SpotiMe

SpotiMe is a react web app that uses the Spotify API and Machine Learning Emotion detection to recommend music based on emotions and music taste.

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SpotiMe 🎧 - MLH Fellowship Pod 3.3.0 Team 1

SpotiMe is a react web app that uses the Spotify API and Machine Learning Emotion detection to recommend music based on emotions and music taste. Once the user logs in with their spotify account, the website shows the summary of the user's music breakdown. The user can click the button "Play" to initiate a camera to capture the user's face (through front camera/webcam) which is analyzed by our ML model to detect the user's emotion. Based on the emotion detected, the user will receive a customized playlist by SpotiME!

Give it a try! https://spotime.duckdns.org

What it does 🖥

  • Shows breakdown & summary of the user's spotify account
  • Recommends a playlist based on the user's current emotion using Machine Learning emotion analyis

How we built it 🛠

To collaborate, we used:

  • Git
  • Discord chat / voice call

To design the website, we used:

  • Figma

To build the website, we used:

We deployed our website using:

  • AWS EC2
  • Duck DNS
  • Nginx

We tested and monitored it with:

  • Github Actions
  • cAdvisor
  • Prometheus
  • Grafana

We set up our database using:

  • mongoDB

Cool repo visualization just for fun:

  • Image

Challenges we ran into 😡

  • Our ML module was hard to deploy. We failed to deploy it on Haroku and ended up migrating it on AWS.
  • Another issue we kept facing an Nginx and CORS error.
  • Miscellaneous errors such as a bug 403 Forbidden page or connection issue, as well as a process such as integrating all frontend and backend was challenging but we were able to

Accomplishments that we're proud of 🌟

  • We are proud of our overall project. However, we are specifically proud of successfully athenticaing Spotify API, connecting the ML module with the captured photo to detect the emotion, clean and user friendly UI, being able to tackle challenging issues that arised during the deployment. The team had excellent communication skills and collaboration to successfully accomplishing the project! In addition, we are very proud that what we've built hasn't commonly done before - that we are one of the pioneers of the new way of song recommendation.

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