Kiln Conventions
Kiln-AI/Kiln
Kiln's code conventions and per-area gotchas, plus a diff gate that enforces the mechanical ones.
Guides building Gradio web UIs and ML demos in Python, covering Interface, Blocks, ChatInterface, event listeners, layouts and chatbots.
$ npx skills add huggingface/skills --skill huggingface-gradio -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install huggingface/skills huggingface-gradio --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .claude/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/huggingface/skills/tree/main/skills/huggingface-gradioType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add huggingface/skills --skill huggingface-gradio -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install huggingface/skills huggingface-gradio --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/huggingface-gradio .agents/skills/huggingface-gradio && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .agents/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add huggingface/skills --skill huggingface-gradio -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install huggingface/skills huggingface-gradio --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/huggingface-gradio .cursor/skills/huggingface-gradio && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .cursor/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/huggingface/skills.git --path skills/huggingface-gradio--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add huggingface/skills --skill huggingface-gradio -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install huggingface/skills huggingface-gradio --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/huggingface-gradio .gemini/skills/huggingface-gradio && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .gemini/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install huggingface/skills huggingface-gradioInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add huggingface/skills --skill huggingface-gradio -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/huggingface-gradio .github/skills/huggingface-gradio && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .github/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add huggingface/skills --skill huggingface-gradio -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install huggingface/skills huggingface-gradio --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huggingface/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/huggingface-gradio .opencode/skills/huggingface-gradio && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "huggingface-gradio" agent skill from https://github.com/huggingface/skills/tree/main/skills/huggingface-gradio into .opencode/skills/huggingface-gradio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "huggingface-gradio", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
huggingface-gradioGuides 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. 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.
Read from SKILL.md and the folder at commit c3ff942. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
upload.wikimedia.orgAlso links to:
gradio.appFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from huggingface/skills at commit c3ff942, republished under its Apache-2.0 licence (© huggingface). 2,265 words, ~6,183 tokens.
.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.Gradio is a Python library for building interactive web UIs and ML demos. This skill covers the core API, patterns, and examples.
Detailed guides on specific topics (read these when relevant):
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()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.
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()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,
) -> DependencySupported events per component:
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 parametersgradio 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 predictionsgradio 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.
© 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
SKILL.md and 1 other file in skills/huggingface-gradio of huggingface/skills.
Open the folder on GitHubat commit c3ff942
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gradio App Builder this skillhuggingface/skills | 11k | 4 repos | ~6.2k | Automated safety check: Pass | Apache-2.0 | |
| Kiln ConventionsKiln-AI/Kiln | 5.2k | — | ~1.7k | Automated safety check: Notes | Custom licence | |
| Dstack Prototypingdstackai/dstack | 2.3k | — | ~1.6k | Automated safety check: Pass | MPL-2.0 | |
| LLaVA Vision-Language ModelOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2k | Automated safety check: Pass | MIT | |
| ComfyUI Frontend Extensionsjtydhr88/comfyui-custom-node-skills | 295 | — | ~3.7k | Automated safety check: Pass | MIT | |
| Fast Agent Designevalstate/fast-agent | 3.9k | — | ~220 | Automated safety check: Pass | Apache-2.0 |
Kiln-AI/Kiln
Kiln's code conventions and per-area gotchas, plus a diff gate that enforces the mechanical ones.
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.
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.
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.
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.
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…
huggingface/skills
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.
huggingface/skills
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.
huggingface/skills
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.
huggingface/skills
Sets up an isolated Python environment with a supported interpreter and current boto3 before any SageMaker deployment, training or AWS automation code runs.
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
huggingface/skills
Deploys SageMaker endpoints with autoscaling, CloudWatch alarms and tags on by default, using scripts for real-time, scale-to-zero and async setups.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.