Building a Real-Time Snack Detection Web App

SnackTrack is an innovative web application designed to monitor eating habits in real-time using computer vision technology. By employing a custom-trained object detection model on Roboflow, the app discerns between apples and cookies, rewarding or penalizing users with points based on their snack choices. The system integrates Roboflow for object detection and MediaPipe for facial landmark detection, allowing it to identify when a snack is near the mouth, thus logging an eating event. Developed with a Flask backend, OpenCV, and a frontend using JavaScript, the application maintains real-time updates through Server-Sent Events (SSE). Users can replicate the project by following a detailed process involving dataset collection, model training, and application setup, with the flexibility to adapt the system to recognize different snacks. The project demonstrates a practical implementation of AI in enhancing personal health monitoring, with the code and dataset publicly available for customization and further exploration.

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