HarmoniFi: A Full-Stack Spotify Clone with Intelligent Recommendations

Project Summary

HarmoniFi is a full-stack web application that replicates the core functionality of Spotify while incorporating an intelligent music recommendation system. This system personalizes the user experience by suggesting music tailored to individual preferences.

Project Lead

• Oluwamusiwa Olamide David (Project Manager, Backend Developer, Frontend Developer, Database Administrator, AI Specialist)

Technologies

• Programming Language: Python
• Framework: Django (backend)
• Frontend: HTML, CSS, JavaScript, Tailwind CSS
• Database: PostgreSQL
• Hosting Platform: Zeet (https://zeet.co/pricing)

Resources

• Django Documentation: https://docs.djangoproject.com/en/5.0/
• Sign Up for Zeet: https://www.youtube.com/watch?v=HozBkJACvPg (Note: Double-check the actual Zeet sign-up link)
• Last.fm API on RapidAPI (example resource): https://rapidapi.com/category/Music
• Spotify Clone Example on GitHub (for reference): https://github.com/topics/spotify-clone
• Django and Cloud Hosting: https://www.cloudwithdjango.com/
• Getting Music Data with Last.fm API using Python: https://www.dataquest.io/blog/last.fm-api-python/

Challenge Statement

• Problem to Solve: Enhance the music streaming experience by offering users a Spotify clone equipped with an intelligent music recommendation system.
• What it Will Not Solve: HarmoniFi will not address music licensing issues. It assumes the availability of content similar to Spotify.
• Target Audience: Music enthusiasts who seek a user-friendly platform with personalized music recommendations.
• Global Reach: The project is targeted towards a global user base, with no specific locale dependence.

Risks and Mitigation Strategies

• Technical Risks:   * Potential API restrictions: We will maintain regular communication with Spotify regarding API updates.   * Algorithm complexity: Prototyping different algorithms will help us determine the most efficient and scalable approach.
• Non-Technical Risks:   * User adoption: We will incorporate user feedback loops to continuously improve the platform and attract users.   * Competition: We will develop well-defined marketing strategies to stand out from existing solutions.

Infrastructure

• Version Control: We will utilize Git for version control, employing branches for feature development and merging after thorough code reviews.
• Deployment Strategy: Automated deployment on Zeet, including deployment of the PostgreSQL database. Staging environments will be used for thorough testing before pushing updates to production.
• Data Population: Initial data will be populated from public music datasets. User-generated data will also contribute to enriching the system over time.
• Testing: Unit testing will cover backend logic, while Selenium will be used for frontend testing. Additionally, continuous integration will ensure automated testing throughout the development process.

Existing Solutions and Differentiation

• Spotify: Similarities include music streaming and playlist functionality. However, HarmoniFi differentiates itself with its intelligent music recommendation system.
• Apple Music and Deezer: Similarities include music streaming capabilities. Differentiation factors lie in user interface/user experience design and the implementation of unique recommendation algorithms.

Reimplementation Decision:

While existing solutions offer music streaming services, HarmoniFi aims to stand out by providing an intelligent recommendation system. It combines the strengths of popular platforms with a personalized user experience.

HarmoniFi Image Progression

This section showcases the visual evolution of the HarmoniFi user interface (UI) through different stages of development.

• Concept Stage:

• Prototype Stage:

• Alpha Stage (Example URL):

• Beta Stage (Example URL):

This README provides a comprehensive overview of the HarmoniFi project. Please feel free to add comments or suggestions for improvement.