The most difficult and critical part of graphical user interface testing is to detect the graphical component of a graphical user interface. To conduct GUI testing or reverse engineering for the GUI interface, the first step is to detect the classes of GUI elements and their exact positions. In this article, we implement a web-based tool to implement graphical user interface element detection named GUIDE. This platform enables its users to detect the classes of graphical user interface testing along with their exact positions by following a very simple series of steps. To deal with complex and diverse images, this toolkit uses a combination of both traditional computer vision methods and models of deep learning. Moreover, to produce an accurate result, this toolkit also has a unique method. It also provides the facility of the dashboard where users can edit or change the results of testing. It also facilitates its user to export GUI classes and images in the form of design files. These files can be further modified in graphical design tools like Photoshop. Overall, this toolkit provides accurate detection and is best to utilize in stream works.