The Smart Diet Tracker is a sophisticated web application that leverages artificial intelligence and machine learning to provide users with personalized dietary insights. It allows users to log their meals by taking photos, which are then analyzed for nutritional content using TensorFlow and other machine learning techniques. Developed as part of the capstone course (BAN 693), this project integrates various cutting-edge technologies to offer personalized nutritional analysis, making it easier for users to make informed decisions about their diet and health.
- Food Image Recognition using TensorFlow: Utilizes computer vision to identify food items from uploaded images
- Nutritional Content Analysis using USDA API: Provides detailed breakdown of macro and micronutrients
- Personalized Diet Recommendations: Offers tailored dietary advice based on user goals and preferences
- Progress Tracking: Allows users to monitor their dietary habits and nutritional intake over time
- User-friendly interface with real-time feedback
- Python: Primary programming language
- Flask: Web application framework
- TensorFlow: Machine learning library for food image recognition
- OpenCV: Computer vision library for image processing
- Pandas & NumPy: Data manipulation and analysis
- SciPy: Used for linear programming in diet optimization
- OpenAI API: Powers the AI chatbot for nutrition advice
- HTML/CSS/JavaScript: Front-end development
- Upload a food image or manually input your meal details.
- View the nutritional analysis of your meal.
- Get personalized nutrition advice using AI.
- Track your progress and view historical data in the user dashboard.
- Generate meal plans based on your nutritional goals.
- Integrate with popular fitness trackers and apps
- Implement a barcode scanner for packaged foods
- Develop a mobile app version for iOS and Android
- Enhance the AI model to recognize mixed dishes and estimate portion sizes
- Add social features for users to share progress and recipes
We welcome contributions to the Smart Diet Tracker project!