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Gradio

Open source Python library for turning machine learning models into web apps and demos that you can share by link.

Open source

What is Gradio?

Gradio is an open source Python package that gives your model or function a web interface without any JavaScript, CSS or frontend work. After pip install gradio, a few lines of Python wrap a function in an Interface, or you can lay out a larger app with Blocks or a ChatInterface for chatbots. More than 40 components cover input and output for images, audio, video, 3D, dataframes, plots and more. Setting share=True prints a public link to the demo running on your computer, which expires after 72 hours, while permanent hosting is available for free on Hugging Face Spaces. Gradio can load models and Spaces from the Hugging Face Hub, offers a Python client, a JavaScript client and cURL access to query an app programmatically, and can be mounted inside a FastAPI app. ML engineers, researchers and developers use it to prototype, collect feedback and ship internal tools. The library is free to use.

Use cases

  • Build a demo for a machine learning model
  • Create a chatbot interface in Python
  • Share a prototype with a public link
  • Host a demo on Hugging Face Spaces
  • Build dashboards from live data
  • Query an ML app from Python or JavaScript

Who it's for

  • ML engineers
  • Researchers
  • Data scientists
  • Python developers

Why use Gradio

  • You stay in Python, so a data scientist can ship a working interface without learning web development.
  • One parameter produces a link you can send to a client or colleague, while the code keeps running on your own machine.
  • Free hosting on Hugging Face Spaces gives demos a permanent address.
  • Programmatic clients let other code call the same app, which is useful for pipelines and automation.
  • A large set of ready components covers common ML input and output types.

Gradio features

  • Interface, Blocks and ChatInterface building blocks
  • 40+ input and output components for text, images, audio, video and 3D
  • Public share links created with share=True
  • Free permanent hosting on Hugging Face Spaces
  • gr.load to wrap Hugging Face models and Spaces
  • Python client and JavaScript client for calling apps
  • Built-in API page and cURL access
  • Authentication and mounting inside FastAPI
  • Real-time dashboards with timers and plots
  • Custom components and Gradio Lite

Gradio integrations

  • Hugging Face
  • Hugging Face Spaces
  • Transformers
  • Supabase
  • BigQuery
  • Google Sheets
  • FastAPI
  • Comet
  • Discord
  • Docker

How to get started with Gradio

  1. Install the library with pip install gradio.
  2. Write a Python function and wrap it in gr.Interface, or compose a layout with gr.Blocks.
  3. Run the script to launch the app on your local machine.
  4. Set share=True for a temporary public link, or deploy the app to Hugging Face Spaces for permanent hosting.
  5. Call the app from Python, JavaScript or cURL using the generated API page.

Gradio limitations

  • Public share links expire after 72 hours unless you set up your own Share Server
  • A share link exposes your app publicly, so add authentication or avoid sensitive functions
  • The first call to a model through Inference Endpoints can be slower while it loads

Gradio pricing

Gradio is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Gradio alternatives

The main Gradio alternatives listed on Debuly are Streamlit, Chainlit, IBM watsonx.ai, BayesiaLab, Dify and Hugging Face. The table compares pricing and platforms; order is A–Z.

Gradio compared with its alternatives
ToolPricingStarting pricePlatforms
GradioOpen source—Web, API, Self-hosted, Python
BayesiaLabPaid—Desktop, Web, API
ChainlitOpen source—Python package, Self-hosted
DifyFreemium$59/moWeb, API, Self-hosted
Hugging FaceFreemium$20/moWeb, API
IBM watsonx.aiFreemium—Web, API, SDK
StreamlitOpen source—Web
  1. BayesiaLab

    Bayesian network software for machine learning, knowledge modeling, simulation and optimization, with an engine API

  2. Chainlit

    Open-source Python framework for building chat interfaces for LLM apps, now archived and no longer maintained.

  3. Dify

    Platform for building LLM apps, agents and RAG workflows on Dify Cloud or your own infrastructure, with a free Sandbox tier.

  4. Hugging Face

    Hub for hosting machine learning models, datasets and demo Spaces, with pay-as-you-go inference, GPU hardware and team plans.

  5. IBM watsonx.ai

    IBM's AI development studio for building generative AI apps and agents with foundation models, prompt tools, and machine learning, billed by usage.

  6. Streamlit

    Open-source Python framework that turns data scripts into shareable web apps without any front-end code.

Gradio FAQ

How much does Gradio cost?

The library is free to install and use. The site lists no paid plans. Hosting on Hugging Face Spaces is described as free, and any other hosting or model inference costs depend on the provider you choose.

Is there a free plan for Gradio?

Gradio itself is free, and you can deploy demos to Hugging Face Spaces at no charge. Hugging Face's Serverless Inference Endpoints are noted as having a generous free tier.

Is Gradio open source?

Yes. Gradio is an open source library installed with pip and developed in public, with community-built clients for languages such as Rust, Go and Ruby.

Does Gradio have an API or integrations?

Yes. Every app gets an API page, and there are a Python client (pip install gradio-client), a JavaScript client (npm install @gradio/client), and cURL access. It also integrates with Hugging Face, Supabase, BigQuery and other services.

How do I share a Gradio app?

Pass share=True to launch() to get a public gradio.live link that expires after 72 hours, or host the app on Hugging Face Spaces for a permanent URL.

What are alternatives to Gradio?

Common alternatives include Streamlit, Dash, Panel, NiceGUI and Chainlit.

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

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