RateFlix - A safehome for Movie buffs

This full stack application allows users to browse movies, write reviews, and receive personalized recommendations. It utilizes Neural Collaborative Filtering (NCF) for recommendations and LSTM neural networks for sentiment analysis of the provided comment in the scale of 0 to 5.

Architecture

TODO

Team and Contributors

Mahbod 🎨 💻

salehtalebi24 🤔

Alirezaarabi 💻 🤔

Mazdak Pakaghideh 💻 🤔 📖

Technologies

The Recommender System / Sentiment Analysis:

• Python - High-level Progeamming Language
• Pandas - Python Data Analysis Library
• Numpy - Python High-level Mathematical Functions
• NTLK - Python Natural Language Toolkit
• scikit-learn - Python Machine Learning Library
• Pytorch - Python Neural Networks and Deep Learning Library

Datasets TODO

Frontend:

• Vue3 - The Progressive JavaScript Framework
• Nuxt3 - The Intuitive Vue Framework · Nuxt
• Pinia - State Management Framework For Vue.js.
• Tailwind.css - Open Source CSS Framework
• OMDb API
• HTML/CSS

Backend:

• Python - High-level Programming Language
• Django - High-level Python web framework
• DRF - Django REST framework
• JWT Auth and ...

Development

Clone The Repo

user@host:~/$ git clone https://github.com/mazdakdev/RateFlix && cd Rateflix

Create .env file in core/

 SECRET_KEY=your-django-secret-key
 DEBUG=True
 CORS_ALLOW_ALL_ORIGINS=True

Note: you can get Django secret key from here.

Start Backend Development Server

user@host:~/$ cd core && poetry install && poetry run python manage.py runserver

Start Fronted Development Server

user@host:~/$ cd rate-flix && npm install && npm run dev

Deployment

Getting Started with Docker

TODO

1. Clone repo

Deploy manualy

Contributions

This project follows the all-contributors specification. Contributions of any kind welcome!