Gradio

Gradio is an open source Python library for turning machine learning functions into interactive web apps. In the quickstart, Gradio describes building a demo or web application for an ML model, an API, or any arbitrary Python function and sharing it in seconds without needing JavaScript, CSS, or separate web hosting knowledge. The two primary construction styles are Interface and Blocks. Interface is a fast wrapper for common single function demos, while Blocks provides more flexibility and control for multi step layouts, dashboards, and custom interaction patterns. Apps are assembled from UI components and event listeners, then launched with a built in web server. Sharing and deployment are practical features rather than a separate product layer. The sharing guide explains that setting share=True in launch() creates a public link that anyone can open in a browser. It also covers hosting options such as Hugging Face Spaces, embedding hosted spaces, deep links, authentication, rate limits, analytics, and mounting a Gradio app within FastAPI. For programmatic use, the docs list client libraries for Python and JavaScript. The Python client is installed with pip install gradio-client and the JavaScript client with npm install @gradio/client, enabling scripts and front ends to call hosted Gradio apps as part of pipelines and automation. Because Gradio is open source, pricing for the library itself is free. Any runtime cost depends on where you host the app and what compute you attach, so teams should evaluate deployment targets, security requirements, and model resource needs separately.