README.md

PlexeAI

Create ML models from natural language descriptions

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🚀 Features

• 🤖 AI-Powered Model Creation - Build ML models using natural language descriptions
• 📊 Automated Training - Upload your data and let PlexeAI handle the rest
• ⚡ Async Support - Built-in async interfaces for high-performance applications
• 🔄 Batch Processing - Efficient batch prediction capabilities
• 🛠️ Simple API - Intuitive interface for both beginners and experts

📦 Installation

pip install plexe

🏃‍♂️ Quickstart

import plexe

# Create a model in seconds
model_version = plexe.build(
    goal="predict customer churn based on usage patterns",
    model_name="churn-predictor",
    data_files="customer_data.csv"
)

# Make predictions
result = plexe.infer(
    model_name="churn-predictor",
    model_version=model_version,
    input_data={
        "usage": 100,
        "tenure": 12,
        "plan_type": "premium"
    }
)

🎯 Example Use Cases

• 📈 Churn Prediction: Predict customer churn using historical data
• 🏷️ Classification: Categorize text, images, or any structured data
• 📊 Regression: Predict numerical values like sales or pricing
• 🔄 Time Series: Forecast trends and patterns in sequential data

🔥 Advanced Usage

Batch Predictions

results = plexe.batch_infer(
    model_name="churn-predictor",
    model_version=model_version,
    inputs=[
        {"usage": 100, "tenure": 12, "plan_type": "premium"},
        {"usage": 50, "tenure": 6, "plan_type": "basic"}
    ]
)

Async Support

async def main():
    model_version = await plexe.abuild(
        goal="predict customer churn",
        model_name="churn-predictor",
        data_files="customer_data.csv"
    )
    
    result = await plexe.ainfer(
        model_name="churn-predictor",
        model_version=model_version,
        input_data={"usage": 100, "tenure": 12}
    )

Direct Client Usage

from plexe import PlexeAI

with PlexeAI(api_key="your_api_key_here") as client:
    # Upload data
    upload_id = client.upload_files("customer_data.csv")
    
    # Create and use model
    model_version = client.build(
        goal="predict customer churn",
        model_name="churn-predictor",
        upload_id=upload_id
    )

📚 Documentation

Check out our comprehensive documentation for:

• Detailed API reference
• Advanced usage examples
• Best practices
• Tutorials and guides

🛠️ Development

# Clone the repository
git clone https://github.com/plexe-ai/plexe
cd plexe

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

🤝 Contributing

We welcome contributions!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by Plexe AI