Allergen-Aware Food Ordering Web App for a Fast Food Restaurant

This study focuses on the development and implementation of a system designed to detect allergens in food ingredients using Natural Language Processing (NLP) techniques. The system was created to improve food safety and prevent allergic reactions by accurately identifying allergens found in product ingredients. Python, Flask, and MySQL were used to create the system, which detects allergens using natural language processing (NLP) techniques. The methodology involved tokenising input text, removing stop words, lemmatization and matching tokens against a database of known allergens. The results show that the system detects allergens with high accuracy while also providing a more user- friendly and accessible interface for end users, which compares favourably to existing literature. The system's implementation provides a valuable tool for people with food allergies, allowing them to make safer food choices and potentially lower their risk of allergic reactions.