Bill Data Extraction API

Extracts line items from bills/invoices using Google Gemini 1.5 Flash Vision.

Setup

1. Create virtual environment:

python -m venv venv
venv\Scripts\activate  # Windows

2. Install dependencies:

pip install -r requirements.txt

3. Create .env file with your Google AI Studio API key:

GOOGLE_API_KEY=your_key_here

4. Run the server:

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

API Usage

POST /extract-bill-data

{
    "document": "https://example.com/invoice.pdf"
}

Response Format

{
    "is_success": true,
    "token_usage": {
        "total_tokens": 1234,
        "input_tokens": 1000,
        "output_tokens": 234
    },
    "data": {
        "pagewise_line_items": [...],
        "total_item_count": 10
    }
}

Deployment

Option 1: Render (Recommended - Free tier available)

1. Push code to GitHub
2. Go to render.com → New Web Service
3. Connect your GitHub repo
4. Settings:
   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn app.main:app --host 0.0.0.0 --port $PORT
   • Environment Variables: Add GOOGLE_API_KEY=your_key_here
5. Deploy!

Option 2: Railway

1. Push code to GitHub
2. Go to railway.app → New Project → Deploy from GitHub
3. Add environment variable: GOOGLE_API_KEY
4. Railway auto-detects FastAPI and deploys

Option 3: Fly.io

1. Install flyctl: iwr https://fly.io/install.ps1 -useb | iex
2. Login: fly auth login
3. Initialize: fly launch (in project directory)
4. Set secret: fly secrets set GOOGLE_API_KEY=your_key_here
5. Deploy: fly deploy

Option 4: Vercel (Serverless)

1. Install Vercel CLI: npm i -g vercel
2. Create vercel.json:

{
  "version": 2,
  "builds": [{"src": "app/main.py", "use": "@vercel/python"}],
  "routes": [{"src": "/(.*)", "dest": "app/main.py"}]
}

3. Deploy: vercel
4. Set env var: vercel env add GOOGLE_API_KEY

Note: For all platforms, make sure to set the GOOGLE_API_KEY environment variable in the platform's dashboard.