Learn Gradio with an in-depth Gradio course to build and deploy machine learning demos, use pretrained models, and create interactive user interfaces and dashboards.

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Gradio is an open-source Python library that simplifies the process of creating user interfaces and applications for your machine learning models. With its intuitive interface, you can easily showcase your work and deploy applications. 

In this course, you will be introduced to all the key features of Gradio. You will learn the tools to create a user interface dashboard and deploy your application. Next, we will look at connecting to your machine learning models or using pretrained models like OpenAI and Hugging Face. Finally, you will have the opportunity to get hands-on experience by building your application.

After completing this course, you can rapidly build demos of machine learning models and leverage powerful existing models. As AI and ML applications continue to gain traction, mastering Gradio is an invaluable skill that can set you apart in the field.

Machine Learning Applications in Gradio

import gradio as gr

def greet(name):
    return "Welcome " + name + " to the Educative course ‘Introduction to Gradio’!"

demo = gr.Interface(
    fn=greet,
    inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
    outputs=gr.Textbox(),
)
demo.launch(server_name="0.0.0.0", server_port=3000)

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


React

Dart

GoJS React

Typescript React

Vue.js

Get introduced to Gradio and the application we are going to build using Gradio!

Getting Started with Gradio

Displaying Components in Gradio

Block Layouts in Gradio

Interactive Components in Gradio

Dashboarding with Gradio

Deploying Machine Learning Model in Gradio

Chatbots with Gradio

Integration with Hugging Face

Miscellaneous

Building a Prediction Model in Gradio

Final Assessment

Conclusion

Introduction to Gradio