ML Playground Dashboard

What is ML Playground Dashboard ?

ML Playground Dashboard is an interactive Gradio app designed for exploring machine learning models. It provides a user-friendly interface to detect harmful or offensive content in images while allowing users to experiment with different models for text, images, and speech. This tool is perfect for developers and researchers looking to prototype and test ML solutions quickly.

Features

• Model Exploration: Test and compare multiple ML models for image, text, and speech processing.
• Real-Time Detection: Identify harmful or offensive content in images using pre-trained models.
• Content Screening: Automatically flag and review potentially inappropriate material.
• Multi-Modal Support: Work with images, text, and speech in a single dashboard.
• Customization: Fine-tune model settings and thresholds for specific use cases.
• Interactive Demo: Experience a hands-on demonstration of ML capabilities.

How to use ML Playground Dashboard ?

1. Install the App: Run pip install ml-playground-dashboard to install the Gradio app.
2. Launch the Dashboard: Execute ml-playground-dashboard in your terminal to start the app.
3. Navigate the UI: Access the dashboard via the provided URL and explore its features.
4. Select a Model: Choose from pre-trained models for image, text, or speech analysis.
5. Upload Input: Provide input (image, text, or audio) to test the model’s functionality.
6. Review Results: Analyze the output and adjust settings as needed for better accuracy.
7. Customize Settings: Modify thresholds or parameters to optimize performance for your use case.

Frequently Asked Questions

What types of content can ML Playground Dashboard detect?
ML Playground Dashboard specializes in detecting harmful or offensive content in images, but it also supports text and speech analysis for similar purposes.

How do I customize the model settings?
You can adjust model thresholds and parameters directly through the dashboard interface. Experiment with different settings to optimize accuracy for your specific use case.

Is ML Playground Dashboard suitable for production use?
While it is primarily designed for prototyping and testing, you can deploy it in production environments by integrating it with your existing infrastructure and scaling solutions.