Voice-powered recommendations that understand your mood, dietary needs, and nutrition goals.

Starting university is overwhelming with all the new classes, new routines, and for many first-years, the first time making daily food decisions without any guidance. We noticed first-hand how often students struggled to choose what to eat while juggling dietary restrictions, fitness goals, emotional well-being, and the stress of residence life. We wanted to build something that doesn’t just list menu items, but actually understands mood, history, and dietary needs to make food choices easier and healthier.

Recommends what to eat from dining halls around campus for first-year Waterloo students living in residence, based on audio recordings, pre-selected preferences, diet restrictions, diet goals, physical and emotional feelings, and past data.

React + TypeScript on the frontend to collect user input through clean, responsive UI components.
Python on the backend to orchestrate logic, data processing, and API communication.
Multiple AI and LLM services to interpret audio, extract mood and context, and generate meaningful recommendations.
A structured pipeline that converts all user audio, preferences, and history into clean data that Gemini can process effectively.

Expanding recommendations to all restaurants within a certain vicinity using Google Maps integration.
Publishing FoodieTrack so that first-year students can use it during orientation and throughout the year.
Adding real-time menu updates, nutrition breakdowns, and habit-tracking features.