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This project generates new designs of your room with AI

Room GPT

How it works

It uses an ML model called ControlNet to generate variations of rooms. This application gives you the ability to upload a photo of any room, which will send it through this ML Model using a Next.js API route, and return your generated room. The ML Model is hosted on Replicate and Upload is used for image storage. Loops is used for emails.

Running Locally

Cloning the repository the local machine.

git clone https://github.com/Nutlope/roomGPT

Creating a account on Replicate to get an API key.

  1. Go to Replicate to make an account.
  2. Click on your profile picture in the top right corner, and click on "Dashboard".
  3. Click on "Account" in the navbar. And, here you can find your API token, copy it.

Storing the API keys in .env

Create a file in root directory of project with env. And store your API key in it, as shown in the .example.env file.

If you'd also like to do rate limiting, create an account on UpStash, create a Redis database, and populate the two environment variables in .env as well. If you don't want to do rate limiting, you don't need to make any changes.

Installing the dependencies.

npm install

Running the application.

Then, run the application in the command line and it will be available at http://localhost:3000.

npm run dev

Auth setup

  1. Use openssl rand -base64 32 to generate NEXTAUTH_SECRET
  2. Add DB URL and SHADOW DB URL from Neon
  3. Create a new project in console.cloud.google.com
  4. Click configure consent screen in API credentials page and click external
  5. Add an app name, do not upload logo, add authorized domain
  6. Publish app
  7. Create credentials -> Oauth client ID
  8. Run npx prisma db push && prisma migrate dev && prisma generate

One-Click Deploy

Deploy the example using Vercel:

Deploy with Vercel

Note: You will need to configure auth by following the setup above by using Neon and next-auth and payments

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Upload a photo of your room to generate your dream room with AI.

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  • TypeScript 96.0%
  • CSS 2.4%
  • JavaScript 1.6%