Allergen Based Diet Suggestor

The Allergen Based Diet Suggestor is a specialized application designed to help users identify and customize their diets based on specific allergens, ensuring they receive suitable food recommendations that align with their dietary needs. This project provides an intuitive user experience, allowing users to filter food options based on allergens, ensuring a safer and more personalized diet selection.

🛠️ Tech Stack

The app was built using the following technologies:

• JavaScript: The core programming language for application logic and interactivity.
• TailwindCSS: For a streamlined and responsive user interface, with a clean, modern design.
• MongoDB: Utilized as the primary database to store user data and dietary recommendations, particularly optimizing for allergen data management.
• Express: A fast and lightweight framework for building server-side logic and managing API endpoints.

📋 Features

• Allergen-Based Filtering: Users can filter diet suggestions based on their specified allergens. This feature allows for highly customized dietary plans, making it especially valuable for individuals with allergies.
• User Authentication: The app includes an authentication flow, allowing users to securely log in and save their dietary preferences and allergens.

🏆 Achievements

This project won the MongoDB Track Prize at Electrothon 6.0 held at NIT Hamirpur. This recognition highlights the effective use of MongoDB's capabilities in managing and organizing allergen and dietary data efficiently.

🌐 Application Flow

1. User Registration/Login: Users create an account or log in to access personalized features.
2. Allergen Selection: Users specify their allergens, and the app filters dietary suggestions accordingly.
3. Diet Suggestions: The app provides a list of suitable food options based on the user’s allergen profile, making dietary choices safer and more convenient.
4. Saved Preferences: Registered users can save their allergen preferences for quick access during future sessions.

🚀 Future Enhancements

Possible additions to improve functionality include:

• Enhanced Dietary Recommendations: Adding more filters such as dietary preferences (vegetarian, vegan, etc.) and nutrition-based sorting.
• Mobile App Integration: Expanding to a mobile app for easier accessibility on-the-go.
• Machine Learning Models: Integrating machine learning for more accurate allergen predictions and dietary suggestions.