What it does
VibeCheck analyzes and interprets a Spotify user's top 10 tracks of last month to provide a general mood of their listening history and tailor specific words of wisdom based on their music vibe.
How we built it
Using React frontend and a FastAPI backend, deployed as a decoupled micro-services using Docker. We pulled data from SpotifyAPI using python and utilized numpy to determine the mood of a user's listening history based on danceability, energy and valence.
Challenges we ran into
Setting up the Docker Compose, coming up with the algorithms to figure out how to categorize the songs into different music vibes and provide messages based on each category.
Accomplishments that we're proud of
We successfully set up a streamlined development environment using Docker Compose, allowing individual management of services. The segregation of authentication files in the .creds folder enhances security, ensuring that sensitive information remains protected.
What we learned
How to navigate Spotify Web API, got experience in React, and the practical differences in vector similarity matrix between cosine similarity and Euclidean distance. Applying linear algebra to practical data science was a cool lesson we learned from this project. Most of our team was not familiar with Docker, so we got the chance to explore that and utilize compose functionality.
What's next for VibeCheck
Expanding the range of analyzed data, providing more statistics and doing a sentiment analysis on the lyrics to provide a more accurate message descriptions.
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