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

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

3. 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

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Built by AbdEl Kader Seif El Islem RAHMANI