# Project Context: Local Food AI

## 🎯 Vision Statement
A local food AI that provides full nutritional value information on any food and can generate complete menu proposals based on the user's specification. The system is designed with a strict privacy-first focus, ensuring no user data leaves the server, and fits within specific hardware limits.

## 🏗️ Architecture & Tech Stack

### Remote Environment
- **Server**: Ubuntu 24.04 VM at `192.168.130.170` (8 vCPUs, 30 GB RAM, no dedicated GPU). Accessed via SSH as `francois` or `root`.
- **Containerization**: Docker (for backend/frontend) or native deployment.
- **LLM Engine**: Ollama (for running lightweight, quantized local language models like `mistral` or `llama3-8b`).
- **Database Server**: MySQL (for user data, saved lists, and nutritional database).

### Frontend Web Interface
- **Framework**: Streamlit (Python)
- **Purpose**: To provide an interactive chat interface for the AI, search functionality for food nutrition, user account management, and food combination calculators.

### Local Environment
- **Workspace**: `c:\Users\lanfr144\Documents\DOPRO1\Antigravity\Food`
- **OS**: Windows

### Python Environment
Python will be used for scripting, data manipulation, and interacting with the LLM and the Database. Required libraries:
- `streamlit`: To build the web application.
- `ollama`: For querying local models.
- `pandas`: For data processing (e.g., ingesting nutrition CSVs).
- `mysql-connector-python` or `SQLAlchemy`: For database access.
- **Web Search Tool**: (e.g., DuckDuckGo API wrapper) for the AI to dynamically gather external information anonymously.

## 🔐 Core Requirements & Privacy
- **User Accounts**: Secure login and registration system.
- **Data Privacy**: No user data leaves the server.
- **Repository**: Public Git repository at `https://git.btshub.lu` named `LocalFoodAI_<your IAM>`. Contains a strict `.gitignore`. Teacher (`evegi144`) added as collaborator.
- **Ease of Use**: Anyone should be able to clone the repo and run it easily (via Docker/scripts).

## 🚀 Key Features (User Stories)
1. **Nutritional Information**: View complete macros, minerals, vitamins, etc., for any food.
2. **Food Combinations**: Enter quantities for multiple foods to get a combined nutritional overview. Store and edit these in named lists.
3. **Nutrient Search**: Search for specific nutrients and sort foods containing them.
4. **AI Menu Proposals**: Get AI-generated menu proposals based on nutritional goals and constraints (e.g., allergies).
5. **AI Nutrition Chat**: Freely chat with the AI about nutrition.
6. **Anonymous Web Search**: The AI can perform local background web searches for missing information.

## 🚀 Installation Prerequisites & Deployment
### Server Prerequisites (Ubuntu 24.04 Native)
- `gcc` and `build-essential`.
- `python3-venv`, `python3-dev`, and `python3-pip`.
- `mysql-server` and `curl`.

### Automated Deployment (`deploy.sh`)
Executing this file on a naked server will automatically:
1. Fetch and install all apt-level system prerequisites.
2. Install Ollama natively.
3. Push custom configurations (`my.cnf`) to MySQL server and configure the local virtual environment.
4. Pip-install the project dependencies.

## 💾 Database Configuration & Data Loading
### 1. Initial MySQL Setup
- `init.sql` script loads into MySQL to create the database, users, and tables for User Profiles, Food Combos, and the Nutrition Data.

### 2. Data Import (CSV)
- A nutritional database `.csv` ingestion script (using `pandas`) populates the MySQL tables.

### 3. Search Capabilities
- The MySQL database must be optimized for text/context queries to support the AI's Retrieval-Augmented Generation (RAG).

## 📝 Roadmap & Next Steps (Sprints)
- [ ] **Sprint 1 (Foundation)**: Initialize Git repository (`LocalFoodAI_<IAM>`), setup `.gitignore`, finalize `deploy.sh`, initialize MySQL (`init.sql`), and build Streamlit user login.
- [ ] **Sprint 2 (Data Core)**: Import food nutritional CSV via Pandas into MySQL. Build Streamlit pages for food search and details.
- [ ] **Sprint 3 (Combinations)**: Implement Streamlit logic to combine foods by gram amounts and save lists to MySQL.
- [ ] **Sprint 4 (Local AI)**: Deploy lightweight Ollama models and build the Streamlit chat interface.
- [ ] **Sprint 5 (Advanced AI)**: Implement RAG for menu proposals and integrate anonymous web search tool.
- [ ] **Sprint 6 (Polish)**: Thorough testing and perfect the `README.md`.

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*Generated by Antigravity. Update this file as technical requirements and data schemas evolve.*