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RhythmiFind offers personalized music recommendations tailored to your tastes, providing a unique and engaging music discovery experience.

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RhythmiFind

Discover Your Soundtrack

RhythmiFind offers personalized music recommendations tailored to your tastes, providing a unique and engaging music discovery experience.


Table of Contents

  1. Team Members
  2. Technologies
  3. Challenge Statement
  4. Risks
  5. Infrastructure
  6. Existing Solutions
  7. APIs and Methods
  8. Data Modeling
  9. User Stories

Team Members

  • Godwin Chukwuma: Backend Developer
  • Emmanuel Paul: Frontend Developer

Justification for Roles

Assigning roles based on expertise ensures efficiency. Godwin's proficiency in handling complex backend systems and Emmanuel's flair for frontend design perfectly cater to the project's demands, setting a solid foundation for success.


Technologies

Languages and Frameworks

  • JavaScript (Node.js) for backend logic
  • HTML/CSS for frontend structure and styling
  • React as the frontend framework
  • Tailwind CSS for styling
  • Supabase for database management

Database

  • Postgres for structured data storage
  • Supabase for real-time database management and serverless backend
  • Supabase for Restful API

Trade-offs and Decisions

  • React vs. Vue.js: Chose React for its robust ecosystem and component-based architecture.
  • Tailwind CSS vs. Bootstrap: Preferring Tailwind for flexibility and customization.
  • Tailwind CSS vs. Traditional CSS: Tailwind CSS offers UI development with predefined classes.

Challenge Statement

RhythmiFind delivers personalized music recommendations, aiming for a tailored listening experience based on individual preferences. It focuses on user interactions to generate relevant suggestions, enhancing the music discovery process.

Scope Limitations

The project does not address copyright issues or broader industry challenges, focusing solely on improving the user's music discovery journey.

Target Audience

Music enthusiasts seeking a personalized discovery experience.


Risks

Technical Risks

  • Scalability challenges
  • Integration complexity

Non-Technical Risks

  • Privacy concerns
  • User engagement and retention

Infrastructure

Branching and Merging

Adopting Gitflow ensures a structured development process.

Deployment Strategy

CI/CD pipeline facilitates automated testing and deployment.

Data Population

Incorporating data from music databases and APIs enriches the recommendation algorithm.

Testing Strategy

Employing unit, integration, and end-to-end tests ensures comprehensive coverage.


Existing Solutions

Comparison with Spotify and Pandora, focusing on user-centric approach and transparency in recommendation processes.


APIs and Methods

API Routes for Web Client

(List of API routes and methods)

Methods for Other Clients

(List of methods for other clients)

Third-Party APIs

  • Spotify API
  • Stripe API
  • Supabase Auth API
  • Supabase Database API

Data Modeling

Visual representaion of the project structure

Entity Relationship Diagram (ERD)

Below is the Entity Relationship Diagram illustrating the data model for RhythmiFind:

Data Modelling

Author

  • Emmanuel Paul
  • Godwin Chukwuma

About Author

Emmanuel Paul and Godwin Chukwuma are students of software engineering at ALX, actively engaged in developing their skills in the field.

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RhythmiFind offers personalized music recommendations tailored to your tastes, providing a unique and engaging music discovery experience.

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