Description

FoodHack is a project coded to process images of users' meals and providing personalized health advice. It runs on react-native and uses firebase for user management, with flask, Azure, and DeepSeek used for image processing and real-time eating recommendations. Copilot was used for debugging. FoodHack fits well with the theme of Code to Connect because it brings users more in tune with their bodies. We are submitting to the Maker Track as well as the 5C, Sustainability, and Health & Humanity overlays. As this is a health product that stores sensitive personal information, we ensured user authentication was robust. In addition, though we personalized health advice to specific users with information such as BMI, we aim to consult health professionals to verify the accuracy of our recommendations.

Purpose

The problems we aim to tackle are threefold and directly pertain to each overlay track. (5C) College students, especially first-years, often struggle to adapt their eating habits to dining halls. This is especially true at the 5Cs, where calories and other nutritional information are not posted consistently around dining halls. FoodHack can either analyze images of menus or food to provide detailed health advice. (Sustainability) More attuned to their bodies, students will only take what they need and minimize waste. (Health & Humanity) Students physical health will improve as they either follow the recommendation to the letter or use it to guide their habits; moreover, burdened by heavy workloads already, the app will streamline dining hall decision-making, reducing anxiety. We hope that FoodHack can evolve to allow users to set long term goals for themselves, inspiring self-reflection, self-pride, and a community of like-minded users at the 5Cs.

How it Works

Users sign up for an account on the FoodHack app, then enter health data to be associated with their profile such as age, weight, height, etc. Users then take a picture of either a food item or menu for the app to process. FoodHack delivers facts about the item such as calorie content, macronutrients, vitamins and minerals, and provides a health recommendation based on the user's profile. Finally, users can store the requests in their history to revisit them later.

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