Gradio Interactive App Builder
Build Gradio web interfaces, machine-learning demos, and chat apps in Python.
The material guides Gradio UI construction and shows no malware, credential theft, or destructive default. However, it includes file, camera, and microphone uploads, public launch, remote prediction uploads, and arbitrary HTML/JS without documenting data flows, sensitive-data handling, user confirmation, access control, isolation, or rollback, so points are deducted.
It provides Interface, Blocks, ChatInterface, component signatures, event signatures, and many examples, making the happy path plausible. The SKILL.md is truncated at the gradio predict example and lacks version constraints, a targeted test suite, abnormal-input handling, and diagnosable failure feedback; static calibration therefore requires a conservative deduction.
The scope clearly covers Python Gradio UIs, components, events, layouts, and chatbots, with reasonably clear invocation scenarios. It does not define non-fit boundaries, input/output contracts, or environment and version requirements, and gives no Chinese-language guidance. Local Gradio use need not depend on overseas services, but reachability and limitations for Spaces, remote documentation, and remote APIs are not addressed.
The material is layered into guides, core patterns, component signatures, HTML, event listeners, and examples.md. The repository supplies an Apache-2.0 license, generated-artifact validation, and a security reporting path. The skill itself lacks dependency installation notes, version policy, changelog, explicit maintenance ownership, update path, and troubleshooting, and the main file is incomplete.
Examples cover basic UIs, layouts, forms, chat, streaming, media, dataframes, and CLI interaction, so they can assist with common Gradio prototypes. There is no execution evidence, dependencies are not declared, production and security concerns are largely absent, and the main document is truncated; direct usability and completeness are therefore limited.
Evidence consists primarily of documented API signatures, code snippets, and examples, with only general repository CI evidence for generated artifacts. There is no skill-specific test suite, successful execution record, or independent corroboration, so conclusions are only partially auditable and remain below the static-review ceiling.
- The main SKILL.md is truncated at the gradio predict example, so completeness cannot be confirmed.
- Before using uploads, camera, microphone, remote Spaces, or custom HTML/JS, add data-flow, privacy, access-control, and user-confirmation guidance.
- No Gradio version is pinned and API compatibility is not demonstrated; reproduce examples against the target version before production use.
- The publisher is not verified by the FollowSkills enterprise registry and should be treated as unknown.
What does this skill do, and when should you use it?
This skill helps agents build Python web UIs and machine-learning demos with Gradio. It covers Interface, Blocks, ChatInterface, core components, custom HTML components, and event listeners. It also documents the Gradio CLI commands `info` and `predict` for discovering and calling Gradio app endpoints. It fits developers creating or editing Gradio applications, although the source does not specify Python or Gradio versions.
Provides Python examples for Gradio Interface, Blocks, and ChatInterface; documents parameters for components including Textbox, Number, Slider, Checkbox, Dropdown, Radio, Image, Audio, Video, File, Chatbot, Button, Markdown, and HTML; explains custom HTML with CSS and JavaScript and component event listeners; uses gradio info to inspect endpoints, parameters, and return values; uses gradio predict to send JSON requests; supports --token for private Spaces and documents filepath parameters for file uploads.
- A Python developer needs a quick input/output web interface around a function.
- An ML developer needs a demo accepting image, audio, video, or file inputs.
- An application developer needs custom Blocks layouts with explicit button and component event wiring.
- A team needs a chatbot UI with message history and response handling.
- A coding agent needs to inspect a Gradio Space API and call its prediction endpoint with JSON.
What are this skill's strengths and limitations?
- Covers both high-level Interface and flexible low-level Blocks patterns.
- Includes component signatures, event lists, and working code examples.
- Documents custom HTML, CSS, JavaScript, and chatbot UI patterns.
- Includes CLI workflows for endpoint discovery and programmatic predictions.
- The supported Python and Gradio versions are not specified.
- No standalone installation or environment setup command is provided.
- The source provides no test suite, compatibility matrix, or troubleshooting workflow.
- Remote predictions require network access, and private Spaces additionally require a token.
How do you install this skill?
Copy or symlink the skills/huggingface-gradio folder from the huggingface/skills repository into a standard Codex .agents/skills location, such as $REPO_ROOT/.agents/skills or $HOME/.agents/skills. The README does not provide a standalone Python or Gradio installation command for this skill.
How do you use this skill?
Mention it explicitly in a coding request, for example: "Use the huggingface-gradio skill to build a Gradio chatbot with Blocks." When creating or editing a Gradio app, follow the relevant guides and patterns in SKILL.md. For a remote Space, run gradio info <space_id_or_url> to discover endpoints, then gradio predict <space_id_or_url> <endpoint> <json_payload> to send a prediction request; add --token for a private Space.