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Gradio: Build Machine Learning Web Apps — in Python
Gradio (pronounced GRAY-dee-oh) is an open-source Python library that has been used to build hundreds of thousands of machine learning and data science demos.
With Gradio, you can quickly create a beautiful user interfaces around your machine learning models and let people "try out" what you've built by dragging-and-dropping in their own images, pasting text, recording their own voice, and interacting with your demo through the browser.
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
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Debugging your model interactively during development using built-in manipulation and interpretation tools
You can find an interactive version of the following Getting Started at https://gradio.app/getting_started.
{% with code=code, demos=demos %} {% include "guides/getting_started.md" %} {% endwith %}
System Requirements:
Gradio requires Python 3.7+ and has been tested on the latest versions of Windows, MacOS, and various common Linux distributions (e.g. Ubuntu). For Python package requirements, please see the setup.py file.
Contributing:
If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
License:
Gradio is licensed under the Apache License 2.0
See more:
You can find many more examples as well as more info on usage on our website: www.gradio.app
See, also, the accompanying paper: "Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild", ICML HILL 2019, and please use the citation below.
@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}
}