Track Nutrition Using Gen AI

Abstract - In this paper, we present a novel system that enables users to track their daily nutrition using Generative AI (Gen AI). Traditional methods of food tracking involve manual logging, which is time-consuming and prone to human error. Our solution leverages generative models to simplify this process by allowing users to input meals using natural language or images. The system intelligently processes these inputs using large language models (LLMs) and computer vision (CV) to extract nutritional details. Personalized recommendations are provided based on the user's health goals and dietary patterns. The proposed method not only improves accuracy but also enhances user engagement by reducing friction in the food logging process. Results demonstrate high accuracy in food identification and calorie estimation, making this a scalable solution for modern health monitoring. Key Words: Generative AI, Nutrition Tracking, NLP, Computer Vision, Health Monitoring, Personalized Diet