AI-Powered Document Intelligence System 📄🤖

Welcome to the AI-Powered Document Intelligence System! This project is a beginner-friendly, full-stack application that uses AI to automatically extract key information from documents like invoices, receipts, contracts, and more. It combines modern web technologies with AI APIs to create a powerful document processing tool. 🚀

📖 Project Overview

Imagine uploading a document (PDF, JPG, or PNG) and having an AI magically extract details like company names, dates, amounts, addresses, and more, presenting them in a neat table or downloadable CSV. That's what this system does! It's perfect for automating data entry tasks and learning about AI integration, full-stack development, and API usage.

Key Features

• 📤 Document Upload: Upload images or PDFs through a drag-and-drop interface.
• 🔍 Text Extraction: Uses OCR (Optical Character Recognition) to read text from documents.
• 🧠 AI Analysis: Leverages OpenAI to extract structured data (e.g., company names, dates, amounts).
• 📊 Results Display: Presents extracted information in a user-friendly table.
• ⬇️ Downloadable Results: Export results as a CSV file.
• 🌐 Responsive UI: Clean and modern frontend built with HTML, CSS, and JavaScript.

Demo Screenshots

Upload Interface	Processing Display

🛠️ Technologies Used

• Frontend: HTML, CSS, JavaScript (Vanilla JS)
• Backend: Python, FastAPI, Uvicorn
• AI & OCR APIs:
  • OpenAI for intelligent data extraction
  • OCR.Space for text extraction from images
• Libraries:
  • Flask, FastAPI, Uvicorn, Requests, Pandas, Python-Dotenv, Pillow
• Environment: Python 3.9+, VS Code/PyCharm

🚀 Getting Started

Follow these steps to set up and run the project locally.

Prerequisites

• Python 3.9+: Download from python.org.
• VS Code: Recommended code editor, download from code.visualstudio.com.
• API Keys:
  • OpenAI API Key (free $5 credit for new users).
  • OCR.Space API Key (free 25,000 requests/month).

Installation

1. Clone the Repository:

   git clone https://github.com/kenreddy23/document-intelligence-system.git
   cd document-intelligence-system

2. Set Up a Virtual Environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Required Libraries:

   pip install requirements.txt

4. Configure Environment Variables: Create a .env file in the root directory and add your API keys:

   OPENAI_API_KEY=your_openai_api_key_here
   OCR_SPACE_API_KEY=your_ocr_space_api_key_here
   

   Important: Add .env to your .gitignore to keep API keys secure.

5. Project Structure:

   document-intelligence/
   ├── backend/
   │   ├── main.py
   │   ├── document_processor.py
   │   └── requirements.txt
   ├── frontend/
   │   ├── index.html
   │   ├── style.css
   │   └── script.js
   ├── uploads/
   ├── results/
   ├── .env
   └── README.md
   

Running the Application

1. Start the Backend:

   cd document-intelligence
   python -m uvicorn backend.main:app --reload --port 8000

2. Access the Frontend: Open your browser and go to http://localhost:8000/static/index.html.

3. Test the System:

   • Upload a document (e.g., invoice, receipt, or business card).
   • Watch the AI extract key information and display it in a table.
   • Download the results as a CSV file.

🧪 Testing

Test the system with various document types:

• Invoice: Should extract company name, date, total amount.
• Receipt: Should extract store name, items, total.
• Business Card: Should extract name, phone, email.
• Contract: Should extract parties, dates, key terms.

🌍 Deployment

To make your application live, you can deploy it to platforms like:

Option 1: Railway (Recommended)

1. Push your code to a GitHub repository.
2. Connect Railway to your GitHub repo.
3. Add environment variables (OPENAI_API_KEY, OCR_SPACE_API_KEY) in the Railway dashboard.
4. Deploy automatically.

Option 2: Heroku

1. Create a Procfile in the root directory:

   web: uvicorn backend.main:app --host=0.0.0.0 --port=${PORT:-5000}
   

2. Push to Heroku.
3. Add environment variables in the Heroku dashboard.

🔮 Next Steps & Enhancements

Here are some ideas to take the project further:

• 🌐 Multi-Language Support: Add support for different OCR languages.
• 📂 Batch Processing: Allow uploading multiple documents at once.
• 🛠️ Custom Templates: Create extraction rules for specific document types.
• 💾 Database Integration: Store processed documents and results.
• 🔒 User Authentication: Add a login system.
• ⚡ Performance Improvements:
  • Caching to avoid reprocessing.
  • Queue system for handling multiple uploads.
  • Image preprocessing for better OCR accuracy.

📚 Learning Outcomes

By building this project, you'll gain skills in:

• API Integration: Working with OpenAI and OCR.Space APIs.
• Full-Stack Development: Building frontend and backend components.
• AI Prompt Engineering: Crafting effective prompts for AI.
• User Experience Design: Creating intuitive interfaces.
• Error Handling: Managing API errors and edge cases.
• Data Processing: Structuring and exporting data.

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Make your changes and commit (git commit -m 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a Pull Request.

Please ensure your code follows the project's style guidelines and includes tests where applicable.

📜 License

This project is licensed under the Apache 2.0. See the LICENSE file for details.

🙌 Acknowledgments

• OpenAI for their powerful AI models.
• OCR.Space for free OCR services.
• The open-source community for providing amazing tools and libraries.

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⭐ Star this repository if you found it helpful!
Feel free to reach out with questions or suggestions. Happy coding! 😄