Inspiration

As a full time college student, I am constantly studying and trying to connect concepts right as the next test comes around. My favorite way of studying is using flashcards - they provide a quick and efficient way of memorizing a lot of information at once. However, the strain of righting 100 flashcards in one sitting can be a lot, so naturally as a Computer Science major, I sought a way to automate this process.

What it does

Quickcards prompts the user to enter a YouTube video URL, so that it can retrieve the videos transcript, parse the video into keywords and an abstract summary using Azure Language Studio, and finally passes the keywords and the abstract summary into OpenAI to generate definitions for each keyword based on the schema of the abstract summary.

How we built it

I built Quickcards using Python, as well as three separate API's :

• YouTube Transcript API
• Azure Language Studio
• OpenAI ## Challenges we ran into Even though this was my first project to use API's, let alone three at once, I think my biggest challenge was time - there were plans to add Django to the project so that there could be a user-friendly front-end, but I am a student first, and I had to prioritize other projects. ## Accomplishments that we're proud of Though there is no front-end for Quickcards, I'm very happy that I managed to make the three API's work in harmony, and produce the desired result. ## What we learned Throughout this Hackathon, one of the biggest things I learned was how to use API's in your own code - it's an incredibly powerful tool to have, and I'm excited to test out some more with projects next year, ## What's next for Quickcards Over winter break, I intend of doing good deal of research on Django, as well as front-end development for Python in general. I'd love for Quickcards to have an interface people can actually interact with, and can't wait to see what i'm able to create with it.

Built With

• azurelanguagestudio
• openai
• python
• youtubetranscriptapi