API reference documentation for the Gradio ecosystem. Explore the Gradio ecosystem — from building interactive demos with the core library, to integrating with client SDKs, to creating your own custom components.
Gradio docs for using Interface Description Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo around a machine learning model (or any Python function) in a few lines of code. You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and (3) the desired output components. Additional parameters can be used ...
Gradio docs for using Components Components Introduction Gradio includes pre-built components that can be used as inputs or outputs in your Interface or Blocks with a single line of code. Components include preprocessing steps that convert user data submitted through browser to something that be can used by a Python function, and postprocessing steps to convert values returned by a Python ...
Gradio docs for using Radio the label for this component, displayed above the component if show_label is True and is also used as the header if there are a table of examples for this component. If None and used in a gr.Interface, the label will be the name of the parameter this component corresponds to.
In addition to tools (which execute functions generally and are the default for any function exposed through the Gradio MCP integration), MCP supports two other important primitives: resources (for exposing data) and prompts (for defining reusable templates). Gradio provides decorators to easily create MCP servers with all three capabilities.
Honestly, without @Gradio, we would not be doing a real time AI trial. We have many other ideas for algorithms we want to test through clinical trials, and we know it's possible thanks to @Gradio.