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According to a Deloitte survey of 2,000 consumers in the United States, 91 percent of users consent to legal terms and service conditions without reading them, resulting in severe privacy risks. We were motivated to create a Summarizer after seeing this. Furthermore, we realized that going over T&Cs isn't just a hardship for us; it's a nightmare for everyone, so we devised this solution to assist them in any way we could.

Summarizer outstanding features works like your virtual PA who's gonna read the large and uninteresting documents to us in a matter of milliseconds so you don't have to go through them.

Initially, we created a responsive and dynamic front end that allows users to engage directly with our prototype. The user interface is made entirely of HTML and CSS. We utilized Flask for the server end or backend. The machine learning domain is where the most essential element of our prototype resides. We employed the hugging face transformers concept in the Natural Language Processing domain and used Python to construct our ML code. Finally comes the integration. We integrated the server end and ML code with the front end to provide a Summarizer to the user in a concise manner.

We were having trouble training the model for the Machine Learning section because we didn't have enough data to do so. Working on natural language processing was also a time-consuming task.

We are happy that we were able to come up with an end-to-end application to assemble a solution and manage application features to work quickly and on schedule.

We learned a lot about Python, making a web server in Flask, Bootstrap, debugging silly errors in our code and managing our limited time efficiently.

We intend to establish a viable web server and work on the model and integrate it with authentication. We also hope to improve performance quicker and smoother in near future!