HarmoniFi: A Dynamic Fusion of Next-Gen Music Discovery and Generative AI-Powered Personalization

Project Summary

HarmoniFi is a modern music streaming application designed to revolutionize the way users discover and enjoy their favorite tunes. Inspired by popular platforms like Spotify, HarmoniFi leverages cutting-edge technologies to deliver a personalized and immersive music listening experience.

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.