FMCG-Text-to-SQL-Analytics-Platform

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FMCG-Text-to-SQL-Analytics-Platform

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FMCG-Text-to-SQL-Analytics-Platform 최신버젼 다운로드

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# FMCG Text-to-SQL Analytics Platform

This project implements an AI-powered Text-to-SQL analytics system for Fast-Moving Consumer Goods (FMCG) data. It allows business users to ask natural language questions about sales, promotions, and inventory, and receive actionable insights backed by dynamically generated SQL queries.

## Architecture

* **Frontend:** Next.js (React) providing a premium conversational interface.
* **Backend:** FastAPI (Python) handling API routing, query execution, and AI orchestration.
* **Database:** SQLite (local prototype), easily swappable to PostgreSQL (Supabase) via SQLAlchemy.
* **AI Engine:** Google Gemini (gemini-1.5-pro) for SQL generation and data summarization.
* **Safety Layer:** Custom SQL validator that strictly allows `SELECT` operations to prevent SQL injection.

## Project Structure

* `/data` - Contains generated synthetic FMCG data (CSV format) and the SQLite database.
* `/scripts` - Contains `generate_data.py` used to produce the synthetic dataset.
* `/backend` - The FastAPI application (`main.py`, `database.py`, `llm.py`, `sql_generator.py`).
* `/frontend` - The Next.js chat interface.
* `/docs` - Architecture diagrams, presentation slides, and evaluation questions.

## Setup Instructions

### Prerequisites
- Node.js (v18+)
- Python (v3.10+)
- A Google Gemini API Key

### 1. Generate Data & Setup Backend
Navigate to the `backend` directory, install requirements, and initialize the database:
```bash
cd backend
python -m venv venv
# Activate virtual environment (Windows: .\venv\Scripts\activate, Mac/Linux: source venv/bin/activate)
pip install -r requirements.txt
python ../scripts/generate_data.py
```

Create a `.env` file in the `backend` folder and add your Gemini API Key:
```env
GEMINI_API_KEY="your_api_key_here"
```

Start the FastAPI server:
```bash
uvicorn main:app --reload
```
The API will run on `http://localhost:8000`.

### 2. Setup Frontend
Navigate to the `frontend` directory and install dependencies:
```bash
cd frontend
npm install
npm run dev
```
The web app will run on `http://localhost:3000`.

## Deployment

- **Frontend:** Can be deployed to Vercel with zero configuration. Simply connect your GitHub repository and import the `frontend/` directory.
- **Backend:** Can be deployed to Render or Railway as a Python Web Service. Set the Root Directory to `backend/` and configure the Start Command as `uvicorn main:app --host 0.0.0.0 --port $PORT`. Ensure you add `GEMINI_API_KEY` to the environment variables.
- **Database:** For production, swap the local SQLite URL in `database.py` with a Supabase PostgreSQL connection string, and run the initialization script again to populate it.

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