As an avid listener of Triple J and their annual Hottest 100, I set out to scrape open data in order to ‘predict’ the Hottest 100.

This project used the spotipy library to scrape for playlists matching the search query ‘triple j hottest 100 2020’ which is automatically generated when authenticating through the Triple J app with Spotify. With this in mind, we were able to aggregate ‘votes’ for the Hottest 100.

Despite the challenges of writing efficient web-ready code, I believe this project served as a great learning exercise for a ‘full stack project’.

Another technology used, namely tinydb, was used for persistent storage of voting data allowing Realtime in-memory caching.

Finally, the option for the user to login with Spotify (through OAuth 2.0) was implemented to allow the streaming of songs and adding them to the users personal collection. By implementing this, I can successfully say that I am more confident with OAuth and look forward to using it in further projects.

Special thanks to James Spencer for working together on the project. This was my first real exposure to working alongside another developer, and allowed me to appreciate working in a team environment.

Click here to visit the site and explore our predictions. The site is currently frozen, however as voting begins in late 2021 we will re-enable the site and begin the aggregation process once more.