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The HighlightedText component displays text with spans highlighted by category or confidence value.

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Basic usage

import gradio as gr

def ner(text):
    # Returns list of (token, label) tuples
    return [
        ("The", None),
        ("Eiffel", "LOC"),
        ("Tower", "LOC"),
        ("is", None),
        ("in", None),
        ("Paris", "LOC")
    ]

gr.Interface(
    fn=ner,
    inputs=gr.Textbox(),
    outputs=gr.HighlightedText()
).launch()

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Constructor

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value
list[tuple[str, str | float | None]] | dict | Callable | None
default:"None"
Highlighted text as:
  • List of (text, label) tuples
  • Dict with "text" and "entities" keys (Hugging Face format)
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color_map
dict[str, str] | None
default:"None"
Dictionary mapping labels to colors (e.g., {"PER": "red", "LOC": "#00FF00"})
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show_legend
bool
default:"False"
Whether to show category legend separately
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show_inline_category
bool
default:"True"
If False, doesn’t display label inline. Only applies when show_legend=False
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combine_adjacent
bool
default:"False"
If True, merges adjacent tokens with same category
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adjacent_separator
str
default:"''"
Separator between adjacent tokens when combine_adjacent=True
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show_whitespaces
bool
default:"True"
If False, strips leading/trailing whitespace from tokens
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rtl
bool
default:"False"
If True, displays text right-to-left

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Events

  • change - Triggered when highlighted text changes
  • select - Triggered when span is selected

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Examples

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Named Entity Recognition

import gradio as gr

def ner_demo(text):
    # Simulated NER output
    entities = [
        ("Apple", "ORG"),
        (" announced the ", None),
        ("iPhone", "PRODUCT"),
        (" in ", None),
        ("2007", "DATE")
    ]
    return entities

gr.Interface(
    fn=ner_demo,
    inputs=gr.Textbox(),
    outputs=gr.HighlightedText(
        color_map={
            "ORG": "#FFD700",
            "PRODUCT": "#90EE90",
            "DATE": "#87CEEB"
        },
        show_legend=True
    )
).launch()

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Sentiment scores

import gradio as gr

def sentiment(text):
    # Returns (text, confidence) tuples
    words = text.split()
    return [(w, 0.9 if i % 2 == 0 else 0.3) for i, w in enumerate(words)]

gr.Interface(
    fn=sentiment,
    inputs=gr.Textbox(),
    outputs=gr.HighlightedText()
).launch()

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HuggingFace format

import gradio as gr

def ner_hf_format(text):
    return {
        "text": "Apple is a technology company",
        "entities": [
            {"entity": "ORG", "start": 0, "end": 5},
            {"entity": "TYPE", "start": 13, "end": 23}
        ]
    }

gr.Interface(
    fn=ner_hf_format,
    inputs=gr.Button("Analyze"),
    outputs=gr.HighlightedText()
).launch()