A Hybrid LLM based Model for Calorie Tracker and Dietary Control

The importance of attainable and efficient dietary management options is clearly visible through the increasing occurrences of obesity and associated health conditions. While traditional techniques of calorie tracking are useful, they can be labor-intensive and highly prone to errors, highlighting the requirement for novel technology alternatives. This work presents the use of advanced natural language processing to intensify user experience and accuracy in dietary control for a Large Language Model (LLM)-based calorie tracker. To get a more natural approach for measuring calorie intake, creating customized dietary goals, and receiving real-time feedback we use AI and machine learning algorithms in the program (GPT-3 and GPT-4). To control intermittent fasting habits, measure hydration, and monitor weight variations, various tools are provided by the device. Deep learning techniques such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) are implemented to give support and personalized recommendations to help users overcome hindrances and achieve their health goals, thus contributing to overall health and well-being.