BrokeBites

Inspiration

How can everyday ingredients become gourmet meals without breaking the bank? Inspired by the magic of resourcefulness and creativity in our own kitchens, we created BrokeBites to turn scarcity into culinary art.

What it does

BrokeBites is a personalized meal prep app that generates meal suggestions and creates tailored grocery lists based on your dietary preferences, allergies, and budget. It leverages AI to provide creative, nutritious, and affordable meal plans that empower you to take control of your culinary journey.

How we built it

 Frontend:
 We built a responsive web application using Next.js and React, designing an intuitive interface that guides users through account setup, preference selection, meal recommendations, and ingredient lists.

 Backend:
 The backend is powered by Flask and MongoDB, handling user authentication, meal recommendation logic, and integrating with external APIs. We also used OpenAI’s GPT models to generate creative ingredients and JSON outputs.

 APIs & Data:
 Our recommendation engine processes user preferences using custom algorithms and integrates with a pricing API to deliver real-time grocery cost estimates. We also used CSV data to simulate realistic ingredient pricing.

Challenges we ran into

We faced a number of challenges throughout this journey. From the very process of generating our initial idea to discovering the technologies and frameworks needed to implement it, we faced hardships every step of the way. We initially struggled with how to integrate Capital One’s Nessie API into our project, which is when we came up with the idea of analyzing a users’ transactions using Nessie in order to identify their food preferences. We further struggled with determining the best ways to classify users’ food choices and how to best process data to make recommendations easier.

Accomplishments that we're proud of

Overall, we feel as though we were able to accomplish what we sought out to do. We have created a product that will dynamically adapt to users’ ever changing palate, generating homemade alternatives to the users’ usual food preferences. By providing information on the price and ingredients, we make it easier for users’ to budget and save money on outside food.

What we learned

Coordinating between multiple technologies (Flask, Next.js, MongoDB, OpenAI API) presented challenges—especially with package versions and environment configurations.

What's next for BrokeBites

Mobile Integration: We plan to build a native mobile app version to provide a seamless experience on the go. Additionally future updates will include advanced machine learning models to better predict user preferences and adapt recipes in real time.