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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...

In the world of serverless computing, AWS Lambda stands out as a powerful platform for deploying applications quickly and efficiently. FastAPI, with its high-performance capabilities and easy-to-use framework, is a perfect match for building robust APIs. In this article, we'll walk through the process of deploying a FastAPI application on AWS Lambda, step by step. Setting Up the Environment First, ensure you have Visual Studio Code open. Begin by installing the necessary dependencies: FastAPI, uvicorn, and Mangum, which serves as the handler for AWS Lambda. In your terminal, execute: pip install fastapi uvicorn mangum Next, generate a requirements.txt file to manage dependencies: pip freeze > requirements.txt Now, create the main application file, main.py , where we'll define our FastAPI application along with the necessary handlers: from fastapi import FastAPI from mangum import Mangum app = FastAPI() handler = Mangum(app) @app.get("/") async def hello...

In today's fast-paced world of machine learning and artificial intelligence, it's essential to be able to showcase your models and applications effectively. Gradio is an open-source Python package that simplifies this process, allowing you to create stunning demos and web applications for your machine learning models or Python functions effortlessly. In this post, we'll explore the basics of Gradio and how you can use it to build interactive demos with just a few lines of code. What is Gradio? Gradio is a Python package designed to streamline the creation of demos and web applications for machine learning models, APIs, or any arbitrary Python function. It eliminates the need for extensive knowledge of JavaScript, CSS, or web hosting, making it accessible to a wide range of users. Getting Started with Gradio To begin using Gradio, ensure you have Python 3.8 or higher installed on your system. You can then install Gradio using pip. pip install gradio Once installed, you can s...