Leveraging LLMs and Computer Vision for Personalized Nutrition Advice

This paper investigates the application of large language models (LLMs) in the domain of dietary advice, focusing on the recognition of ingredients and nutritional values from food products and the integration of this information into a system capable of delivering personalized recommendations. The research involved the development of a mobile application utilizing React Native and Python Flask frameworks. Optical character recognition (OCR) was implemented through the docTR model to extract nutritional information and ingredients from product images. Based on the extracted data and user profiles stored in a Firestore database, the system generates tailored dietary advice employing OpenAI’s GPT-3.5-turbo model. The findings demonstrate the feasibility of using LLMs to provide personalized dietary recommendations, thereby opening new opportunities in the digital transformation of nutrition and dietary planning.