Nutribrain

Inspiration

As foodies and lovers of travel, we both share a love of adventurous food. The less pronounceable the name, the better. We dive right in without a second thought. But many of our friends are subject to dietary restrictions - be it allergies, diabetes, or simply due to moral or religious reasons. We wanted to empower them to join us as culinary pioneers without having to worry themselves about staying with known safe dishes.

What it does

Nutribrain combines the power of machine learning with a robust nutritional database, allowing users to see a list of probable allergens and other relevant nutritional information.

How we built it

We used CoreML to run image classification in near-real time, and React Native to connect everything together.

Challenges we ran into

Setting up React Native was tricky considering neither of us had any background in iOS app development. Moreover, we did have to tweak a few of the native components, which meant navigating some Objective C.

Accomplishments that we're proud of

This was our first major iOS app, and it was incredibly gratifying to see our own code running on our phones. Moreover, this was the first time either of us used React Native, and our first major machine learning project. It was a heck of a lot of firsts.

What's next for Nutribrain

In the future, we'd like to polish the app quite a bit and perhaps train a more versatile model. After which, we'd add support for additional nutrition stats and dietary restrictions. From there, we may do some form of nutrition logging before releasing it to the world.

Built With

• core-ml
• ios
• javascript
• react-native