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Hugging Face Gradio Skill · Data Ai

Build Interactive ML Interfaces with Hugging Face Gradio

Master Gradio with 12 comprehensive guides.

Build Python-based ML demos and interactive UIs in minutes. Download the skill to automate your frontend workflow.

  • Python
  • Machine Learning
  • UI Framework
  • Hugging Face
  • Web Demos

About This Skill

Leverage 12 detailed guides and 2 core patterns to build or edit Gradio apps. This skill covers everything from the high-level Interface class to low-level Blocks for custom layouts and event listeners.

Quick Start

  1. 1Install gradio via pip
  2. 2Define your Python function
  3. 3Launch the Interface or Blocks app
Example Command
import gradio as gr
def greet(name): return f'Hello {name}!'
gr.Interface(fn=greet, inputs='text', outputs='text').launch()

Core Capabilities

The Interface Class

Quickly wrap functions with input and output components for high-level prototyping and rapid deployment.

Blocks and Event Listeners

Create flexible layouts with explicit event wiring for complex application logic and fine-grained control.

Controlling Layout

Organize components using rows, columns, and tabs to build professional, structured dashboards.

Streaming Outputs and Inputs

Handle real-time data flow for chat interfaces and live model predictions using generator functions.

Usage Examples

Input

Create a simple text greeting app

Output

gr.Interface(fn=greet, inputs='text', outputs='text').launch()

Before

Single column default layout with limited customization.

After

Multi-column custom layout using gr.Row() and gr.Column() for better UX.

Input

Add a button to trigger a function

Output

btn = gr.Button('Submit') btn.click(fn=my_func, inputs=inp, outputs=out)

SKILL.md

---
source: "https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio"
name: hugging-face-gradio
description: Build or edit Gradio apps, layouts, components, and chat interfaces in Python.
risk: unknown
---

# Gradio

## When to Use
Use this skill when a user wants a Gradio demo, UI prototype, or Python-based ML interface.

Gradio is a Python library for building interactive web UIs and ML demos. This skill covers the core API, patterns, and examples.

## Guides

Detailed guides on specific topics (read these when relevant):

- [Quickstart](https://www.gradio.app/guides/quickstart)
- [The Interface Class](https://www.gradio.app/guides/the-interface-class)
- [Blocks and Event Listeners](https://www.gradio.app/guides/blocks-and-event-listeners)
- [Controlling Layout](https://www.gradio.app/guides/controlling-layout)
- [More Blocks Features](https://www.gradio.app/guides/more-blocks-features)
- [Custom CSS and JS](https://www.gradio.app/guides/custom-CSS-and-JS)
- [Streaming Outputs](https://www.gradio.app/guides/streaming-outputs)
- [Streaming Inputs](https://www.gradio.app/guides/streaming-inputs)
- [Sharing Your App](https://www.gradio.app/guides/sharing-your-app)
- [Custom HTML Components](https://www.gradio.app/guides/custom-HTML-components)
- [Getting Started with the Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client)
- [Getting Started with the JS Client](https://www.gradio.app/guides/getting-started-with-the-js-client)

## Core Patterns

**Interface** (high-level): wraps a function with input/output components.

```python
import gradio as gr

def greet(name):
    return f"Hello {name}!"

gr.Interface(fn=greet, inputs="text", outputs="text").launch()
```

**Blocks** (low-level): flexible layout with explicit event wiring.

```python
import gradio as gr

with gr.Blocks() as demo:
    name = gr.Textbox(label="Name")
    output = gr.Textbox(label="Greeting")
    btn = gr.Button("Greet")
    btn.click(fn=lambda n: f

Frequently Asked Questions

FAQ

Is Gradio compatible with other Python tools?
Yes, Gradio works seamlessly with popular libraries like NumPy, Pandas, Matplotlib, and all major ML frameworks like PyTorch and TensorFlow.
Who is the target audience for this skill?
It is designed for Python developers, data scientists, and ML engineers who want to create web-based demos for their models without learning HTML/CSS/JS.
How does Gradio differ from alternatives like Streamlit?
Gradio is specifically optimized for ML model interaction and provides built-in support for components like image editors and audio players, whereas Streamlit is more general-purpose for data dashboards.
Does this skill support multiple languages?
The core library is Python-based, but this skill also covers the Python and JavaScript clients for interacting with hosted Gradio apps programmatically.
What results can I expect after using this skill?
You will be able to build, customize, and share interactive ML prototypes and chat interfaces with custom CSS, streaming capabilities, and complex layouts.

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Trigger Phrases

Use these phrases to activate this skill in your AI coding assistant:

create a gradio appbuild an ml interfacegradio blocks exampleadd custom css to gradiostream outputs in gradio