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Skillv1.0.0

gradio

Creates ML demo interfaces with Gradio, supporting images, text, audio, and video inputs/outputs.

by ssrjkk(0) 0 installs
Free
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Free account. Installing gives you the manifest plus copy-paste snippets.

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About

Imported from ssrjkk/claude-skills (.claude/skills/ai/gradio/SKILL.md). Install upstream with npx skills add ssrjkk/claude-skills --skill gradio. Copyright stays with the author.

Gradio

Build demo web apps for machine learning models.

Quick Start

import gradio as gr
def greet(name, intensity): return "Hello " + name + "!" * intensity
demo = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Name"), gr.Slider(1, 5, value=2)], outputs=gr.Textbox())
demo.launch()

Blocks Layout

with gr.Blocks() as demo:
    gr.Markdown("# My App")
    with gr.Row():
        with gr.Column(): input_img = gr.Image(); btn = gr.Button("Process")
        with gr.Column(): output_img = gr.Image()
    btn.click(fn=process, inputs=input_img, outputs=output_img)

When to Use

  • ML model demos; Team sharing; User testing; API prototyping

Validation

  1. Interface launches on shareable URL; 2. Input types work; 3. Function returns expected output

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/ssrjkk-claude-skills-gradio/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

ssrjkk-claude-skills-gradio.ocm.jsonjson
{
  "ocm": "1",
  "id": "ssrjkk-claude-skills-gradio",
  "kind": "skill",
  "name": "gradio",
  "description": "Creates ML demo interfaces with Gradio, supporting images, text, audio, and video inputs/outputs.",
  "publisher": "ssrjkk",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "gradio",
      "python",
      "ml",
      "demo",
      "interactive",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Creates ML demo interfaces with Gradio, supporting images, text, audio, and video inputs/outputs."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/ssrjkk/claude-skills",
      "path": ".claude/skills/ai/gradio/SKILL.md",
      "ref": "e1fe9f4b8b62cbb6be06d22940974a7ca87a70b9",
      "url": "https://github.com/ssrjkk/claude-skills/blob/e1fe9f4b8b62cbb6be06d22940974a7ca87a70b9/.claude/skills/ai/gradio/SKILL.md",
      "key": "ssrjkk/claude-skills/.claude/skills/ai/gradio/SKILL.md"
    }
  },
  "instructions": "# Gradio\n> Build demo web apps for machine learning models.\n## Quick Start\n```python\nimport gradio as gr\ndef greet(name, intensity): return \"Hello \" + name + \"!\" * intensity\ndemo = gr.Interface(fn=greet, inputs=[gr.Textbox(label=\"Name\"), gr.Slider(1, 5, value=2)], outputs=gr.Textbox())\ndemo.launch()\n```\n## Blocks Layout\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"# My App\")\n    with gr.Row():\n        with gr.Column(): input_img = gr.Image(); btn = gr.Button(\"Process\")\n        with gr.Column(): output_img = gr.Image()\n    btn.click(fn=process, inputs=input_img, outputs=output_img)\n```\n",
  "cost": {
    "context_tokens": 196
  }
}

Fetch it by URL: GET /api/v1/registry/ssrjkk-claude-skills-gradio/manifest?version=1.0.0

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