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NeuroCanvas

Apache-2.0 License Python 3.10.11 Streamlit 1.29.0 Built with Love

Welcome to the NeuroCanvas GitHub repository! This project aims to help those with little to no coding knoweldge to create and visualize neuronal networks in an easy user-friendly way.

Features

  • Through the menu on the right the user can select between the model and the trainer setting.
  • When the menu settings are selected the user then can easily add layers or activations to the model
    • When a layer is added the view on the right side of the page automatically updates.
    • The user can view the model as a table or a directed graph.
    • Additionally a code is generated for the made model.
  • Same thing goes for the trainer menu except no graph.
    • Keeping in mind that the user can only add one optimizer and one dataset to avoid any issues with the generated code.
  • You can train models and see the visual evaluations- This is still unstable and was tested with linear models
  • Deployed web application accessible at https://neurocanvas.streamlit.app/

Getting Started

To get started with the NeuroCanvas, follow these steps:

  1. Clone the repository:

    git clone https://github.com/hamdi3/NeuroCanvas.git

  2. Install the required dependencies. We recommend using a virtual environment:

    cd NeuroCanvas
python3.10 -m venv env
source env/bin/activate
pip install -r requirements.txt

  3. Launch the web application:

    streamlit run app.py

  4. Access the web application by opening http://localhost:8501 in your browser.

Contributing

Contributions are welcome and greatly appreciated! To contribute to the NeuroCanvas project, follow these steps:

  1. Fork the repository.

  2. Create a new branch:

    git checkout -b feature/my-feature

  3. Make the desired changes and commit them:

    git commit -m "Add my feature"

  4. Push to the branch:

    git push origin feature/my-feature

  5. Open a pull request in the main repository.

License

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

Contact

If you have any questions, suggestions, or feedback, please feel free to contact me:

I'm open to collaboration and look forward to hearing from you!

Thank you for visiting the PRNU Predictor repository. I hope you find it useful and informative. Happy device identification using PRNU values!