Gradio is an open-source Python library that enables rapid creation of web applications for machine learning models without requiring frontend development expertise. Built on FastAPI and Svelte, it allows data scientists to deploy interactive demos through simple Python code, supporting everything from basic interfaces to complex multi-component applications with custom layouts, state management, and real-time streaming. Gradio apps can be shared instantly with automatically generated public URLs, hosted permanently on Hugging Face Spaces, or embedded as web components in existing websites, making ML models accessible to anyone with a browser.
What This Cheat Sheet Covers
This topic spans 15 focused tables and 114 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core Interface Types
| Class | Example | Description |
|---|---|---|
gr.Interface(fn=predict, inputs="image", outputs="label").launch() | Basic ML demo with single function • Automatically generates UI from function signature • Best for simple input-output workflows | |
with gr.Blocks() as demo: btn = gr.Button() btn.click(fn, inputs, outputs) | Low-level builder for custom layouts • Full control over component placement and event flows • Supports complex multi-step interactions |