Lilly Tech Systems
Learn Gradio
Build interactive ML demos and web UIs for your machine learning models using Python. From quick prototypes with Interface() to fully custom layouts with Blocks() — deploy to Hugging Face Spaces in minutes.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Gradio? Why use it for ML demos, key features, comparison with Streamlit, and the Gradio ecosystem.
2. Installation
Install Gradio, set up your environment, build your first demo, and understand the launch() configuration.
3. Interface
Master gr.Interface() for quick demos. Input/output components, Textbox, Image, Audio, Video, File, and more.
4. Blocks
Build custom layouts with gr.Blocks(). Rows, columns, tabs, event handling, state management, and theming.
5. Deployment
Deploy Gradio apps with share=True, Hugging Face Spaces, Docker, API access, and authentication.
6. Best Practices
Performance optimization, error handling, UI/UX design, security, testing, and production tips.
What You'll Learn
By the end of this course, you'll be able to:
Build ML Demos
Create interactive web UIs for any machine learning model with just a few lines of Python code.
Custom Layouts
Design complex multi-component interfaces with Blocks, rows, columns, tabs, and custom themes.
Deploy Anywhere
Share demos instantly or deploy to Hugging Face Spaces, Docker, and cloud platforms.
API Integration
Expose your Gradio apps as REST APIs and integrate them into larger applications.