While munching down on 3 Snickers bars, 10 packs of Welch's Fruit Snacks, a few Red Bulls, and an apple, we were trying to think of a hack idea. It then hit us that we were eating so unhealthy! We realized that as college students, we are often less aware of our eating habits since we are more focused on other priorities. Then came GrubSub, a way for college students to easily discover new foods for their eating habits.

What it does

Imagine that you have recently been tracking your nutrient intake, but have run into the problem of eating the same foods over and over again. GrubSub allows a user to discover different foods that fulfill their nutritional requirements, substitute missing ingredients in recipes, or simply explore a wider range of eating options.

How I built it

GrubSub utilizes a large data set of foods with information about their nutritional content such as proteins, carbohydrates, fats, vitamins, and minerals. GrubSub takes in a user-inputted query and finds the best matching entry in the data set. It searches through the list for the entry with the highest number of common words and the shortest length. It then compares this entry with the rest of the data set and outputs a list of foods that are the most similar in nutritional content. Specifically, we rank their similarities by calculating the sum of squared differences of each nutrient variable for each food and our query.

Challenges I ran into

We used the Django framework to build our web application with the majority of our team not having prior knowledge with the technology. We spent a lot of time figuring out basic functionalities such as sending/receiving information between the front and back ends. We also spent a good amount of time finding a good data set to work with, and preprocessing the data set so that it would be easier to work with and understand.

Accomplishments that I'm proud of

Finding, preprocessing, and reading in the data set into the Django framework was one of our first big accomplishments since it was the backbone of our project.

What I learned

We became more familiar with the Django framework and python libraries for data processing.

What's next for GrubSub

A better underlying data set will naturally make the app better, as there would be more selections and more information with which to make comparisons. We would also want to allow the user to select exactly which nutrients they want to find close substitutes for. We implemented this both in the front and back ends, but were unable to send the correct signals to finish this particular function. We would also like to incorporate recipes and ingredient swapping more explicitly into our app, perhaps by taking a food item and an ingredient, and being able to suggest an appropriate alternative.

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