Refactoring is a critical task in software maintenance and is usually performed to enforce best design practices, or to cope with design defects. Extract method refactoring is widely used for merging duplicate code into a single new method. Several studies attempted to recommend extract method refactoring opportunities through program slicing, program dependency graph, code modification analysis, structural similarity, and feature extraction. However, all approaches thus far are interfere with developer workflow, consider all refactoring suggestions in the entire projects without focusing on the context of development. To increase the adoption and usage rates of extract method refactoring, in this demo, we aim at investigating the effectiveness of machine learning algorithms in recommending extract method refactoring by maintaining the workflow of the developer, and then reporting on a user study that evaluates the proposed technique.