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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.

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.

Gradio is useful for:

• Demoing your machine learning models for clients/collaborators/users/students.

• 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.

Quickstart

Prerequisite: Gradio requires Python 3.7 or higher, that's all!

What Does Gradio Do?

One of the best ways to share your machine learning model, API, or data science workflow with others is to create an interactive app that allows your users or colleagues to try out the demo in their browsers.

Gradio allows you to build demos and share them, all in Python. And usually in just a few lines of code! So let's get started.

Hello, World

To get Gradio running with a simple "Hello, World" example, follow these three steps:

1. Install Gradio using pip:

pip install gradio

2. Run the code below as a Python script or in a Jupyter Notebook (or Google Colab):

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()

3. The demo below will appear automatically within the Jupyter Notebook, or pop in a browser on http://localhost:7860 if running from a script:

The Interface Class

You'll notice that in order to make the demo, we created a gradio.Interface. This Interface class can wrap any Python function with a user interface. In the example above, we saw a simple text-based function, but the function could be anything from music generator to a tax calculator to the prediction function of a pretrained machine learning model.

The core Interface class is initialized with three required parameters:

• fn: the function to wrap a UI around
• inputs: which component(s) to use for the input (e.g. "text", "image" or "audio")
• outputs: which component(s) to use for the output (e.g. "text", "image" or "label")

Let's take a closer look at these components used to provide input and output.

Components Attributes

We saw some simple Textbox components in the previous examples, but what if you want to change how the UI components look or behave?

Let's say you want to customize the input text field — for example, you wanted it to be larger and have a text placeholder. If we use the actual class for Textbox instead of using the string shortcut, you have access to much more customizability through component attributes.

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(
    fn=greet,
    inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
    outputs="text",
)
demo.launch()

Multiple Input and Output Components

Suppose you had a more complex function, with multiple inputs and outputs. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. Take a look how you pass a list of input and output components.

import gradio as gr

def greet(name, is_morning, temperature):
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return greeting, round(celsius, 2)

demo = gr.Interface(
    fn=greet,
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    outputs=["text", "number"],
)
demo.launch()

You simply wrap the components in a list. Each component in the inputs list corresponds to one of the parameters of the function, in order. Each component in the outputs list corresponds to one of the values returned by the function, again in order.

An Image Example

Gradio supports many types of components, such as Image, DataFrame, Video, or Label. Let's try an image-to-image function to get a feel for these!

import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189], 
        [0.349, 0.686, 0.168], 
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
demo.launch()

When using the Image component as input, your function will receive a NumPy array with the shape (width, height, 3), where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array.

You can also set the datatype used by the component with the type= keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input Image component could be written as:

gr.Image(type="filepath", shape=...)

Also note that our input Image component comes with an edit button