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The Interface class is Gradio’s main high-level abstraction for creating web-based UIs around Python functions. It provides a simple way to wrap any function with input and output components, making it ideal for quick demos and prototypes.

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What is an Interface?

An Interface allows you to create a web-based GUI around a machine learning model (or any Python function) in just a few lines of code. You specify three main parameters:
  1. The function to wrap (fn)
  2. The input components (inputs)
  3. The output components (outputs)
Gradio automatically generates the UI, handles user interactions, and displays results.

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Basic usage

Here’s the simplest possible Interface: 
import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
demo.launch()
This creates a complete web interface with:
  • An input textbox for entering a name
  • A submit button
  • An output textbox showing the greeting

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Using component shortcuts

You can specify components using string shortcuts (like "textbox", "image", "label") or instantiate them explicitly: 
import gradio as gr

def image_classifier(img):
    return {'cat': 0.3, 'dog': 0.7}

demo = gr.Interface(
    fn=image_classifier,
    inputs="image",
    outputs="label"
)
demo.launch()
For more control, use component objects: 
import gradio as gr

demo = gr.Interface(
    fn=image_classifier,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3)
)

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Multiple inputs and outputs

You can pass lists of components for multiple inputs or outputs: 
import gradio as gr

def process_data(text, temperature, enable_feature):
    result = f"Text: {text}, Temp: {temperature}, Feature: {enable_feature}"
    return result, temperature * 2

demo = gr.Interface(
    fn=process_data,
    inputs=[
        gr.Textbox(label="Input Text"),
        gr.Slider(0, 100, label="Temperature"),
        gr.Checkbox(label="Enable Feature")
    ],
    outputs=[
        gr.Textbox(label="Result"),
        gr.Number(label="Double Temperature")
    ]
)
The number of function parameters must match the number of input components, and the number of return values must match the number of output components.

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Key parameters

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Title and description

Add metadata to make your Interface more informative: 
demo = gr.Interface(
    fn=greet,
    inputs="textbox",
    outputs="textbox",
    title="Greeting Generator",
    description="Enter your name to receive a personalized greeting."
)

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Examples

Provide example inputs to help users get started: 
demo = gr.Interface(
    fn=greet,
    inputs="textbox",
    outputs="textbox",
    examples=[["Alice"], ["Bob"], ["Charlie"]]
)

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Additional inputs

Use additional_inputs to add optional parameters that appear in a collapsible accordion: 
demo = gr.Interface(
    fn=process_text,
    inputs="textbox",
    outputs="textbox",
    additional_inputs=[
        gr.Slider(0, 100, value=50, label="Max Length"),
        gr.Dropdown(["formal", "casual"], label="Style")
    ],
    additional_inputs_accordion="Advanced Options"
)

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Live mode

Set live=True to automatically update outputs when inputs change (no submit button needed): 
demo = gr.Interface(
    fn=lambda x: x.upper(),
    inputs="textbox",
    outputs="textbox",
    live=True
)

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Working with state

For stateful applications, you can use the special "state" component: 
import gradio as gr

def increment(count, state):
    state = state + 1
    return f"Count: {state}", state

demo = gr.Interface(
    fn=increment,
    inputs=["textbox", "state"],
    outputs=["textbox", "state"]
)
When using state, there must be exactly one state input and one state output.

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Loading from pipelines

The Interface.from_pipeline() class method creates an Interface from Hugging Face transformers pipelines: 
import gradio as gr
from transformers import pipeline

pipe = pipeline("image-classification")
demo = gr.Interface.from_pipeline(pipe)
demo.launch()
Gradio automatically determines the appropriate input and output components based on the pipeline type.

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API endpoints

Every Interface automatically generates API endpoints. Control their visibility: 
demo = gr.Interface(
    fn=greet,
    inputs="textbox",
    outputs="textbox",
    api_name="greet_user",  # Custom endpoint name
    api_visibility="public"  # "public", "private", or "undocumented"
)

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Comparison with Blocks

Interface is ideal for:
  • Quick prototypes and demos
  • Single-function applications
  • Simple input-output workflows
Use Blocks instead when you need:
  • Custom layouts and multi-page apps
  • Multiple interconnected functions
  • Complex event handling and data flows

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See also

  • Blocks - For more complex layouts and workflows
  • ChatInterface - Specialized interface for chatbots
  • Components - Available input/output components
  • Events - Understanding event listeners