How to Deploy a Gradio Application on Render: A Step-by-Step Guide

Introduction

Deploying a Gradio application on a server allows you to share your web-based machine learning demos with a wider audience. In this guide, we'll walk you through the process of deploying a Gradio app using Render, a popular platform for hosting web applications.

Prerequisites

Before we begin, make sure you have the following:

  • A Gradio application developed and tested locally.
  • A GitHub account with your Gradio app code pushed to a repository.
  • A Render account.

Steps to Deploy

1. Prepare Your Gradio App

First, ensure your Gradio app runs correctly on your local machine. Use the requirements.txt file to manage your dependencies and a virtual environment to isolate your app.

# Activate virtual environment and install dependencies
source venv/bin/activate
pip install -r requirements.txt

# Run your Gradio app locally
uvicorn run:app --reload

Check the local deployment to confirm everything works as expected.

2. Push Your Code to GitHub

Commit your code changes and push them to your GitHub repository.

git add .
git commit -m "Ready for deployment"
git push origin main

You can find the repository here.

3. Set Up Render

Log in to your Render account and connect it to your GitHub repository.

  • Click on "New" and select "Web Service".
  • Choose the repository containing your Gradio app.

4. Configure Render Settings

Fill in the necessary details:

  • Name: A descriptive name for your app, e.g., gradio-demo-app.

  • Region: Select the region closest to your target users.

  • Branch: Ensure it's set to main.

  • Build Command: Leave it as default.

  • Start Command:

    uvicorn main:app --host 0.0.0.0 --port 5000

  • Python Version: Add an environment variable PYTHON_VERSION and set it to your local Python version, e.g., 3.9.17.

5. Deploy the Application

Select the free tier for hosting, and then click "Create Web Service". Render will start the deployment process by creating a virtual environment, installing dependencies, and running your app.

6. Test the Deployed App

Once the deployment is complete, you will see a "Live" status. Click on the provided URL to access your deployed Gradio app. You can find the deployed app here.

Conclusion

Deploying a Gradio application on Render is straightforward and efficient. By following these steps, you can share your machine learning applications with others effortlessly.

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