# HackRx Insurance Policy Assistant

A FastAPI application that processes PDF documents and answers questions using AI, deployed on Hugging Face Spaces.

## Features

- PDF document parsing and text extraction
- Vector-based document search using FAISS
- AI-powered question answering using Google Gemini
- RESTful API endpoints for document processing

## API Endpoints

### Health Check
- `GET /` - Root endpoint
- `GET /health` - API status check

### Process PDF from URL
- `POST /api/v1/hackrx/run`
- **Headers**: `Authorization: Bearer <your_token>`
- **Body**:
```json
{
  "documents": "https://example.com/document.pdf",
  "questions": ["What is the coverage amount?", "What are the exclusions?"]
}
```

### Process Local PDF File
- `POST /api/v1/hackrx/local`
- **Body**:
```json
{
  "document_path": "/app/files/document.pdf",
  "questions": ["What is the coverage amount?", "What are the exclusions?"]
}
```

## Environment Variables

Set these in your Hugging Face Space settings:

- `GOOGLE_API_KEY` - Your Google Gemini API key

## Usage Examples

### Using curl

```bash
# Health check
curl https://your-space-name.hf.space/

# Process PDF from URL
curl -X POST https://your-space-name.hf.space/api/v1/hackrx/run \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_token_here" \
  -d '{
    "documents": "https://example.com/insurance-policy.pdf",
    "questions": ["What is the coverage amount?", "What are the exclusions?"]
  }'
```

### Using Python

```python
import requests

# Health check
response = requests.get("https://your-space-name.hf.space/")
print(response.json())

# Process PDF
url = "https://your-space-name.hf.space/api/v1/hackrx/run"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer your_token_here"
}
data = {
    "documents": "https://example.com/insurance-policy.pdf",
    "questions": ["What is the coverage amount?", "What are the exclusions?"]
}

response = requests.post(url, headers=headers, json=data)
print(response.json())
```

## Local Development

To run the application locally:

```bash
pip install -r requirements.txt
python app.py
```

The API will be available at `http://localhost:7860`

## Deployment

This application is configured for deployment on Hugging Face Spaces using Docker. The following files are included:

- `app.py` - Main application entry point
- `Dockerfile` - Docker configuration
- `.dockerignore` - Docker build optimization
- `requirements.txt` - Python dependencies

## Model Information

- **Framework**: FastAPI
- **AI Model**: Google Gemini
- **Vector Database**: FAISS
- **Document Processing**: PyMuPDF