Moodini 🎵🧠

AI-powered Spotify music recommendations based on weather conditions and user mood, built with a scalable microservices architecture.

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Overview

Moodini is a modern music recommendation platform that combines:

• 🌦 Weather-based music recommendations
• 😊 Mood-based music recommendations
• 🎧 Spotify integration
• 🤖 AI-powered mood analysis
• 🏗 Microservices architecture
• ⚡ Scalable and cloud-ready infrastructure

The goal is to create a personalized music discovery experience by understanding both environmental context and user emotions.

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Features

Weather-Based Recommendations

Generate Spotify song recommendations based on the current weather in the user's location.

Examples:

Weather	Recommendation Style
Sunny	Pop, Dance, Funk
Rainy	Lo-fi, Ambient, Piano
Snowy	Chill, Acoustic
Cloudy	Indie, Alternative

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Mood-Based Recommendations

Users can:

• Select a mood manually
• Describe how they feel in text
• Receive AI-analyzed recommendations

Supported moods:

• Happy
• Sad
• Calm
• Energetic
• Angry
• Romantic
• Focused
• Neutral

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Spotify Integration

• Spotify OAuth Authentication
• Track Search
• Playlist Creation
• Personalized Recommendations
• User Library Integration

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AI Recommendation Engine

Moodini combines multiple signals:

• User mood
• Current weather
• User preferences
• Listening history (future feature)

to generate highly personalized recommendations.

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Architecture

Frontend (Next.js)
        │
        ▼
API Gateway (Django)
        │
 ┌──────┼─────────────┐
 ▼      ▼             ▼

Mood   Weather   Recommendation
Service Service      Service

        │
        ▼

Spotify Service
        │
        ▼

Spotify Web API

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Tech Stack

Frontend

• Next.js
• TypeScript
• Tailwind CSS
• Axios

Backend

• Django
• Django REST Framework
• FastAPI

AI

• OpenAI API (planned)
• Hugging Face Transformers (planned)

Database

• PostgreSQL

Infrastructure

• Docker
• Docker Compose
• Redis (planned)
• Kubernetes (planned)

Development Workflow

• Git
• Git Flow
• GitHub Actions (planned)

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Project Structure

moodini/
│
├── services/
│   ├── gateway/
│   ├── user-service/
│   ├── mood-service/
│   ├── weather-service/
│   ├── spotify-service/
│   └── recommendation-service/
│
├── frontend/
│
├── infra/
│   ├── docker/
│   └── nginx/
│
├── docker-compose.yml
├── README.md
└── .gitignore

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Design Principles

Moodini follows:

• HackSoft Django Styleguide
• Domain-driven organization
• Thin views, fat services
• Separation of concerns
• Independent deployable services
• Scalability-first architecture

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Development Workflow

Branching Strategy

main
│
develop
│
├── feature/*
├── release/*
└── hotfix/*

Examples:

feature/spotify-auth
feature/mood-analysis
feature/weather-integration
release/v1.0.0
hotfix/fix-oauth-callback

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Getting Started

Clone Repository

git clone https://github.com/<your-username>/moodini.git

cd moodini

Start Services

docker compose up --build

Frontend

cd frontend

npm install

npm run dev

Backend Services

Run each service independently or through Docker Compose.

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Roadmap

Version 1.0

• Microservices foundation
• Django API Gateway
• Mood Service
• Weather Service
• Recommendation Service
• Frontend foundation

Version 1.1

• Spotify OAuth
• Real Spotify recommendations
• User profiles
• Playlist generation

Version 1.2

• OpenAI mood analysis
• Embedding-based recommendations
• Recommendation feedback loop

Version 2.0

• Redis caching
• Kubernetes deployment
• Real-time recommendations
• Recommendation analytics

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Contributing

Contributions are welcome.

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push your branch
5. Open a Pull Request

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License

MIT License

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Author

Mehran RedRose

• GitHub: https://github.com/mehranredrose
• Email: mehranredrose@gmail.com

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Project Status

🚧 Under Active Development

Moodini is currently in the early development stage. The architecture is being built with long-term scalability and maintainability in mind.