How to build your first Web Application with Gradio in Python: A Step-by-Step Guide

Introduction

Gradio is an open-source Python library that allows you to create user-friendly web interfaces for machine learning models with minimal effort. It’s widely used for building demos, sharing models, and prototyping machine learning applications. In this tutorial, we'll walk you through how to set up a basic Gradio project from scratch.

What is Gradio?

Gradio is a simple yet powerful tool for creating interfaces that interact with machine learning models. Whether you’re dealing with images, text, or audio, Gradio allows you to build demos with a few lines of Python code. This is particularly useful when you want to share your model with others or test it in real-time.

Why Use Gradio?

Gradio offers several advantages for machine learning developers:

• Ease of use: You can create a full interactive interface with just a few lines of code.
• Compatibility: It works with a wide range of Python libraries like TensorFlow, PyTorch, and Hugging Face.
• Instant sharing: Gradio automatically generates a shareable link for your app, making it accessible to others.

Prerequisites

Before we dive in, make sure you have the following:

• Python installed (version 3.8 or higher) ☑️
• Python virtual environment created ☑️

Step 1: Install Gradio

To begin, you’ll need to install the Gradio library on the virtual environment. You can do this by running the following command in your terminal or command prompt:

pip install gradio

This will install the latest version of Gradio and its dependencies.

Step 2: Create Python File

Create a new Python file in your preferred IDE or text editor. For this example, we’ll create a simple demo where a user can input text, and our application will return it in uppercase.

Let’s name the file app.py.

Step 2: Import Packages

Import the following Python packages:

import gradio as gr

Step 3: Define Function

Now that we have our script, the next step is to define the function that Gradio will use. This function will perform the task of converting text to uppercase.

def to_uppercase(text):
    return text.upper()

You can replace this function with something more complex if you’re working with an actual machine learning model, but we’re keeping it simple for demonstration purposes.

Step 4: Create Gradio Interface

Gradio makes it easy to create interfaces with different types of inputs and outputs. In our case, we’ll be working with text input and output.