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With the advent of readily and widely available applications based on deepfake technology, several cybersecurity threats are on the rise. It is challenging to curtail these threats as deepfakes are realistic and very difficult to detect. The present work proposes a Fake Detection Tool (FDT) that streamlines the procedure of fake detection by incorporating various manipulation techniques and aids users in detecting and visualizing the same. The tool is also integrated with Twitter for streaming facial image posts based on hashtags. It provides an output dataframe and presents statistics of virality, sentiments, etc, using pie charts for better visualization. The proposed tool uses a wide variety of large-scale datasets for training to deal with the fakes in the wild and deploys models that are at par with the cutting-edge models. It is an efficient, user-friendly, and freely available software for fake detection. The source code of the FDT package toolkit is available at https://github.com/surbhiraj786/GUI_Fake-Detection.