Vibe

Vibe is a service to give you a vacation itinerary just from a location and how you are feeling. It takes the frustration out of planning a complex vacation.

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Vibe.

Inspiration

Have you ever been sitting around in your home, bored and hoping you could travel somewhere that captures the Vibe you want? Websites like Tripadvisor give you a general list of things to do, but you have to sort through these recommendations yourself to gauge their vibes. We wanted to make a website that does this all for you based on how you want the vibes to be.

What it does

With Vibe, you don’t have to worry about typing exactly what you want. Just type in what you want the Vibes to be like, and the website takes care of the rest for you! Vibe lets you type in a feeling that you would like to capture and it returns a list of tailored recommendations for you. You can also enter any location and choose how far away you want these recommendations to be from this location.

How we built it

We built this project using a variety of services including Google Cloud Run which automatically containerized our machine learning model using Docker, Google Maps, React, and a pre-trained machine learning model from hugging face. Our pre-trained model takes in the feeling the user has entered on the front end and sends it to the backend where it returns places with similar reviews to the feeling.

Our Architecture

Challenges we ran into

We faced many challenges while we were trying to implement Vibe. The first challenge we ran into was finding out how exactly to implement the website architecture in a way that could be worked on by multiple people at the same time rather than having to wait for each person to finish their part before the next person could start. We solved this problem by mapping out how we thought the website architecture would look like, and then tweaking it so that each person could work on a different part. Another challenge we faced was the learning curve required to build our backend. We had to learn what software we could use and how to implement web scraping, machine learning models, and how to connect everything in order to build a functioning website.

Accomplishments

As Sophomores, we are proud of ourselves for pushing our knowledge and working with a variety of unfamiliar technologies. Understanding how to implement servers, work with Google Cloud Run, machine learning models to return scores were all very difficult. We also read through a variety of articles and watched numerous youtube videos to learn about different techniques for our task.

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