Facing an increasingly polarized American society, we thought of the idea of providing users with articles presenting varying perspectives on a particular topic (ex: communism). We believe that this encourages civil discussion among users and help them gain a much more nuanced and comprehensive understanding of a particular societal issue, contributing to the spread of knowledge among the public.

What it does

It allows users to post their thoughts on featured articles. Users also have the option of responding to the posts of other users.

How we built it

We built the app using HTML/CSS for the user interface. We used web API's to fetch data from Wikipedia and provide users with articles. We used Javascript for handling user posts and responses.

Challenges we ran into

The main challenges we ran into were on the server side with integrating our python machine learning code with our HTML/JavaScript front end. Oftentimes, the local test servers fails to run and keep producing a number of strange errors despite following documentation exactly. We were able to find workarounds in some cases like extracting a file to be hosted on a different website. But in many cases, we are unable to integrate our completed python backend with our front end, leaving some functionalities we initially intended unable to be realized.

Accomplishments that we're proud of

We're proud of being able to fetch article content from Wikipedia and provide a corresponding image that corresponds to the article. We were able to provide succinct article previews for the user in the webpage.

What we learned

Throughout the project, we were able to gain a significantly better understanding in client-side internet technologies and development using HTML, CSS and Javascript. We also got a profound exposure into firebase's authentication and realtime database features. The text-filtering part of this project also got us very interested in the field of artificial intelligence and machine learning. And although we can only run our final ML product independently from our front end, we learned a lot about networking and server connection/communication throughout our trial-and-errors that lasted days.

What's next for E-lightenment

While we originally intended to implement a text-filtering feature that would use machine learning to analyze and give feedback on whether a post was polite or not, we had issues integrating the python with our HTML and JavaScript. We referenced one of our previous projects in making this app, but the backend code was significantly changed and revamped and the layout was readapted. As such, the next step for E-lightenment would naturally be this text-filtering feature. We were also planning to implement a point system that would reward users based on the politeness of their posts. However, because our plans for the text-filtering fell through, the point system unfortunately fell through as well. We plan to develop this point system once we figure out the text-filtering.

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