FoodSnap: Mobile Application Instant Pricing with Image Recognition

This project enhances traditional food pricing methods by employing image recognition and machine learning. Unlike physical price tags which can lead to inconsistencies, this app enables users, particularly financially constrained students, to capture food images and receive accurate pricing information instantly. The mobile application is built on a robust dataset to ensure the model's accuracy across various cuisines. The development process follows the waterfall model, consisting of five phases: Requirement Analysis, Design, Development, Evaluation, and Documentation. Key technologies used include TensorFlow for machine learning model training, MySQL for secure data storage, and Flutter for cross-platform mobile application development. This combination allows users to seamlessly capture images and receive real-time pricing details. The applications accuracy and usability were tested through user acceptance tests (UAT). During testing, all modules worked flawlessly, and the application was praised for its simplicity and effectiveness. The findings highlight that this mobile application significantly improves food pricing accessibility and accuracy. With ongoing updates, it holds the potential to transform consumer interaction with food pricing, increasing transparency and enhancing overall user satisfaction.