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gradiogradio 演示文稿

Agent Skill

gradio 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

29,988

周安装

1,225

GitHub Stars

2

下载量

9,702
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:gradio(gradio 演示文稿)
来源仓库:https://github.com/ivangdavila/gradio
安装命令:
openclaw skills install gradio
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install gradio

简介

gradio 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中维护前端项目或生成界面组件时使用。

  • 它支持构建 ML 演示界面,结合状态管理和生产模式提升开发效率。
  • 通过 clawhub 安装,命令为 openclaw skills install gradio。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • gradio 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
Gradio
description
Build and deploy ML demo interfaces with proper state management, queuing, and production patterns.
metadata
{"clawdbot":{"emoji":"🎨","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}}

Gradio Patterns

Interface vs Blocks

  • gr.Interface is for single-function demos — use gr.Blocks for anything with multiple steps, conditional UI, or custom layout
  • Blocks gives you .click(), .change(), .submit() event handlers — Interface only has one function
  • Mixing Interface inside Blocks works but creates confusing state — pick one pattern per app

State Management

  • gr.State() creates per-session state — it resets when the user refreshes the page
  • State values must be JSON-serializable or Gradio silently drops them — no custom classes without serialization
  • Pass State as both input AND output to persist changes: fn(state) -> state — forgetting the output loses updates
  • Global variables shared across users cause race conditions — always use gr.State() for user-specific data

Queuing and Concurrency

  • Without .queue(), long-running functions block all other users — always call demo.queue() before .launch()
  • concurrency_limit=1 on a function serializes calls — use for GPU-bound inference that can't parallelize
  • max_size in queue limits waiting users — without it, memory grows unbounded under load
  • Generator functions with yield enable streaming — but they hold a queue slot until complete

File Handling

  • Uploaded files are temp paths that get deleted after the request — copy them if you need persistence
  • gr.File(type="binary") returns bytes, type="filepath" returns a string path — mismatching causes silent failures
  • Return gr.File(value="path/to/file") for downloads, not raw bytes — the component handles content-disposition headers
  • File uploads have a default 200MB limit — set max_file_size in launch() to change it

Component Traps

  • gr.Dropdown(value=None) with allow_custom_value=False crashes if the user submits nothing — set a default or make it optional
  • gr.Image(type="pil") returns a PIL Image, type="numpy" returns an array, type="filepath" returns a path — inconsistent inputs break functions
  • gr.Chatbot expects list of tuples [(user, bot), ...] — returning just strings doesn't render
  • visible=False components still run their functions — use gr.update(interactive=False) to disable without hiding

Authentication

  • auth=("user", "pass") is plaintext in code — use auth=auth_function for production with proper credential checking
  • Auth applies to the whole app — there's no per-route or per-component auth without custom middleware
  • share=True with auth still exposes auth to Gradio's servers — use your own tunnel for sensitive apps

Deployment

  • share=True creates a 72-hour public URL through Gradio's servers — not for production, just demos
  • Environment variables in local dev don't exist in Hugging Face Spaces — use Spaces secrets or the Settings UI
  • server_name="0.0.0.0" to accept external connections — default 127.0.0.1 only allows localhost
  • Behind a reverse proxy, set root_path="/subpath" or assets and API routes break

Events and Updates

  • Return gr.update(value=x, visible=True) to modify component properties — returning just the value only changes value
  • Chain events with .then() for sequential operations — parallel .click() handlers race
  • every=5 on a function polls every 5 seconds — but it holds connections open, scale carefully
  • trigger_mode="once" prevents double-clicks from firing twice — default allows rapid duplicate submissions

Performance

  • cache_examples=True pre-computes example outputs at startup — speeds up demos but increases load time
  • Large model loading in the function runs per-request — load in global scope or use gr.State with initialization
  • batch=True with max_batch_size=N groups concurrent requests — essential for GPU throughput

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.54%
按下载量换算9,172

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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