DietCoach: AI-Powered Personal Diet and Workout Recommender System

Personalized healthcare and fitness guidance have become increasingly important due to the growing prevalence of unhealthy lifestyles and lifestyle-related diseases. This paper presents an AI-powered mobile application that generates customized diet and workout recommendations according to individual user characteristics such as age, gender, weight, height, activity level, dietary preferences, and fitness goals. The proposed system combines rule-based health metric analysis with a large language model (LLM)-based inference engine to deliver adaptive and context-aware recommendations in real time. The application is developed using Flutter to support cross-platform mobile deployment, while Firebase is utilized for cloud synchronization and local offline storage respectively. The system calculates important physiological parameters including Body Mass Index (BMI), Basal Metabolic Rate (BMR), and daily calorie requirements before generating personalized plans. In addition, external nutrition APIs are integrated to provide updated food and exercise information. Unlike conventional fitness applications that rely on static templates and generic suggestions, the proposed system provides interactive, scalable, and dynamically adjustable recommendations through AI-driven processing. Experimental evaluation demonstrates that the application can generate personalized responses with low latency while improving user engagement and accessibility. The proposed framework offers a practical and scalable solution for intelligent digital health and fitness management.