Have you ever eaten with your eyes? What about buying a bunch of fruit and then completely forgetting about it? Globally, we waste about 1.4 billion tons of food every year. The issue with food waste, especially in America, is concerning to us and we decided we wanted to create an app that combats this issue in a sustainable way by incorporating healthier and more sustainable practices into things you already do everyday. After researching the issue more, three statistics lead us to prioritize three key features in our app*:

  • More than 80% of Americans discard perfectly good food because they misunderstand expiration labels
  • Wasting food contributes to 11% of the world’s greenhouse gas emissions
  • 43% of waste comes from the home
    Our app focuses on incorporating three ways to combat food waste whilst also encouraging users to live healthier lives by suggesting recipes that include ingredients they already have at home, sharing food with a community (such as college students who live in the same dorm), and getting notifications when food is going to expire and how to compost it.

What it does

The app has a number of features from keeping a living inventory of what's in one's kitchen (categorized from freezer to fridge to beverages). Additionally, one can input dietary restrictions and food preferences which will then impact what recipes the algorithm suggests they make based on those and their recipe history. The app also notifies users when food will expire soon and lets them either share the food with their community, see recipes which will use those ingredients, or give resources on how to compost the item if applicable. The app suggests recipes with what's in your kitchen and one can update what's in their kitchen via many means such as scanning the item or receipt.

How we built it

On the front end side we used Expo and React Native. We used Figma to develop our designs for the front end. As for the backend we primarily used Express. To record our data we used MongoDB for the database and hosted it on Google Cloud. For our Machine Learning Image Classification we also used Google Cloud with the Cloud Vision API. To implement the recipe recommendation algorithm we used data from the Spoonacular API.

Challenges we ran into

As for most hackathons, time and remaining sharp with little to no sleep seems to be the biggest obstacle. Besides this we ran into a coupe more challenges we had to overcome: Learning how to incorporate different APIs

  • Getting the front end to work well with the back end
  • Creating the algorithms for suggesting recipes based on their preferences
  • Finishing all the screens we designed for; we overestimated how quickly we could finish a screen because it takes awhile to get the details right so we designed a lot of screens in Figma that won’t end up getting finished

Accomplishments that we're proud of

We were very proud of all we accomplished in the past 36 hours! Some of our highlights are: Creating an app that can have a sustainable real world impact on a real issue that we should all be working on ways to combat

  • Using Figma to document our design iterations and making comprehensive designs to be implemented later
  • Incorporating lots of visuals and options that can accommodate users of numerous backgrounds, making the app very accessible and intuitive
  • Designing a really cute logo :)
  • Overall, combining a lot of different and complex pieces of technology and having them all work together

What we learned

We're all really proud of learning how to divide up the work to specializations that way we all got to work on pieces of the project we were interested about and enjoyed doing. We're also glad we got to learn more about this pertinent issue and how we can make a difference. From a tech standpoint, individually Rachel learned more about how the front and back end communicate and work together along with the importance of good designs and implementing them; Munim learned about making API calls and using databases in a project; Michael learned about implementing Machine Learning features with Google Cloud; Jesse learned about understanding scope of a detailed project like ours and staying realistic with goals in a high pressure situation.

What's next for Mindful Bytes

We had many stretch goals we ran out of time for, these include:

  • Community recipes → friends within communities can plan events like friendsgiving within the app to pull ingredients together to make stuff and share grocery lists
  • Publish it to the app store for all to use
  • Implement the rest of our designs on Figma that we didn’t have time
  • Adding live image recognition since right now it only works for still images
  • Adding a hardware feature that will detect expiring food within fridge


Figma Designs Walkthrough:
Demo of App:

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