Building a custom GPT for websites is a breeze, and you can do it for free or on the cheap. Just 60 lines of code and you’re off to the races. Simply deploy on render, and you’re good to go. You can even use natural language to specify what you want. It’s like magic β¨. #WebsiteGPT π
Overview π
In this video, I’m going to show you how to build a custom GPT and deploy it to the GPT store. Do this for free or at a very low cost. You’ll see a demonstration of how this works and then I’ll guide you through the steps to deploy this service, the back-end setup, and the GPT action interaction. Let’s get started!
Creating a New Project
We’ll use npm to create a new project. Then, we’ll set up the index.js file, import the required dependencies, and define routes for our application.
Setting up Middleware
Middleware allows interaction with the GPT from the front end to the back end of the server. We’ll also define our route, generate a unique file name, and create an instance of Express for our application.
Dependencies:
- crypto
- express
Deploying the Service
We’ll create a unique file name, specify the path for our new file, and set up the HTML structure. Also, a quick check for directory existence, making a directory if it doesn’t exist, and using a basic HTML structure will all be a part of this step.
Setting Up on GitHub
After setting up a new repository on GitHub, we’ll make our first commit and push it. This will ensure deploying the repository on the render service, making our server live.
Tip: Incorporating logic to use an AWS S3 bucket could help in preserving these HTML files over time.
Building & Deployment from Git Repository
We’ll access the render dashboard, create a new web service, and deploy our project from the GitHub repository. This involves setting up the service, pushing to the specific branch, and seeing our successful build.
Action Setup & Integration
In this step, we will learn about setting up a unique name for the web service, recognizing and starting it, and setting up the URL for the render service. Once done, we’ll serve the directory of everything within our public folder.
Conclusion
That’s it for this guide to build and deploy a custom GPT to the GPT store. I hope you found this video helpful. Don’t forget to like, comment, share, and subscribe for more content.
Key Takeaways:
- Understanding dependencies for setting up a custom GPT
- Step-by-step deployment process
- Pointers for preserving HTML files
- Building and deploying from repositories
FAQ:
- Can we create multiple unique file names for deployment?
- What other options are available for preserving HTML files?
- Are there permissions required for accessing the render service?
Remember, the more we engage with GPT and customize, the more it evolves to fulfill our needs. It’s all about building and integrating the future of AI interactions! π
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