A Deep Learning Framework for Recipe Generation from Multi Input Food Data

In a world with food innovation entwined in artificial intelligence, we introduce here an intelligent web-based system proposing customized recipes on the basis of a deep learning model based on Convolutional Neural Networks (CNN). This system enables people to either add a list of ingredients or even add a photograph of a meal, from where the model distinguishes the dish and gives instructions on how to cook it, along with approximate calorie content, as well as a downloadable list for shopping. Apart from its technical efficiency, our application features a “Flavor Memories” module to enable users to save personal and emotional memories that relate to food, making meals a meaningful experience. The “Community Recipes” module provides an active platform for cultural exchange to share, like, comment, and discover global recipes. Also, an integrated chatbot improves usability by helping users with recipe cooking, nutrition monitoring, and app navigation in general. This system unites technology and human experience, making recipe discovery a personalized, socially interactive, and emotionally rich dining experience. This paper outlines the model structure, system architecture, user flow, and the way CNN-driven model is revolutionizing the way we find and relate to each other through food.