BoozeBuddy

Cheers to Clarity
BoozeBuddy

Inspiration:

Created to assist new 21-year-olds navigating bar menus and dining options, BoozeBuddy aims to simplify the world of drinks and cuisine.

Relation to Sponsors:

Leveraging MongoDB on Intel's cloud platform, BoozeBuddy revolutionizes 21-year-olds' dining experiences with personalized drink recommendations and intelligent food pairings, ensuring both enjoyment and responsible consumption.
Powered by MongoDB and Intel's cloud infrastructure, BoozeBuddy employs cutting-edge algorithms to curate tailored drink suggestions and dietary accommodations, setting new standards for seamless UX in the realm of dining apps.

What it does: BoozeBuddy offers personalized guidance on drink choices, food pairings, and responsible drinking limits for a safer and enjoyable dining experience.

How we built it: Our team crafted BoozeBuddy during the University of Virginia's HooHacks event, with HTML/CSS for the front-end, Flask for the back-end, and MongoDB as the data-layer solution.

Challenges we ran into: We encountered challenges in designing intuitive user interfaces and integrating complex dietary restriction features seamlessly. We also ran into trouble finding, and then preprocessing, the large datasets that we needed for our conceptual requirements. A lot of the sources we found were either largely incomplete or unreliable, so data consistency was a big issue.

Accomplishments that we're proud of: We're proud to have developed a user-friendly platform that empowers users to make informed decisions about their drinking and dining experiences. While not the usual choice, we used Flask for a large, multi-page project and put a lot of effort into the back-end design.

What we learned: Through BoozeBuddy, we gained valuable insights into user experience design, database management, and the importance of responsible drinking advocacy. We also learned web data processing, message passing, and route handling working with Flask and MongoDB.

What's next for BoozeBuddy: In the future, we aim to expand BoozeBuddy's features, integrate more comprehensive allergy information, and enhance user engagement through community-driven content. We will expand into cocktails and mixed drinks, and automating more of the data collection process once we find reliable sources.

Tech Used: HTML, CSS, JavaScript, Flask, MongoDB,

Team members:

Abhishek Nepal:Marymount University - [email protected] Demi Daniel-Akanle: (George Washington University) - [email protected] Ethan Ton:(George Mason University) - [email protected]