Record Relations

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  "One Dance" reference

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  "SKIPP" reference

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  Home Screen

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  Loading Screen

Inspiration

We were greatly inspired by The Internet Map and the visuals of everything being connected in some way.

What it does

It follows a recommendation path from a user input song or artist using last.fm and displays a graph showing a number of different recommended songs. The colors correspond to genres, and the nodes are scaled based on the popularity of the song.

How we built it

We use Python for the backend along with the API from last.fm and Flask to integrate it with the frontend. The algorithm recursively recommends songs, branching out into different areas or even circling back to the beginning. We use HTML and Javascript for the frontend.

Challenges we ran into

One of the main challenges was creating a visually appealing frontend. We had initially tried using React, but all the files led to too much complexity for a short time frame, so we ended up using with classic HTML and JS. Another challenge we faced at the beginning was using the Spotify API. The functionalities we wanted was deprecated, so we had to search for a new API that could serve our needs. In addition to this, the API we used did not have a genre output, so we had to improvise with the tags we could get, in order to have some sort of grouping.

Accomplishments that we're proud of

We are proud of ourselves for working so diligently at our first hackathon and working through the different issues that we have faced.

What we learned

Hackathons are really time intensive, and one has to do quite a bit of research to make sure all of the plans and technologies are feasible within such a period.

What's next for Record Relations

We would love to host this on a website and store a database of songs that have been searched and their connections. This way we could make one big map of all the songs.

Built With

• css
• flask
• html
• javascript
• last.fm
• python