Hugging Face Gradio V2

Gradio

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/hugging-face-gradio from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Gradio

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Guides, Core Patterns, Key Component Signatures, Custom HTML Components, Event Listeners, Prediction CLI.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Build or edit Gradio apps, layouts, components, and chat interfaces in Python.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Guides

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

• Quickstart
• The Interface Class
• Blocks and Event Listeners
• Controlling Layout
• More Blocks Features
• Custom CSS and JS
• Streaming Outputs
• Streaming Inputs
• Sharing Your App
• Custom HTML Components
• Getting Started with the Python Client
• Getting Started with the JS Client

Examples

Example 1: Ask for the upstream workflow directly

Use @hugging-face-gradio-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @hugging-face-gradio-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @hugging-face-gradio-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @hugging-face-gradio-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/hugging-face-gradio, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• examples.md

Imported Reference Notes

Imported: Additional Reference

• End-to-End Examples — complete working apps

Imported: Core Patterns

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

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.

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.

import gradio as gr

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

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

Imported: 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).

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.

Imported: 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:

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()

Imported: Event Listeners

All event listeners share the same signature:

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

Imported: 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

gradio info <space_id_or_url>

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

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

gradio predict <space_id_or_url> <endpoint> <json_payload>

Returns a JSON object with named output keys.

# 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.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.