LingoBridge

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  Bridging the Gap between Generations One Word at a Time

Inspiration

A viral Australian senator speech a couple of weeks prior to this Hackathon reminded us of the importance of communication and reaching one's target audience. One may do so through explanations, examples, and even purposeful vocabulary and diction in order to communicate and convey their ideas. Senator Fatima Payman's speech implemented such techniques to reach Gen Z and Gen Alpha through the usage of "brainrot" language using terms such as "capping" and "skibbidi." While this language does target the youth, it often leaves out those who aren't as updated on modern slang including those amongst an older generation and those who aren't as familiar with English. This is how LingoBridge was born.

What it does

LingoBridge is an innovative translator that takes slang-filled language and transforms it into a digestible explanations that anyone can understand. First, the user will have the option to choose between 3 options to translate their text into "Brain Rot," "English," or simply exit out of the application. They will then be prompted to insert their desired text. Afterwards, the terminal will display their now translated text AND explanation. They will be prompted for another input until they exit out of the application.

How we built it

We built the project by using the OpenAI library and using API calls to reference ChatGPT. We first gave AI model context on our desired tasks by telling it that it will be a translator and what "Brain Rot" reads like. After that we made a chat loop that lets the user chat with the model in the terminal. This was done by using a while loop with logic statements to continue or break the loop depending on the user's input.

Challenges we ran into

Getting to our final product required several reruns and restarts. Our initial idea involved redistricting/gerrymandering, and creating an informative website regarding its implications. We thought this would've been a good project yet we found that we couldn't tie our project neatly into the prompt so we had to scrap it. Brainstorming our current idea took us a while as we started struggling and panicking about whether we would be able to even complete a project before the deadline.

Accomplishments that we're proud of

We're proud of the fact that we were able to integrate our experiences into our project and use an API for the first time ever. We are also glad that we persevered after our initial setback in the beginning as we struggled with coming up with a useful idea that is related to the prompt.

What we learned

We learned how to use an API key for the first time as well as how to use the OpenAI library, allowing us to better understand the AI platforms we utilize to aid us in our learning processes. We better understood how large language models work and how AI platforms are able to use deep learning to perform natural language processing. Just as we attempted to bridge the gap between generations through the creation of LingoBridge, the gap between our group members was also bridged as we haven't formally worked in a group project together before, let alone a hackathon.

What's next for LingoBridge

Because we built this into the terminal, we would increase its accessibility in the future by creating a user-friendly website which would look more professional and credible.

Built With

• ai
• api
• llm
• ml
• python