download_csv.sh 4655c26f1f Add untracked project files and configs 2 hete
generate_taiga_wiki.py e78a25bf3c TG-2: Populate Sprint 2 accomplishments in Taiga Wiki 4 hete
ingest_csv.py e3f96b1f33 Sprint 7: Zabbix and SNMPv3 Monitoring Integration 2 hete
init.sql ae711f7d4c TG-3: Docker Setup and DB Creation 1 hónapja
init_zabbix_db.sh 06df1fda4e Add Zabbix DB init script 2 hete
master_trigger.sh 38a83a1bf0 Deployment Finalization: Vitamin schemas, Green UI, and Taiga tools 3 hete
my.cnf 86c76e282d TG-1: Fix MySQL 8.0 startup crash by removing premature validate_password plugin config 4 hete
myloginpath.py 4655c26f1f Add untracked project files and configs 2 hete
proper_reset.sh 776d6a6153 Add proper reset 2 hete
requirements.txt e3f96b1f33 Sprint 7: Zabbix and SNMPv3 Monitoring Integration 2 hete
reset_zabbix_db.sh 9e59bd56c5 Add reset DB script 2 hete
setup_db.py d5eae6eb05 Disable foreign key checks during drop 2 hete
setup_logins.exp c830b35313 TG-2: Automate DB setup and mysql_config_editor passwords for CI/CD 4 hete
setup_mail_forwarding.sh ab7e3b1d3a TG-2: Restructure schema for all CSV columns, async ingestion, and mail forwarding 3 hete
setup_postfix.sh 38a83a1bf0 Deployment Finalization: Vitamin schemas, Green UI, and Taiga tools 3 hete
setup_searxng.sh ebfb102bc7 TG-20: Create setup_searxng.sh to install Docker and bind anonymous SearXNG to localhost:8080. 3 hete
setup_sprint7_taiga.py e3f96b1f33 Sprint 7: Zabbix and SNMPv3 Monitoring Integration 2 hete
setup_sprint8_taiga.py 69bad82b3b Add Sprint 8 Taiga script 2 hete
setup_unix_user.sh 4655c26f1f Add untracked project files and configs 2 hete
snmp_notifier.py c7eda9a94d Fix snmp_notifier to use snmptrap cli 2 hete
start_batch_ingest.sh 00f1d63625 Fix python virtual env paths 3 hete
sync_taiga.py ef9531a80d TG-3: Update python sync script with correct username FrancoisLange 4 hete
taiga_sync_fixer.py 4655c26f1f Add untracked project files and configs 2 hete
taiga_wiki_push.py 12b1760ccf Add Taiga Wiki Sync 2 hete
test_login.py d7f6558318 Add test login 2 hete
test_snmp.py 1d5ce8580c Add test SNMP script 2 hete
unit_converter.py 620543f87d Implement full dynamic CSV schema ingestion and unit conversion module 2 hete

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)