Eye diseases pose a significant global health challenge, affecting millions of people and leading to substantial visual impairment. Analyzing eyelid blinking and movement is crucial for monitoring patients with abnormal eyelid motions. This paper introduces the so-called Bapp (Blink Application), a mobile application designed to evaluate eyelid movement by recording the opening and closing of the eyes over time and detecting eye blinks using a machine-learning approach. The application processes pre-recorded videos and then validates the results against publicly available datasets. The proposed system operates in a unified stage, utilizing the pre-recorded video as input and evaluating the degree of eyelid openness in each frame using predictions from Google ML Kit, a machine learning-based framework integrated into the Flutter platform. The results are stored in a local database and can be exported to Excel for further analysis. Developed on the Flutter platform, the Bapp supports multiple languages, ensuring accessibility to a broad audience. The blink prediction results align with those obtained from other methods applied to the same dataset, demonstrating the app´s effectiveness in objectively monitoring the clinical progression of patients with eyelid diseases.