TuneGraph

Inspiration

Our inspiration behind this program is that we wanted to contribute to the implications of music artists and AI. We originally wanted to analyze trends over periods of times, but analyzing specific attributes can be more helpful in providing artists clues to help direct their music to a specific audience.

What it does

The website takes a dataset of 250+ and parses specific attributes to find the general correlations and trends between the different points.

How we built it

We built the project by originally laying out the design and repository, which we then built upon by building the back end work which we then connected to the front end, adding minor tweaks in between.

Challenges we ran into

Some major challenges we ran into were connecting the front end and back end, as well as minor bugs that seemed correct in the code, but did not run correctly on the website.

Accomplishments that we're proud of

We are proud that we were able to fully finish four graphs and the user interface and design of the website while ensuring that we worked together as a cohesive team.

What we learned

We learned the importance of communicating to each other what we are working on, as well as finding approaches to get around the issues in our code.

What's next for TuneGraph

Hopefully, we may be able to create some sort of game/activity for the users to interact with on the website other than just looking at processed data, providing them with insights on their own music taste.

Built With

• fastapi
• nextjs
• python
• react
• typescript