NutriTrack is a cross-platform nutrition tracking application that helps users track daily food intake, analyze nutritional values, and predict calorie consumption using Machine Learning.
This project was developed collaboratively with my friends.
My primary contribution was Backend Development using Spring Boot, including REST APIs, authentication, and ML service integration.
NutriTrack is built as a modular system consisting of three components:
- Cross-platform mobile application (Android & iOS)
- Clean and interactive UI
- Food logging and meal history
- Displays nutritional insights
- Communicates with backend via REST APIs
- RESTful API architecture
- User authentication & authorization using JWT
- Stores user and food data
- Business logic implementation
- Integrates with ML service
- Secure and scalable backend
- Food classification
- Calorie estimation
- Nutrition prediction
- Exposed as REST API
- Extendable to image-based food recognition
- User authentication & profile management
- Daily food logging
- Calorie and nutrition breakdown
- ML-based predictions
- Secure API communication
- Scalable architecture
- Flutter
- Dart
- Spring Boot
- Java
- Spring Security (JWT)
- REST APIs
- Python
- Scikit-learn / TensorFlow
- Flask / FastAPI
- MongoDB