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Gradio: Build Machine Learning Web Apps — in Python

Gradio is an open-source Python package that allows you to quickly build a demo or web application for your machine learning model, API, or any arbitrary Python function. You can then share a link to your demo or web application in just a few seconds using Gradio's built-in sharing features. No JavaScript, CSS, or web hosting experience needed!

It just takes a few lines of Python to create your own demo, so let's get started ?

Installation

Prerequisite: Gradio 5 requires Python 3.10 or higher

We recommend installing Gradio using pip, which is included by default in Python. Run this in your terminal or command prompt:

pip install --upgrade gradio

Tip

It is best to install Gradio in a virtual environment. Detailed installation instructions for all common operating systems are provided here.

Building Your First Demo

You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere else you write Python. Let's write your first Gradio app:

import gradio as gr

def greet(name, intensity):
    return "Hello, " + name + "!" * int(intensity)

demo = gr.Interface(
    fn=greet,
    inputs=["text", "slider"],
    outputs=["text"],
)

demo.launch()

Tip

We shorten the imported name from gradio to gr. This is a widely adopted convention for better readability of code.

Now, run your code. If you've written the Python code in a file named app.py, then you would run python app.py from the terminal.

The demo below will open in a browser on http://localhost:7860 if running from a file. If you are running within a notebook, the demo will appear embedded within the notebook.

License

Gradio is licensed under the Apache License 2.0 found in the 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, 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},
}