Gradio

Gradio is an AI project builder tool that builds and shares machine learning web apps in Python. A Python library that turns machine learning functions into shareable web apps. It ships more than 40 input and output components, supports instant public links, and offers free permanent hosting through Hugging Face Spaces.

Gradio is an open source Python library for turning machine learning models into working web interfaces without writing frontend code. A developer wraps a function in an interface, picks input and output types, and launches a local server. The library renders the browser UI, handles the request cycle, and exposes a public share link when needed. Installation is a single pip command and a first app can run in a few lines of Python, which is why it is often the first stop for people who want to demo a model rather than design a page. The component set covers most data types a model will touch: images, audio, video, 3D models, dataframes, plots, JSON, chat transcripts, file explorers, sliders, checkboxes, dropdowns and annotated images, among more than 40 options. Inputs and outputs can be chained into multi step workflows, and the chatbot component supports streaming responses, which makes it a common choice for conversational LLM demos. Because the library handles layout and event wiring, the same script that runs on a laptop can be pushed to a host unchanged. Gradio suits data scientists, ML engineers and researchers who want a shareable prototype fast, plus teams that need an internal review tool for a model before committing to a full product build. Sharing works two ways: a temporary public URL for a locally running app, or permanent hosting on Hugging Face Spaces, which is free and auto scales. That path from local script to public demo is the main reason the library is popular. Among alternatives, Gradio sits closer to Streamlit and Dash than to a general web framework. It is narrower by design: the abstraction is built around model inputs and outputs, not around arbitrary application state or custom page design. Projects that outgrow that abstraction typically keep Gradio for the demo layer and move the product itself to a conventional stack. The library remains a prototype and demo tool first, and it is strongest when speed of sharing matters more than pixel level control.