This project aims to create a powerful, user-friendly tool that helps individuals track and analyze their spending habits. By leveraging advanced technologies like image recognition and AI parsing, this application transforms receipt data into actionable financial insights.

• Receipt Analysis

  • Upload pictures of your purchases or receipts.
  • Extract relevant details such as date, total amount, and itemized list.

• AI Integration

  • Integrates GPT4All to enhance parsing and provide meaningful insights from the extracted data.
  • Identifies spending patterns and suggests budgeting improvements.

• Frontend: HTML & CSS.
• Backend: Python (with Flask for deployment).
• Machine Learning: OpenCV for image recognition and GPT4All for AI parsing.
• Database: PostgreSQL for data storage & AWS S3 for image storage.

1. Upload Receipts: Users can upload images of receipts directly through the application.
2. Data Extraction: Using OpenCV, the system extracts text and relevant information from the receipt images.
3. Parsing with LLMs: GPT4All parses the extracted text to classify and analyze expenses.
4. Storage: The images are forwarded to an Amazon S3 storage bucket, and the contents of the receipt are stored in a PostgreSQL database, tied to specific users.

1. Clone the repository:

   git clone https://github.com/your-repo/budgeting-app.git
   cd budgeting-app

2. Set up a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

• Mobile application for on-the-go budgeting.
• Integration with bank APIs for real-time expense tracking.
• Multi-currency support.
• Cloud-based data storage and user authentication.
• AWS SES and Lambda Function/Message Queues for storage

Contributions are welcome! If you’d like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and push to your fork.
4. Submit a pull request.

This project is licensed under the MIT License.