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https://github.com/hamid-rezaei/music-recommender
This project, developed using the Django-rest-framework, serves as a practical training ground for students enrolled in the Cloud Computing Course,
https://github.com/hamid-rezaei/music-recommender
django django-rest-framework docker docker-compose postgresql rabbitmq simple-storage-service
Last synced: 7 days ago
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This project, developed using the Django-rest-framework, serves as a practical training ground for students enrolled in the Cloud Computing Course,
- Host: GitHub
- URL: https://github.com/hamid-rezaei/music-recommender
- Owner: Hamid-Rezaei
- Created: 2024-02-23T11:38:08.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-03-12T19:07:30.000Z (8 months ago)
- Last Synced: 2024-11-09T23:16:31.738Z (7 days ago)
- Topics: django, django-rest-framework, docker, docker-compose, postgresql, rabbitmq, simple-storage-service
- Language: Python
- Homepage:
- Size: 660 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Music Recommender System
## Overview
This project, developed using the Django-rest-framework, serves as a practical training ground for students enrolled in the Cloud Computing Course, offering hands-on experience and insights into the utilization of Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS) paradigms.
Implementation Details
----------------------
The project architecture comprises three distinct sections:
- `main`: The central component of the project is a Django application that acts as the primary service, orchestrating the entire system by managing API endpoints and coordinating the logic across all sections. This Django app serves as the main service, connecting and integrating the functionalities of other sections.
- `adapter`: Serving as the cornerstone for interacting with essential cloud services such as Amazon S3, RabbitMQ, and MailGun.
- `schediler`: This section functions as a dedicated scheduler within the project framework, executing tasks in response to specified job triggers. This segment operates autonomously, managing and overseeing the scheduling of various jobs based on predefined criteria.
How To Run
----------
1. **Clone the project**
2. **Set Up the Environment**: Fill this `.env` file with appropriate values.
```
# General
FILES_FOLDER=
# Postgres
DB_NAME=
DB_USER=
DB_PASSWORD=
DB_HOST=
DB_PORT=
# Redis
REDIS_HOST=
REDIS_PORT=
# RabbitMQ
RABBITMQ_AUTO_ACK=
RABBITMQ_USERNAME=
RABBITMQ_PASSWORD=
RABBITMQ_HOST=
RABBITMQ_PORT=
RABBITMQ_QUEUE=
RABBITMQ_VHOST=
# S3
ENDPOINT_URL=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_STORAGE_BUCKET_NAME=
# Shazam
SHAZAM_API_KEY=
SHAZAM_URL=
SHAZAM_HOST=
# Spotify
SPOTIFY_API_KEY=
SPOTIFY_SEARCH_URL=
SPOTIFY_RECOMMENDATION_URL=
SPOTIFY_HOST=
# Mail
MAIL_URL=
MAIL_API_KEY=
MAIL_DOMAIN_NAME=
```
3. **Create project docker image**
```bash
docker build . -t main_service
```
4. **Create docker network**
```bash
docker network create musicnet
```
5. **Start docker-compose.yml file**
```bash
docker-compose up -d
```