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The Label component displays classification results with confidence scores.

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Basic usage

import gradio as gr

def classify(text):
    return {"positive": 0.8, "negative": 0.2}

gr.Interface(
    fn=classify,
    inputs=gr.Textbox(),
    outputs=gr.Label()
).launch()

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Constructor

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value
dict[str, float] | str | float | Callable | None
default:"None"
Default value:
  • Dict of {label: confidence} pairs
  • Single string/number label
  • Function that returns label data
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num_top_classes
int | None
default:"None"
Number of top classes to display
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label
str | None
default:"None"
Label displayed above component
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color
str | None
default:"None"
Background color as CSS color name or hex code
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show_heading
bool
default:"True"
Whether to show the top class as heading

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Events

  • change - Triggered when value changes
  • select - Triggered when label is selected

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Examples

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Multi-class classification

import gradio as gr

def predict(image):
    return {
        "cat": 0.6,
        "dog": 0.3,
        "bird": 0.1
    }

gr.Interface(
    fn=predict,
    inputs=gr.Image(),
    outputs=gr.Label(num_top_classes=3)
).launch()

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Single label

import gradio as gr

def categorize(text):
    return "Category A" if len(text) > 10 else "Category B"

gr.Interface(
    fn=categorize,
    inputs=gr.Textbox(),
    outputs=gr.Label()
).launch()

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With custom color

import gradio as gr

gr.Label(
    value={"success": 1.0},
    color="green"
)