Document Scanner and Analyzer

This project is a document scanner and analyzer designed to process PDF documents to extract high-level descriptions, summaries, and notable information.

The project includes both a backend API built with FastAPI and a front-end web application built with React and TypeScript.

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Project Architecture

document-scanner/
├── backend/
│   ├── app.py                 # FastAPI application entry point
│   ├── main.py                # Minimal CLI tool (optional)
│   ├── config.py              # Application configuration and logging setup
│   ├── document_processor.py  # PDF processing and OCR fallback logic
│   ├── document_analyzer.py   # DSPy-based document analysis module
│   ├── exceptions.py          # Custom exception classes
│   ├── requirements.txt       # Python virtual environment dependencies
│   └── .env                   # Environment variables (not committed)
├── frontend/
│   ├── public/
│   │   └── index.html         # HTML template
│   ├── src/
│   │   ├── App.tsx            # React application code
│   │   ├── App.css            # CSS styles for the front-end
│   │   ├── index.tsx          # React DOM render entry point
│   │   └── index.css          # Global CSS
│   └── package.json           # Frontend dependencies and scripts
└── README.md                  # This documentation

• Backend:
  The backend is built using FastAPI. It handles file uploads at /analyze/, processes the document using a combination of direct text extraction, OCR (if necessary), and then analyzes the content using DSPy.

• Frontend:
  The front-end (React/TypeScript) provides a interface allowing users to browse and select a PDF file, then submit it for analysis. It displays status messages, error messages, and formatted results.

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Prerequisites

• Python 3.10–3.12 (for the backend)
• Node.js and npm (for the frontend)

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Setup

Backend Setup

1. Create and Activate Virtual Environment:

cd backend
python -m venv .venv
source .venv/bin/activate  # Linux/MacOS; on Windows use `.venv\Scripts\activate`

2. Install Dependencies:

pip install -r requirements.txt

3. Configure Environment Variables:

Create a .env file in the backend directory and add:

OPENAI_API_KEY=your_api_key_here
DSPY_MODEL=openai/gpt-4o-mini
TEMP_DIR=temp
MAX_FILE_SIZE_MB=2
LOG_LEVEL=INFO
LOG_FILE=app.log

Input your OpenAI API key for OPENAI_API_KEY. Change the configuration variables if needed.

4. Run the FastAPI App:

uvicorn app:app --reload

The API will run on http://localhost:8000. It might take some on the first startup for DocTR to download the OCR models.

Frontend Setup

1. Install Node.js:

Ensure Node.js and npm (or yarn) are installed.

2. Navigate to the Frontend Directory:

cd frontend

3. Install Dependencies:

npm install

4. Run the Frontend Application:

npm start

The front-end application should run on http://localhost:3000.