Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives to complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex world in the sentence into account. In this paper, we are proposing a lexical simplifier that is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool that can make use of the broader context of the sentence in both forward and backward directions. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after the computation is over.