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Uses the Spotify API to graph a multivariable analysis of songs related to the user's top tracks!

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Spotify Mood Ring v2

This project originally got its start in 2019 during a Hackathon at the University of California, San Diego. My teammate, Brandon Hawi, and I worked on this project for two days, and figured out a basic understanding of the Spotify RESTful API as well as there authentication measures. We created a very rudementary cite that allowed users to sign in to their Spotify account and access recommended songs based solely on their current top 5 songs. The project at this point consisted of a few EJS files and few Javascript files to access user data and graph it!

The Update!

Recently, I had decided to fork the orignal Mood Ring project and revamp it to add more functionality. Some of my goals for the v2 of the web app was to clean up the overall backend code, reduce the amount of EJS by simplifying data storage, and reduce the amount of calls made to the API when visiting our web app. I was able to add a navigation bar, some error handling, and more pages to allow for more functionality and seamlessness when moving around the cite.

What's To Come ?

In the future I would like to add song recommendations based on genres the user chooses to allow a broader scope of data analysis in the user's search for new music. I would also like to incorporate the ability to make or edit playlists from these recommendations so that users can start listening to their new recommended music right from the interface of the web app! Another option I am exploring the possibility of is the ability to select multiple artists and return all the music from all of the artsits in the past X amount of days, so that users can catch up on music they may have missed

Spotify Mood Ring v1 - Brandon Hawi and Ryan Loveland (2019)
Spotify Mood Ring v2 - Ryan Loveland (2022)

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Uses the Spotify API to graph a multivariable analysis of songs related to the user's top tracks!

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  • EJS 49.3%
  • JavaScript 48.5%
  • CSS 2.2%