FoodLen is a machine learning-powered application that automates meal recognition and nutritional tracking. By leveraging advanced image recognition models, the system detects various food items from images, estimates their caloric content, and retrieves detailed nutritional information from the USDA Food and Nutrient Database.

Food Detection: Utilizes machine learning models from Hugging Face (kaludi/food-category-classification-v2.0, nateraw/food) and a custom-built model for precise recognition of fruits and vegetables.

Nutritional Information: Seamless integration with the USDA database to provide detailed nutritional breakdown, including proteins, fats, carbohydrates, vitamins, and minerals.

Streamlit Interface: Built with Streamlit for a user-friendly and intuitive interface, making meal logging as simple as snapping a photo.

Automate food detection and calorie estimation.

Provide users with real-time feedback on their dietary intake.

Enable seamless integration with nutritional databases for comprehensive tracking.

1-Clone the repository. 2-Install the required dependencies. 3-Run the Streamlit app to start using FoodLen.