AI DIET RECOMMENDATION SYSTEM

Abstract

This AI system provides automated dietary plans through content-based filtering by assessing food characteristics alongside user health targets and dietary limitations and individual food choices. Scikit-Learn machine learning library executed learning algorithms from the selected dataset that encompass elements about portions and nutritional value and ingredients and types of meals. The framework consists of FastAPI for power backend processing and Streamlit for user-friendly front operations through which users can provide their dietary information. When user inputs reach the machine learning model the system suggests meals which fulfill both the user specifications and their health targets. Real-time operation allows the system to integrate modifications to user eating habits focused on health without disrupting the user experience throughout an extended period of use. Such a system would benefit from more development to integrate third-party dietary APIs which provide nutritional data and enable users to input their nutrition logs. The implemented combination of technologies ensures the system operates at its peak performance while upholding speed and scalability which allows it to serve numerous users simultaneously.