Grab a byte was inspired by the story of a friend who struggled to meet her nutritional needs. She sought to live a balanced lifestyle, and at the core of that lifestyle was a healthy diet. She would constantly keep track of the macronutrients in every individual item she consumed, however, the process slowly took away large chunks of her time.
Out of this situation, grab a byte was created, to aid people like her. Grab a byte is an iOS exclusive image recognition platform that applies firebases' API to identify specific foods and provide their nutritional value with accuracy.
To build the front end of the application, the team used Xcode and the Swift 4.0 programming language. At the same time, the front end calls a back end which is built using a firebase API and FirebaseAutoML kit. The Firebase API and Machine Learning Kit are responsible for taking the picture and running it through a Machine Learning Algorithm to determine the type of object. From there, the front end takes this data and appropriately provides the correct nutritional facts to the user.
Some challenges we ran into included:
- integrating API data with iOS controls
- working with Firebase and it's iOS implementation
- Issues with the lower accuracy of the on-device Firebase labeller model
Due to the 36-hour time limit, grab a byte failed to reach its full potential. A future feature expansion for the app includes a diet management system in which the user inputs their daily targetted caloric amount and scans their meals as they go through their day. The app would register every component of their meal, providing an approximate caloric value, and adding it to their daily total. In doing this, the user can manage their daily caloric intake without having to meticulously plan and research every component of their meal.
Accomplishments that I'm proud of
- Some teammates had never coded in Swift before, learned a lot building an iOS app for the first time
- Harsh built a fantastic UI
What I learned
- How to integrate Firebase into an iOS app
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