Movie List and AI Recommendation App

Description

This project allows users to create personalized movie lists and receive real-time movie recommendations powered by AI. The recommendations adapt based on the content of the movie list as users add films, making suggestions more accurate over time.

This app is a second-year Bachelor's project and is composed of two main services: a Next.js front-end and a Flask back-end, both containerized via Docker. The back-end handles data with MySQL and provides AI recommendations using ChromaDB.

Features

• Create Movie Lists: Users can create and manage movie lists.
• AI-Powered Recommendations: The app suggests movies based on the current list and refines recommendations dynamically.
• MySQL Database: All movie and user data are stored in a MySQL database.
• Dockerized: Both the front-end and back-end run in Docker containers for easy setup and deployment.

Project Structure

• Next.js: Front-end framework for rendering the UI.
• Flask: Back-end service providing API endpoints and handling business logic.
• ChromaDB: AI model for real-time, adaptive movie recommendations.
• MySQL: Database for storing movie and user information.

Docker Setup

This project is built with Docker, and the services can be started using Docker Compose. Here's a breakdown of the services:

• nextjs: Next.js front-end service (port 3000).
• flask: Flask back-end service (port 5001), interacts with MySQL and ChromaDB.
• db: MySQL database service (port 3306), initialized with a MovieDb.sql script.
• chromadb: AI service for recommendations (port 8000).

Docker Compose Configuration

To start the app, use the following commands:

docker-compose up

This will launch all services and make the application accessible at http://localhost:3000.

Environment Variables

• NEXT_PUBLIC_API_URL: URL for the Flask back-end.
• SQLALCHEMY_DATABASE_URI: Connection string for MySQL.
• CHROMADB_URI: URL for the ChromaDB service.
• JWT_SECRET_KEY: Secret key for authentication.

Installation and Usage

1. Clone the repository:

   git clone https://github.com/Lip1200/color_movie.git
   cd color_movie

2. Set up environment variables in .env files for both services.
3. Start the services with Docker Compose:

   docker-compose up

4. Access the app at http://localhost:3000.

Future Enhancements

• Expand the AI model for more complex recommendations.
• Add user-specific profiles for personalized movie recommendations.

License

This project is licensed under the MIT License.