Authors - Sunil Sangve, Rutuparn Kakade, Saamya Gupta, Sahil Akalwadi, Soham Pawar Abstract - The paper introduces a novel and detailed approach for automated generation of fully customizable and user-centric food recipes. The system utilizes fine-tuned GPT2 and fine-tuned YOLOv9 to work efficiently. The user can input the available ingredients by uploading an image which is then processed by the fine-tuned YOLOv9 model to extract the ingredients present in the image. Alternatively, the user can also input ingredients manually. The user input also contains macro and micro nutrient levels, caloric values, cholesterol levels, taste and texture. Utilizing fine-tuned GPT2 LLM, the system then generates a unique recipe that follows the input. Web application developed by Next JS 14 with backend support from Flask, helps greatly in enhancing User experience. The system also utilizes Stable Diffusion to generate colorful recipe images for user reference of how the final recipe should look like.
