NutriSafe:Allergen detection using NLP and Gen AI

In recent years, the growing prevalence of food allergies and the need for allergen detection has gained significant attention, particularly concerning health. Recognizing this need, our work focuses on developing a sustainable solution for allergen detection. To address this challenge, we developed a web application that helps to detect allergen foods present in the food item, using Natural Language Processing (NLP) and Optical character recognition (OCR). This web application is designed to process the images of food product labels to extract relevant ingredient information which is then used to assess the consumption of the product by the user. The user information about dietary restrictions and allergies is stored in the Firebase. The OCR component uses Tesseract.js, to convert images of food labels into texts. This text is then processed using the spaCy for Named Entity Recognition (NER) to extract pertinent food ingredient information for cross referencing with the user’s dietary profile which is done using the Natural Language Toolkit (NLTK), that evaluates whether the product is safe for consumption. This intelligent system highlights the application of Generative AI and automated analysis capability, which aims at enhancing the efficiency and accessibility of food safety assessments for users.