Coffee is the staple of any student on a college campus. Between projects, papers, and programs, it seems you can never get too excited about your next obligation - especially with caffeine flowing through your veins. Regrettably, as we all may know from personal experience, there's such thing as too much of a good thing...

What it does

CoffeeCount uses sleep tracking data from your Pebble smartwatch combined with coffee consumption data (from either your Pebble smartwatch or your smartphone), communicating with a database of caffeine amounts for coffee items.

How we built it

We used MongoDB to store the user's coffee consumption in a database, and then through leveraged a Python-backend and fluid Material Design flavored frontend. Using these two combined, we are able to create a beautiful and functional dashboard for the user to follow up on their coffee drinking habits.

Challenges we ran into

Having the Pebble and Smartphone app communicate with the server was a bit troublesome, but allowed for a hardy and robust method of transferring data to the server quickly and efficiently.

Accomplishments that we're proud of

We're happy to have been able a smartwatch into this project, as well as show dynamic data on a dashboard. With user accounts and a more polished algorithm, this is guaranteed to help anyone with a coffee problem make it a coffee talent.

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