Vibe Match

- Fetches the top 20 tracks of the user from Spotify's API. - Retrieves audio features for each track - Generates a description of the user's music taste using Ollama - Matches 5 most similar users

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Inspiration

Vibe Match was inspired by the desire to connect music lovers and help them discover their unique musical tastes through data-driven insights. We wanted to create a platform where users can not only explore their favorite tracks but also find similarities with others based on their listening habits.

What it does

Vibe Match fetches users' top tracks from Spotify, analyzes their audio features to generate a personalized music taste description, and displays the top users with similar music tastes, fostering a sense of community among music enthusiasts.

How we built it

We built Vibe Match using Next.js for a dynamic and modular web stack solution, MongoDB for a NoSQL database, and Tailwind CSS for custom styling. We interfaced with the Spotify Web API to automatically pull tracks and song attributes for maximum accessibility.

Challenges we ran into

One of the primary challenges was handling the authentication flow with Spotify, particularly managing the access tokens securely. Additionally, ensuring smooth data retrieval and processing from the Spotify API required careful handling of asynchronous requests and user data.

Accomplishments that we're proud of

We are proud of successfully integrating the Spotify API to fetch user data and audio features, creating an engaging user interface that enhances the user experience, and utilizing Ollama to generate a personalized description of the user's music taste.

What we learned

We learned about the intricacies of working with third-party APIs, particularly in terms of authentication and data management. Additionally, we gained experience in building responsive web applications using Next.js and Tailwind CSS, as well as collaborating as a team to solve problems and iterate on our ideas.

What's next for Vibe Match

Next, we plan to enhance Vibe Match by adding features like social sharing options to allow users to showcase their music taste and improved algorithms for matching users based on their listening habits.

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