We propose a system to recognize users and detect reading styles based on eye movement patterns while reading text content on screen. This system utilizes eye-tracking technology and machine learning to distinguish users based on their reading habits and identify four specific reading styles including reading, skimming, and, scanning. By analyzing key features such as fixation duration, saccade length, and regression movements, the system offers a discreet method for user identification and reading behavior analysis in real time. This demo showcases its potential for personalised content delivery and enhanced user experience.