This project integrates a Supabase-authenticated API with a Next.js typescript frontend and a Python backend for Hugging Face model inference.

• Next Typescript Python Supabase
  • Table of Contents
  • Overview
  • Features
  • Setup
    • Prerequisites
    • Environment Variables
  • Deployment

The project consists of two main parts:

1. Backend: A Python API using serverless functions, which handles user authentication with Supabase and communicates with the Hugging Face API for model inference.
2. Frontend: A Next.js application for user authentication and sending inference requests to the backend.

• User authentication using Supabase
• Secure token verification
• Hugging Face model inference integration
• Simple frontend for user interaction

• Python 3.12
• Node.js and npm
• Supabase account
• Hugging Face account

Create a .env file in the root directory and add the following environment variables:

SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key
HUGGINGFACE_API_TOKEN=your_huggingface_api_token

NEXT_PUBLIC_SUPABASE_KEY=your_supabase_key
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url

Deploy app to vercel and add the environment variables in the settings.

SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key
NEXT_PUBLIC_SUPABASE_KEY=your_supabase_key
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
HUGGINGFACE_API_TOKEN=your_huggingface_api_token