EcoEats

This project was inspired by experiences with food waste. University students often face busy schedules and may forget the food in their fridge or buy whatever is available during exam season. Reflecting on these decisions highlights the significant impact of food consumption and interaction on the environment. It is important for people to take every purchasing decision seriously, as every bite wasted could have gone to someone in need.

EcoScore is accessible via browser on a laptop. Upon opening, users can choose to start scanning or add an item to inventory. Selecting scan mode opens the camera, allowing users to place a food item in view. A picture is automatically taken, processed, and the food item is identified, displaying a corresponding "Eco-Score" out of 5. The Eco-Score considers carbon emissions and water consumption during production, comparing it with data from other items.

The basic user interface (front end) was developed using HTML, CSS, and JavaScript. The back end involves a machine learning model built with TensorFlow. Hundreds of pictures of different food items were used to train the model for accurate prediction. An algorithm for developing the Eco-Score was also created.