Whether it be through scrolls, manuscripts, or books, the act of reading is what allows us to gain access to new categories of knowledge. However, with the rise in social media came the prevalence of shorter, more succinct internet content which has resulted in a general decrease in attention span and reading comprehension. This problem has been magnified by the COVID-19 pandemic, where people have become increasingly reliant on the internet to stay connected to the outside world. The lack of reading comprehension leads to rash decision-making and a lack of personal growth. Quotary is our solution to help incentivize reading and improve reading comprehension levels within the individual as well as society as a whole.

What it does

Quotary is a web app that provides a random quote to the user from a book. The user chooses to either skip the quote or get more info on it. If the user likes it they can access more information about the quote such as from what book it's from and what type of category it falls under.

How we built it

Quotary was built using:

  • Platform: Flask
  • Languages: Python, CSS, HTML, SQLite3
  • Frameworks: SQLAlchemy, Jinja2

SQLite3 was used to store information about the quotes in the database. This database was then linked to the frontend.

Challenges we ran into

As a team, this was the first time we had tried to implement a back-end solution with databases with a front-end solution using Flask. Thus, we had to overcome the challenge of looking at new documentation for libraries we were unfamiliar with.

Accomplishments that we're proud of

  • Implementation of SQLAlchemy database
  • Consolidation of Flask Backend and HTML/CSS/Javascript Frontend
  • Using a Framework for the first time with very limited help

What we learned

By building Quotary, we learned how to collaboratively work effectively together when simultaneously working on the front-end and back-end of an app using Flask. We were able to learn how to access the database using sqlite3 and learned how to query to retrieve information of the book based on the user's choice to get more info on a quote.

What's next for Quotary

In the future, we plan to extend Quotary by adding new features such as allowing the user to choose a specific category of quotes or books to choose from, as well as a recommendation algorithm that recommends quotes that the user might like based on their previous interactions with the web app. We plan to implement this algorithm based on weights on liked themes using AI/ML or another statistical approach. The focus is on ideas and themes rather than trivial measurements (book cover, visibility), making it better than conventional algorithms.

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