DESIGN AND EVALUATION OF FOODSENSE AI FOR CUSTOMIZED DIETARY GUIDANCE

In today’s fast-paced lifestyle, maintaining proper nutrition has become increasingly challenging due to irregular schedules, limited awareness, and reliance on unhealthy fast food options. Most available diet plans adopt a generic “one-size-fits-all” approach, which fails to address individual health conditions, preferences, and goals. FoodSense AI is an intelligent, AI-powered nutritional recommender system designed to provide personalized meal suggestions tailored to a user’s unique profile, including age, weight, dietary habits, and fitness objectives. The system leverages machine learning algorithms integrated with a Flask-based backend, MongoDB database, and an interactive Streamlit frontend to deliver real-time, data-driven meal recommendations. Using nutritional datasets from Kaggle, the model predicts optimal food combinations that promote balanced eating and long-term wellness. This approach simplifies meal planning, automates diet tracking, and enhances user compliance through intuitive visualization of nutritional values. The proposed system bridges the gap between technology and health management, empowering individuals to make informed dietary decisions. Ultimately, FoodSense AI aims to promote sustainable eating habits and reduce the prevalence of diet-related health issues such as obesity and nutrient deficiencies.