neuroflow-ml 0.1.0

A local-first, node-based ML orchestrator with a visual pipeline builder

🧠 NeuroFlow

A local-first, node-based ML orchestrator with a visual pipeline builder.

✨ Features

• 🎨 Visual Pipeline Builder - Drag-and-drop nodes to create ML workflows
• 🔌 Node-Based Architecture - Connect data loaders, preprocessors, models, and evaluators
• 📊 Built-in Visualizations - View metrics, charts, and model performance
• 💾 Local-First - All data stays on your machine
• 📦 Export/Import - Save and share workflows as JSON
• 🚀 One Command - Install and run with a single command

🚀 Quick Start

Installation

# Install from PyPI (coming soon)
pip install neuroflow

# Or install from source
git clone https://github.com/aayushgokhale/neuroflow.git
cd neuroflow/NeuroFlow
pip install -e .

Running NeuroFlow

# Start NeuroFlow (opens browser automatically)
neuroflow

# Or specify a custom port
neuroflow --port 3000

# Don't open browser automatically
neuroflow --no-browser

Visit http://localhost:8000 to access the visual pipeline builder.

📖 Usage

Building a Pipeline

1. Drag nodes from the left sidebar onto the canvas
2. Connect nodes by dragging from output ports to input ports
3. Configure nodes by clicking on them and editing parameters
4. Run the workflow by clicking the "Run Workflow" button
5. View results in the Results Panel with metrics and charts

Node Types

Category	Nodes
Data	CSV Loader, SQL Query, API Fetch
Preprocessing	Normalize, Encode, Split Data
Models	Random Forest, Neural Net, XGBoost
Evaluate	Accuracy Chart, Loss Curve, Confusion Matrix

Example Workflow

CSV Loader → Split Data → Random Forest → Evaluator

🛠️ Development

Prerequisites

• Python 3.9+
• Node.js 18+
• pnpm (recommended)

Setup

# Clone the repository
git clone https://github.com/aayushgokhale/neuroflow.git
cd neuroflow

# Install backend dependencies
cd NeuroFlow
pip install -e ".[dev]"

# Install frontend dependencies
cd ../Frontend
pnpm install

# Run development servers
# Terminal 1: Backend
cd NeuroFlow && uvicorn app.main:app --reload

# Terminal 2: Frontend
cd Frontend && pnpm dev

Building for Distribution

cd NeuroFlow
./build.sh
pip install -e .

📁 Project Structure

neuroflow/
├── Frontend/                 # Next.js frontend
│   ├── app/                  # App router pages
│   ├── components/           # React components
│   │   └── neuroflow/        # NeuroFlow-specific components
│   └── public/               # Static assets
├── NeuroFlow/                # Python backend package
│   ├── app/                  # FastAPI application
│   │   ├── cli.py            # CLI entry point
│   │   ├── main.py           # FastAPI app
│   │   ├── engine.py         # Workflow execution engine
│   │   ├── schemas.py        # Pydantic models
│   │   └── executors/        # Node executors
│   ├── data/                 # Demo datasets
│   ├── static/               # Built frontend (after build)
│   └── pyproject.toml        # Package configuration

🔧 API Reference

REST Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/workflows/execute	Execute a workflow
GET	/workflows/{id}/status	Get execution status
POST	/data/upload	Upload data files
GET	/models/{id}/download	Download trained model

WebSocket

Connect to /ws/workflow/{run_id} for real-time execution progress updates.

📝 License

MIT License - see LICENSE for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📧 Contact

Created by Aayush Gokhale