# AI-Assisted Smart Data Entry Backend

This project provides a FastAPI-based backend for extracting structured data from medical receipts using OpenAI's GPT-4o model with structured outputs.

## Features

- **FastAPI**: Modern, fast web framework for building APIs.
- **Structured Extraction**: Uses OpenAI's `beta.chat.completions.parse` to ensure extracted data adheres to a Pydantic schema.
- **CORS Enabled**: Configured to allow access from frontend applications (e.g., Angular).
- **Health Check**: Simple endpoint to verify server status.

## Setup

1. **Install Dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

2. **Configure Environment**:
   Create a `.env` file in the root directory and add your OpenAI API key:
   ```env
   OPENAI_API_KEY=your_api_key_here
   ```

3. **Run the Server**:
   ```bash
   python main.py
   ```
   Or using uvicorn:
   ```bash
   uvicorn main:app --reload
   ```

## API Endpoints

### `GET /health`
Returns the status of the server.

### `POST /api/v1/extract`
Extracts data from a medical receipt image.

**Request Body**:
- `file`: Multipart image file.
- `user_name` (optional): Name of the employee.
- `department` (optional): Department of the employee.

**Response**:
Returns a structured JSON matching the `ExtractionResponse` schema.

## Project Structure

- `main.py`: Entry point and API endpoints.
- `schemas.py`: Pydantic models for data validation and structured output.
- `services/`:
  - `openai_service.py`: Logic for interacting with OpenAI API.
- `requirements.txt`: Python dependencies.
- `.env`: Environment variables (not tracked by git).