Introducing Gradio

Gradio is an open-source library for building AI interfaces that lets you quickly build your own applications and interact with AI models. In this blog, we will cover the basics of Gradio and the steps to get started.

Install Gradio

Installing Gradio is very easy, just use the following command:


pip install gradio

Build the Gradio application

To build a Gradio application, you need to define a handler function that contains input and output components. For example, here is a handler function that takes text input and outputs uppercase:


def capitalize_text(input_text):
    return input_text.upper()

Next, you can gradio.Interface()create a new Gradio interface with the class and specify the handler function as a parameter. For example, here is a simple Gradio application that takes text input and outputs uppercase:


import gradio as gr

def capitalize_text(input_text):
    return input_text.upper()

iface = gr.Interface(fn=capitalize_text, inputs="text", outputs="text")
iface.launch()

In the above code, we first imported Gradio and defined a capitalize_text()handler function called . We then gr.Interface()created a new Gradio interface using classes and capitalize_text()assigning functions as handlers. Finally, we use iface.launch()the method to start the Gradio application.

Gradio's input and output components

Gradio provides a variety of input and output components that allow you to interact with AI models in different ways. Here are some commonly used input and output components:

Input components: text, textbox, number, checkbox, dropdown, radio, image, audio, file
Output components: text, textbox, label, image, audio, file, keyvalues, json

For instance, in the example above, we used textan input component and textan output component. If you want to use another component, just pass it as an argument to inputsand outputsparameter.

Here is an example of a processing function that accepts an image as input and outputs a similar image:


import cv2
from skimage.measure import compare_ssim

def find_similar_image(input_image):
# Load reference image
    reference_image = cv2.imread("reference.jpg")

# Compute structural similarity index
    similarity_index = compare_ssim(reference_image, input_image, multichannel=True)

# Return similarity index as text outputreturn "Similarity index: {:.2f}".format(similarity_index)

In the above code, we use OpenCV and scikit-image library to calculate the similarity index between the input image and the reference image. Finally, we return the similarity index as text output.

Gradio's Advanced Features

Gradio also provides many advanced features such as setting default values, customizing components, adding descriptions, etc. Here are some commonly used advanced features:

Set default value: Use defaultparameters to set the default value of the input component.
Custom Components: Use gradio.custom()functions to define custom components.
Add Description: Use descriptionthe parameter to add a description to the component.

For example, here's a handler function that takes a number as input and outputs its square, and customizes a slider component:


import gradio as gr

def square_number(input_number):
    return input_number ** 2

custom_slider = gr.inputs.Slider(minimum=0, maximum=10, step=0.1, default=5, label="Select a number:")
iface = gr.Interface(fn=square_number, inputs=custom_slider, outputs="text", description="Enter a number and get its square.")
iface.launch()

In the above code, we first define a square_number()processing function named , which takes a number input and outputs its square. Then, we gr.inputs.Slider()customized a slider component using functions and specified its min, max, step and label. Finally, we descriptionadded a description to the interface using parameters.

Summarize

This blog introduces Gradio's basic knowledge and getting started steps, including installing Gradio, building Gradio applications, Gradio's input and output components, and Gradio's advanced features. I believe that through the study of this blog, readers can quickly master Gradio and use it to build their own AI applications.

For the advanced version, please refer to: (206 messages) Gradio Advanced: Using Gradio to Realize the Front End

Step-by-step Gradio tutorial, super complete! ! ! (with actual combat code)