Official agent skill

Gradio App Builder

by huggingface in huggingface/skills

Guides building Gradio web UIs and ML demos in Python, covering Interface, Blocks, ChatInterface, event listeners, layouts and chatbots.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Gradio App Builder

skills CLI
$ npx skills add huggingface/skills --skill huggingface-gradio -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install huggingface/skills huggingface-gradio --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/huggingface-gradio .claude/skills/huggingface-gradio && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
huggingface-gradio
GitHub stars
11k
Used in
4 other repos
Token cost
~6.2k tokens
SKILL.md length
2,265 words
Files
2
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guides building Gradio web UIs and ML demos in Python, covering Interface, Blocks, ChatInterface, event listeners, layouts and chatbots.

  • Building a quick web demo for a model or a function
  • SKILL.md covers Guides, Core Patterns, Key Component Signatures and Custom HTML Components, plus 3 more sections
  • Reaches upload.wikimedia.org
  • Wiring component events in a Blocks layout

What it does

A reference for writing Gradio apps in Python. It explains three core patterns: Interface, which wraps a function with input and output components; Blocks, which gives flexible layout with explicit event wiring; and ChatInterface, a ready-made wrapper for chatbot UIs. Short Python examples accompany each.

The skill links to the official Gradio guides for deeper topics: the quickstart, the Interface class, Blocks and event listeners, layout control, custom CSS and JS, streaming inputs and outputs, sharing an app, custom HTML components and the Python and JavaScript clients. An examples.md file is bundled, and a section on key component signatures follows the core patterns.

When your agent uses it

  • Building a quick web demo for a model or a function
  • Wiring component events in a Blocks layout
  • Creating a chatbot interface
  • Editing the layout of an existing Gradio app

Example prompts

  • “Build a Gradio demo that classifies an uploaded image.”
  • “Add a chat interface to my Python script with Gradio.”
  • “Rearrange this Blocks layout into two columns with tabs.”

Requirements

  • Python with the gradio package

What it can do on your machine

Read from SKILL.md and the folder at commit c3ff942. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • upload.wikimedia.org

    Also links to:

    • gradio.app

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gradio App Builder loads about 6.2k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 2,265 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~6.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from huggingface/skills at commit c3ff942, republished under its Apache-2.0 licence (© huggingface). 2,265 words, ~6,183 tokens.

Download SKILL.mdSave it as .claude/skills/huggingface-gradio/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
huggingface-gradio
description
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

Gradio

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):

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"Hello {n}!", inputs=name, outputs=output)

demo.launch()

ChatInterface: high-level wrapper for chatbot UIs.

python
import gradio as gr

def respond(message, history):
    return f"You said: {message}"

gr.ChatInterface(fn=respond).launch()

Key Component Signatures

Textbox(value: str | I18nData | Callable | None = None, type: Literal['text', 'password', 'email'] = "text", lines: int = 1, max_lines: int | None = None, placeholder: str | I18nData | None = None, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, autofocus: bool = False, autoscroll: bool = True, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", text_align: Literal['left', 'right'] | None = None, rtl: bool = False, buttons: list[Literal['copy'] | Button] | None = None, max_length: int | None = None, submit_btn: str | bool | None = False, stop_btn: str | bool | None = False, html_attributes: InputHTMLAttributes | None = None)

Creates a textarea for user to enter string input or display string output..

Number(value: float | Callable | None = None, label: str | I18nData | None = None, placeholder: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None, precision: int | None = None, minimum: float | None = None, maximum: float | None = None, step: float = 1)

Creates a numeric field for user to enter numbers as input or display numeric output..

Slider(minimum: float = 0, maximum: float = 100, value: float | Callable | None = None, step: float | None = None, precision: int | None = None, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", randomize: bool = False, buttons: list[Literal['reset']] | None = None)

Creates a slider that ranges from {minimum} to {maximum} with a step size of {step}..

Checkbox(value: bool | Callable = False, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None)

Creates a checkbox that can be set to True or False.

Dropdown(choices: Sequence[str | int | float | tuple[str, str | int | float]] | None = None, value: str | int | float | Sequence[str | int | float] | Callable | DefaultValue | None = DefaultValue(), type: Literal['value', 'index'] = "value", multiselect: bool | None = None, allow_custom_value: bool = False, max_choices: int | None = None, filterable: bool = True, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None)

Creates a dropdown of choices from which a single entry or multiple entries can be selected (as an input component) or displayed (as an output component)..

Radio(choices: Sequence[str | int | float | tuple[str, str | int | float]] | None = None, value: str | int | float | Callable | None = None, type: Literal['value', 'index'] = "value", label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", rtl: bool = False, buttons: list[Button] | None = None)

Creates a set of (string or numeric type) radio buttons of which only one can be selected..

Image(value: str | PIL.Image.Image | np.ndarray | Callable | None = None, format: str = "webp", height: int | str | None = None, width: int | str | None = None, image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None = "RGB", sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None = None, type: Literal['numpy', 'pil', 'filepath'] = "numpy", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, buttons: list[Literal['download', 'share', 'fullscreen'] | Button] | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, streaming: bool = False, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", webcam_options: WebcamOptions | None = None, placeholder: str | None = None, watermark: WatermarkOptions | None = None)

Creates an image component that can be used to upload images (as an input) or display images (as an output)..

Audio(value: str | Path | tuple[int, np.ndarray] | Callable | None = None, sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None = None, type: Literal['numpy', 'filepath'] = "numpy", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, streaming: bool = False, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", format: Literal['wav', 'mp3'] | None = None, autoplay: bool = False, editable: bool = True, buttons: list[Literal['download', 'share'] | Button] | None = None, waveform_options: WaveformOptions | dict | None = None, loop: bool = False, recording: bool = False, subtitles: str | Path | list[dict[str, Any]] | None = None, playback_position: float = 0)

Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output)..

Video(value: str | Path | Callable | None = None, format: str | None = None, sources: list[Literal['upload', 'webcam']] | Literal['upload', 'webcam'] | None = None, height: int | str | None = None, width: int | str | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", webcam_options: WebcamOptions | None = None, include_audio: bool | None = None, autoplay: bool = False, buttons: list[Literal['download', 'share'] | Button] | None = None, loop: bool = False, streaming: bool = False, watermark: WatermarkOptions | None = None, subtitles: str | Path | list[dict[str, Any]] | None = None, playback_position: float = 0)

Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).

File(value: str | list[str] | Callable | None = None, file_count: Literal['single', 'multiple', 'directory'] = "single", file_types: list[str] | None = None, type: Literal['filepath', 'binary'] = "filepath", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, height: int | str | float | None = None, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", allow_reordering: bool = False, buttons: list[Button] | None = None)

Creates a file component that allows uploading one or more generic files (when used as an input) or displaying generic files or URLs for download (as output).

Show full SKILL.md (912 more words)Show less
Chatbot(value: list[MessageDict | Message] | Callable | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, autoscroll: bool = True, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", height: int | str | None = 400, resizable: bool = False, max_height: int | str | None = None, min_height: int | str | None = None, editable: Literal['user', 'all'] | None = None, latex_delimiters: list[dict[str, str | bool]] | None = None, rtl: bool = False, buttons: list[Literal['share', 'copy', 'copy_all'] | Button] | None = None, watermark: str | None = None, avatar_images: tuple[str | Path | None, str | Path | None] | None = None, sanitize_html: bool = True, render_markdown: bool = True, feedback_options: list[str] | tuple[str, ...] | None = ('Like', 'Dislike'), feedback_value: Sequence[str | None] | None = None, line_breaks: bool = True, layout: Literal['panel', 'bubble'] | None = None, placeholder: str | None = None, examples: list[ExampleMessage] | None = None, allow_file_downloads: <class 'inspect._empty'> = True, group_consecutive_messages: bool = True, allow_tags: list[str] | bool = True, reasoning_tags: list[tuple[str, str]] | None = None, like_user_message: bool = False)

Creates a chatbot that displays user-submitted messages and responses.

Button(value: str | I18nData | Callable = "Run", every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, variant: Literal['primary', 'secondary', 'stop', 'huggingface'] = "secondary", size: Literal['sm', 'md', 'lg'] = "lg", icon: str | Path | None = None, link: str | None = None, link_target: Literal['_self', '_blank', '_parent', '_top'] = "_self", visible: bool | Literal['hidden'] = True, interactive: bool = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", scale: int | None = None, min_width: int | None = None)

Creates a button that can be assigned arbitrary .click() events.

Markdown(value: str | I18nData | Callable | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, rtl: bool = False, latex_delimiters: list[dict[str, str | bool]] | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", sanitize_html: bool = True, line_breaks: bool = False, header_links: bool = False, height: int | str | None = None, max_height: int | str | None = None, min_height: int | str | None = None, buttons: list[Literal['copy']] | None = None, container: bool = False, padding: bool = False)

Used to render arbitrary Markdown output.

HTML(value: Any | Callable | None = None, label: str | I18nData | None = None, html_template: str = "${value}", css_template: str = "", js_on_load: str | None = "element.addEventListener('click', function() { trigger('click') });", apply_default_css: bool = True, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool = False, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", min_height: int | None = None, max_height: int | None = None, container: bool = False, padding: bool = False, autoscroll: bool = False, buttons: list[Button] | None = None, server_functions: list[Callable] | None = None, props: Any)

Creates a component with arbitrary HTML.

Custom HTML Components

If a task requires significant customization of an existing component or a component that doesn't exist in Gradio, you can create one with gr.HTML. It supports html_template (with ${} JS expressions and {{}} Handlebars syntax), css_template for scoped styles, and js_on_load for interactivity — where props.value updates the component value and trigger('event_name') fires Gradio events. For reuse, subclass gr.HTML and define api_info() for API/MCP support. See the full guide.

Here's an example that shows how to create and use these kinds of components:

python
import gradio as gr

class StarRating(gr.HTML):
    def __init__(self, label, value=0, **kwargs):
        html_template = """
        <h2>${label} rating:</h2>
        ${Array.from({length: 5}, (_, i) => `<img class='${i < value ? '' : 'faded'}' src='https://upload.wikimedia.org/wikipedia/commons/d/df/Award-star-gold-3d.svg'>`).join('')}
        """
        css_template = """
            img { height: 50px; display: inline-block; cursor: pointer; }
            .faded { filter: grayscale(100%); opacity: 0.3; }
        """
        js_on_load = """
            const imgs = element.querySelectorAll('img');
            imgs.forEach((img, index) => {
                img.addEventListener('click', () => {
                    props.value = index + 1;
                });
            });
        """
        super().__init__(value=value, label=label, html_template=html_template, css_template=css_template, js_on_load=js_on_load, **kwargs)

    def api_info(self):
        return {"type": "integer", "minimum": 0, "maximum": 5}


with gr.Blocks() as demo:
    gr.Markdown("# Restaurant Review")
    food_rating = StarRating(label="Food", value=3)
    service_rating = StarRating(label="Service", value=3)
    ambience_rating = StarRating(label="Ambience", value=3)
    average_btn = gr.Button("Calculate Average Rating")
    rating_output = StarRating(label="Average", value=3)
    def calculate_average(food, service, ambience):
        return round((food + service + ambience) / 3)
    average_btn.click(
        fn=calculate_average,
        inputs=[food_rating, service_rating, ambience_rating],
        outputs=rating_output
    )

demo.launch()

Event Listeners

All event listeners share the same signature:

python
component.event_name(
    fn: Callable | None | Literal["decorator"] = "decorator",
    inputs: Component | Sequence[Component] | set[Component] | None = None,
    outputs: Component | Sequence[Component] | set[Component] | None = None,
    api_name: str | None = None,
    api_description: str | None | Literal[False] = None,
    scroll_to_output: bool = False,
    show_progress: Literal["full", "minimal", "hidden"] = "full",
    show_progress_on: Component | Sequence[Component] | None = None,
    queue: bool = True,
    batch: bool = False,
    max_batch_size: int = 4,
    preprocess: bool = True,
    postprocess: bool = True,
    cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
    trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
    js: str | Literal[True] | None = None,
    concurrency_limit: int | None | Literal["default"] = "default",
    concurrency_id: str | None = None,
    api_visibility: Literal["public", "private", "undocumented"] = "public",
    time_limit: int | None = None,
    stream_every: float = 0.5,
    key: int | str | tuple[int | str, ...] | None = None,
    validator: Callable | None = None,
) -> Dependency

Supported events per component:

  • AnnotatedImage: select
  • Audio: stream, change, clear, play, pause, stop, pause, start_recording, pause_recording, stop_recording, upload, input
  • BarPlot: select, double_click
  • BrowserState: change
  • Button: click
  • Chatbot: change, select, like, retry, undo, example_select, option_select, clear, copy, edit
  • Checkbox: change, input, select
  • CheckboxGroup: change, input, select
  • ClearButton: click
  • Code: change, input, focus, blur
  • ColorPicker: change, input, submit, focus, blur
  • Dataframe: change, input, select, edit
  • Dataset: click, select
  • DateTime: change, submit
  • DeepLinkButton: click
  • Dialogue: change, input, submit
  • DownloadButton: click
  • Dropdown: change, input, select, focus, blur, key_up
  • DuplicateButton: click
  • File: change, select, clear, upload, delete, download
  • FileExplorer: change, input, select
  • Gallery: select, upload, change, delete, preview_close, preview_open
  • HTML: change, input, click, double_click, submit, stop, edit, clear, play, pause, end, start_recording, pause_recording, stop_recording, focus, blur, upload, release, select, stream, like, example_select, option_select, load, key_up, apply, delete, tick, undo, retry, expand, collapse, download, copy
  • HighlightedText: change, select
  • Image: clear, change, stream, select, upload, input
  • ImageEditor: clear, change, input, select, upload, apply
  • ImageSlider: clear, change, stream, select, upload, input
  • JSON: change
  • Label: change, select
  • LinePlot: select, double_click
  • LoginButton: click
  • Markdown: change, copy
  • Model3D: change, upload, edit, clear
  • MultimodalTextbox: change, input, select, submit, focus, blur, stop
  • Navbar: change
  • Number: change, input, submit, focus, blur
  • ParamViewer: change, upload
  • Plot: change
  • Radio: select, change, input
  • ScatterPlot: select, double_click
  • SimpleImage: clear, change, upload
  • Slider: change, input, release
  • State: change
  • Textbox: change, input, select, submit, focus, blur, stop, copy
  • Timer: tick
  • UploadButton: click, upload
  • Video: change, clear, start_recording, stop_recording, stop, play, pause, end, upload, input

Prediction CLI

The gradio CLI includes info and predict commands for interacting with Gradio apps programmatically. These are especially useful for coding agents that need to use Spaces in their workflows.

gradio info — Discover endpoints and parameters
bash
gradio info <space_id_or_url>

Returns a JSON payload describing all endpoints, their parameters (with types and defaults), and return values.

bash
gradio info gradio/calculator
# {
#   "/predict": {
#     "parameters": [
#       {"name": "num1", "required": true, "default": null, "type": {"type": "number"}},
#       {"name": "operation", "required": true, "default": null, "type": {"enum": ["add", "subtract", "multiply", "divide"], "type": "string"}},
#       {"name": "num2", "required": true, "default": null, "type": {"type": "number"}}
#     ],
#     "returns": [{"name": "output", "type": {"type": "number"}}],
#     "description": ""
#   }
# }

File-type parameters show "type": "filepath" with instructions to include "meta": {"_type": "gradio.FileData"} — this signals the file will be uploaded to the remote server.

gradio predict — Send predictions
bash
gradio predict <space_id_or_url> <endpoint> <json_payload>

Returns a JSON object with named output keys.

bash
# Simple numeric prediction
gradio predict gradio/calculator /predict '{"num1": 5, "operation": "multiply", "num2": 3}'
# {"output": 15}

# Image generation
gradio predict black-forest-labs/FLUX.2-dev /infer '{"prompt": "A majestic dragon"}'
# {"Result": "/tmp/gradio/.../image.webp", "Seed": 1117868604}

# File upload (must include meta key)
gradio predict gradio/image_mod /predict '{"image": {"path": "/path/to/image.png", "meta": {"_type": "gradio.FileData"}}}'
# {"output": "/tmp/gradio/.../output.png"}

Both commands accept --token for accessing private Spaces.

Additional Reference

© huggingface, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/huggingface-gradio of huggingface/skills.

  • SKILL.md
  • examples.md

Open the folder on GitHubat commit c3ff942

Used in 4 other repositories

We found 14 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in huggingface/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Gradio App Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gradio App Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gradio App Builder this skillhuggingface/skills11k4 repos~6.2kAutomated safety check: PassApache-2.0
Kiln ConventionsKiln-AI/Kiln5.2k—~1.7kAutomated safety check: NotesCustom licence
Dstack Prototypingdstackai/dstack2.3k—~1.6kAutomated safety check: PassMPL-2.0
LLaVA Vision-Language ModelOrchestra-Research/AI-Research-SKILLs13k6 repos~2kAutomated safety check: PassMIT
ComfyUI Frontend Extensionsjtydhr88/comfyui-custom-node-skills295—~3.7kAutomated safety check: PassMIT
Fast Agent Designevalstate/fast-agent3.9k—~220Automated safety check: PassApache-2.0

Similar skills

  • Kiln Conventions

    Kiln-AI/Kiln

    Kiln's code conventions and per-area gotchas, plus a diff gate that enforces the mechanical ones.

    5.2k GitHub stars~1.7k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check: notes
  • Dstack Prototyping

    dstackai/dstack

    Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven.

    2.3k GitHub stars~1.6k tokensUpdated today
    AI & LLM EngineeringAuto-check passed
  • LLaVA Vision-Language Model

    Orchestra-Research/AI-Research-SKILLs

    Guide to LLaVA for image chat, visual question answering and captioning, with model sizes, CLI and Gradio usage and multi-turn conversation code.

    13k GitHub starsUsed in 6 repos~2k tokens
    AI & LLM EngineeringAuto-check passed
  • ComfyUI Frontend Extensions

    jtydhr88/comfyui-custom-node-skills

    Guide to writing JavaScript extensions for the ComfyUI frontend from custom nodes, covering lifecycle hooks, widgets, sidebar tabs, commands, settings, toasts and dialogs.

    295 GitHub stars~3.7k tokensUpdated 2 mo ago
    Frontend & DesignAuto-check passed
  • Fast Agent Design

    evalstate/fast-agent

    A skill your agent uses to generate well-branded interfaces and assets for fast-agent, either for production or throwaway prototypes/mocks/etc.

    3.9k GitHub stars~220 tokensUpdated today
    Frontend & DesignAuto-check passed
  • Google Agents CLI Adk Code

    pifferologo/cloud-agents-cli

    This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management", or needs ADK (Agent…

    129 GitHub starsUsed in 1 repo~768 tokens
    AI & LLM EngineeringAuto-check passed

More from huggingface/skills

All 25 skills in this repo
  • Official

    Finds or validates a usable SageMaker execution role before deploying or training, so scripts do not try to create IAM roles they lack permission to create.

    11k GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed
  • Official

    Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.

    11k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed
  • Official

    Routes a sentence-transformers training task to the right model type and required reference docs and example scripts, covering bi-encoders, rerankers, sparse and multi-vector models.

    11k GitHub starsUsed in 1 repo~2.6k tokens
    Auto-check passed
  • Official

    Sets up an isolated Python environment with a supported interpreter and current boto3 before any SageMaker deployment, training or AWS automation code runs.

    11k GitHub starsUsed in 2 repos~1.7k tokens
    Auto-check passed
  • Official

    Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.

    11k GitHub starsUsed in 2 repos~1.6k tokens
    Auto-check passed
  • Official

    Deploys SageMaker endpoints with autoscaling, CloudWatch alarms and tags on by default, using scripts for real-time, scale-to-zero and async setups.

    11k GitHub starsUsed in 1 repo~6.9k tokens
    Auto-check passed

Works with

Questions about Gradio App Builder

What does Gradio App Builder do?

Guides building Gradio web UIs and ML demos in Python, covering Interface, Blocks, ChatInterface, event listeners, layouts and chatbots. A reference for writing Gradio apps in Python. It explains three core patterns: Interface, which wraps a function with input and output components; Blocks, which gives flexible layout with explicit event wiring; and ChatInterface, a ready-made wrapper for chatbot UIs.

When should I use Gradio App Builder?

Gradio App Builder fits situations like: building a quick web demo for a model or a function; wiring component events in a Blocks layout; creating a chatbot interface; editing the layout of an existing Gradio app.

How do I install Gradio App Builder in Claude Code?

Run `npx skills add huggingface/skills --skill huggingface-gradio -a claude-code`. Or copy the skill folder (skills/huggingface-gradio in huggingface/skills) into .claude/skills/huggingface-gradio in your project. Claude Code loads it when a task matches its description.

How do I install Gradio App Builder in Codex?

Run `npx skills add huggingface/skills --skill huggingface-gradio -a codex`. Or copy the skill folder (skills/huggingface-gradio in huggingface/skills) into .agents/skills/huggingface-gradio in your project. Codex loads it when a task matches its description.

Can I use Gradio App Builder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add huggingface/skills --skill huggingface-gradio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/huggingface-gradio, .gemini/skills/huggingface-gradio, .github/skills/huggingface-gradio and .opencode/skills/huggingface-gradio in your project.

What does Gradio App Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Gradio App Builder is instructions for the agent only. Our summary lists: Python with the gradio package.

Does Gradio App Builder access the network?

SKILL.md names 2 domains. In commands or code: upload.wikimedia.org; the agent is likely to contact it when it follows the instructions. As links in the text: gradio.app. This is read from the text; nothing was executed.

Is Gradio App Builder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gradio App Builder use?

Gradio App Builder is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gradio App Builder use?

About 6.2k tokens (SKILL.md is roughly 25k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gradio App Builder?

Skills that share tags, products or a category with Gradio App Builder: Kiln Conventions (Kiln-AI/Kiln, 5.2k stars), Dstack Prototyping (dstackai/dstack, 2.3k stars), LLaVA Vision-Language Model (Orchestra-Research/AI-Research-SKILLs, 13k stars) and ComfyUI Frontend Extensions (jtydhr88/comfyui-custom-node-skills, 295 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gradio App Builder?

huggingface (a GitHub organization, an official publisher) maintains it in huggingface/skills, which has 11,151 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 8, 2026.

Source: huggingface/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.