| <div align="center"> |
|
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| [<img src="readme_files/gradio.svg" alt="gradio" width=300>](https://gradio.app)<br> |
| <em>Build & share delightful machine learning apps easily</em> |
|
|
| [](https://github.com/gradio-app/gradio/actions/workflows/backend.yml) |
| [](https://github.com/gradio-app/gradio/actions/workflows/ui.yml) |
| [](https://pypi.org/project/gradio/) |
| [](https://pypi.org/project/gradio/) |
|  |
| [](https://twitter.com/gradio) |
|
|
| [Website](https://gradio.app) |
| | [Documentation](https://gradio.app/docs/) |
| | [Guides](https://gradio.app/guides/) |
| | [Getting Started](https://gradio.app/getting_started/) |
| | [Examples](demo/) |
| | [中文](readme_files/zh-cn#readme) |
|
|
| </div> |
|
|
| # Gradio: Build Machine Learning Web Apps — in Python |
|
|
| Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications. |
|
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| With Gradio, you can quickly create a beautiful user interface around your machine learning models or data science workflow and let people "try it out" by dragging-and-dropping in their own images, |
| pasting text, recording their own voice, and interacting with your demo, all through the browser. |
|
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|  |
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| Gradio is useful for: |
|
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| - **Demoing** your machine learning models for clients/collaborators/users/students. |
|
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| - **Deploying** your models quickly with automatic shareable links and getting feedback on model performance. |
|
|
| - **Debugging** your model interactively during development using built-in manipulation and interpretation tools. |
|
|
| $getting_started |
| |
| ## Open Source Stack |
| |
| Gradio is built with many wonderful open-source libraries, please support them as well! |
| |
| [<img src="readme_files/huggingface_mini.svg" alt="huggingface" height=40>](https://huggingface.co) |
| [<img src="readme_files/python.svg" alt="python" height=40>](https://www.python.org) |
| [<img src="readme_files/fastapi.svg" alt="fastapi" height=40>](https://fastapi.tiangolo.com) |
| [<img src="readme_files/encode.svg" alt="encode" height=40>](https://www.encode.io) |
| [<img src="readme_files/svelte.svg" alt="svelte" height=40>](https://svelte.dev) |
| [<img src="readme_files/vite.svg" alt="vite" height=40>](https://vitejs.dev) |
| [<img src="readme_files/pnpm.svg" alt="pnpm" height=40>](https://pnpm.io) |
| [<img src="readme_files/tailwind.svg" alt="tailwind" height=40>](https://tailwindcss.com) |
| [<img src="readme_files/storybook.svg" alt="storybook" height=40>](https://storybook.js.org/) |
| [<img src="readme_files/chromatic.svg" alt="chromatic" height=40>](https://www.chromatic.com/) |
| |
| ## License |
| |
| Gradio is licensed under the Apache License 2.0 found in the [LICENSE](LICENSE) file in the root directory of this repository. |
| |
| ## Citation |
| |
| Also check out the paper _[Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://arxiv.org/abs/1906.02569), ICML HILL 2019_, and please cite it if you use Gradio in your work. |
| |
| ``` |
| @article{abid2019gradio, |
| title = {Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild}, |
| author = {Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James}, |
| journal = {arXiv preprint arXiv:1906.02569}, |
| year = {2019}, |
| } |
| ``` |
| |