AI Powered Weight Based Meal Recommendation QnA Chatbot using Pre-Trained Language Model

In recent years, artificial intelligence (AI) has revolutionized personalized health and nutrition systems by integrating machine learning and natural language processing (NLP). This research introduces an AI-powered, weight-based meal recommendation and question-answering (QnA) chatbot system, developed using pre-trained transformer models (T5-base and T5-large). The system aims to assist users in receiving personalized dietary recommendations based on gender and BMI-based weight categories (Underweight, Normal weight, Overweight, Obesity) and offers interactive responses to common health-related queries. The chatbot engine is trained using a custom dataset of 30 question-answer pairs for each of the 8 user categories (based on gender and weight classification), stored and retrieved dynamically using MongoDB. The model leverages Transfer Learning with T5-base and T5-large, both fine-tuned for sequence-to-sequence tasks. Despite its lighter structure, T5-base remains suitable for low-resource devices with as little as 8GB RAM and integrated GPU, offering scalability and accessibility for broader deployment. The system is served through a Flask backend integrated with a responsive front-end developed using HTML, CSS, and JavaScript, enabling real-time user interaction. The final solution demonstrates the viability of transformerbased language models in healthcare QnA and recommendation systems with minimal computational overhead. This framework provides a blueprint for future developments in AI-powered nutrition systems and intelligent health assistants.