huggingface-gradio 是做什么的？
使用Python构建Gradio网页界面和演示。在创建或编辑Gradio应用、组件、事件监听器、布局或聊天机器人时使用。
使用Python构建Gradio网页界面和演示。在创建或编辑Gradio应用、组件、事件监听器、布局或聊天机器人时使用。
此技能专注于使用Python构建Gradio网页界面和交互式演示。Gradio是一个流行的开源Python库，允许开发者快速创建机器学习模型、数据科学工具或任何Python函数的用户界面（UI）。该技能覆盖了Gradio的核心功能，包括创建各种UI组件（如文本框、滑块、图像、音频输入/输出）、设置事件监听器（如按钮点击或输入变化时触发函数）、设计响应式布局（如行、列、Tab），以及构建聊天机器人等高级交互模式。它在多种AI代理平台（如Claude Code、Cursor、Codex等）上运行，通过解析用户需求并生成完整的Gradio应用代码，帮助开发者专注于逻辑实现而无需手动编写UI代码。该技能基于Gradio的官方文档最佳实践工作，确保了与最新版本（如gradio>=4.0）的兼容性。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-gradioInstalled? Explore more 编程与开发 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
使用Python构建Gradio网页界面和演示。在创建或编辑Gradio应用、组件、事件监听器、布局或聊天机器人时使用。
This repo contains 5 individual skills — each has its own dedicated page.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.