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NeuroForge (Beta) 🚀

Build neural networks with ease! 🧠✨

NeuroForge is an intuitive tool for data preprocessing, analysis, visualization, and neural network creation using a drag-and-drop interface. This beta version provides core functionality while maintaining room for expansion.

✨ Features (Beta)

  • Data Processing 📊

    • CSV and Excel file upload support
    • Basic data preprocessing capabilities
    • Data preview and basic statistics

  • Data Visualization 📈

    • Automated histogram generation for numerical columns
    • Interactive plots using Plotly
    • Basic data insights

  • Neural Network Builder 🧠

    • Drag-and-drop interface for network creation
    • Support for basic PyTorch layers:
      • Linear layers
      • Convolutional layers (Conv2d)
    • CUDA support for GPU acceleration (when available) ⚡

🚀 Installation

# Clone the repository
git clone https://github.com/RAHAMNIabdelkaderseifelislem/neuroforge.git
cd neuroforge

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

🎮 Usage

  1. Start the application:
streamlit run app.py

  1. Access the web interface at http://localhost:8501

  2. Upload your dataset and follow the intuitive UI to:

    • Process and visualize your data 📊
    • Create neural network architectures 🧠
    • Configure and train your models ⚡

📁 Project Structure

neuroforge/
├── src/
│   ├── core/
│   │   ├── data_processor.py
│   │   ├── model_builder.py
│   │   └── model_trainer.py
│   └── ui/
│       └── app.py
├── requirements.txt
└── README.md

🏗️ Architecture

NeuroForge follows clean architecture principles and design patterns:

  • Factory Pattern 🏭: Used for layer creation
  • Builder Pattern 🔨: Implements neural network construction
  • Strategy Pattern 🎯: Handles different data processing approaches
  • Dependency Injection 💉: Manages component dependencies

⚠️ Limitations (Beta)

  • Limited layer types available
  • Basic visualization options
  • Simple data preprocessing capabilities
  • Training functionality is limited

🗺️ Roadmap

  • Add more PyTorch layer types
  • Enhance visualization capabilities
  • Implement advanced data preprocessing
  • Add model export in various formats
  • Improve UI/UX
  • Add comprehensive testing suite

🤝 Contributing

This is a beta version and contributions are welcome! Please feel free to submit issues and pull requests.

📜 License

MIT License - see LICENSE file for details

Built by AbdEl Kader Seif El Islem RAHMANI