The Arogyabite system integrates several algorithms to provide allergen detection, food label scanning, and personalized diet recommendations:
Users enter details like age, weight, allergies, and preferences to create a profile. Food labels are scanned or uploaded, processed with OpenCV, and text is extracted using Tesseract OCR. NLP techniques match ingredients to a trained allergen database for detection. Machine learning models (e.g., KNN, Random Forest) then generate personalized, allergen-free diet plans, factoring in nutritional content like calories, protein, and carbs to align with user goals.
PROPOSED METHODOLOGY
User Profile Creation: Users input personal details like allergies, dietary preferences, and health goals, which are stored in MongoDB for customization.
OCR-based Food Label Reading: Users upload images of food labels. The Tesseract OCR engine extracts text, which is pre-processed for accuracy.
Personalized Diet Recommendations: The system uses machine learning models to recommend foods and diets based on user preferences, allergies, and health goals.
Natural Language Processing (NLP): SpaCy processes ingredients and nutritional info, ensuring accurate extraction and understanding of food labels.
Statistical Representation: The system generates graphs and charts using Matplotlib to visualize users' dietary intake and allergens exposure.
User Interface: Built with React.js and Tailwind CSS, the interface allows easy interaction, while the backend, powered by Flask and Django REST Framework, manages data and APIs.
Continuous Improvement: User feedback helps refine machine learning models, improving allergen detection and dietary recommendations.
LANGUAGES AND TOOLS USED Frontend:
React.js: For building a dynamic, user-friendly interface.
Tailwind CSS: For modern, responsive styling.
Tesseract OCR: Open-source OCR engine used to extract text from food label images.
Backend:
Python: Used for backend development, specifically for integrating machine learning models.
Flask: A lightweight Python framework for building the backend and handling API requests.
Django REST Framework: For building robust APIs to manage the data flow between frontend and backend.
Express.js: A fast, minimal web framework for building the backend in JavaScript.
Node.js: A JavaScript runtime used for the backend development.
Database:
MongoDB: A NoSQL database used for storing user profiles, food information, and allergy-related data.