Club Terp

Helping people find events to go to at UMD based on their interests.

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Inspiration

Our inspiration for this project came from our experience with a website called YemNews. YemNews helps users discover a tailored selection of news stories based on their interests each day. Intrigued by this concept, we sought to create a similar platform for the University of Maryland (UMD) community.

We recognized the need for a centralized hub where members of the UMD community could easily find events that cater to their interests and schedules. With this vision in mind, we embarked on developing a website that serves as an events finder specifically designed for the UMD community. Our goal was to provide a seamless and personalized experience for users to explore and engage with various events happening on or near the UMD campus.

What it does

The UMD Events Finder is designed to streamline the process of discovering campus events for members of the University of Maryland (UMD) community.

User Experience

  1. Interest Input: Users can input their interests via a search bar on the platform.
  2. AI Matchmaking: Our AI "matchmaker" algorithm processes the user's interests and preferences.
  3. Event Recommendations: Based on the user's input, the system suggests relevant events from the calendar.umd.edu listing.
  4. Personalized Experience: Users receive tailored event suggestions that align with their interests and preferences.

Integration with Calendar.umd.edu

Our platform aggregates events from the official UMD event calendar, ensuring comprehensive coverage of campus activities.

How we built it

Backend

  • We utilized a Python script to extract event data from calendar.umd.edu and transform it into a CSV file.
  • Leveraging the OpenAI API, we developed an AI model capable of generating event recommendations based on user interests.
  • Python scripting was employed for data processing and model integration.

Frontend

  • Our frontend was developed using JavaScript, HTML, and CSS
  • The frontend allows users to input their interests and interact with the AI "matchmaker" to receive personalized event recommendations.

Hosting

  • AWS Lightrail: We chose AWS Lightrail to host our web application, scripts, and AI models. Lightrail provided a reliable and scalable hosting solution, ensuring optimal performance for our platform.

Workflow

  1. Data Extraction: Event data was collected from calendar.umd.edu using a Python script and converted into a CSV format.
  2. AI Integration: The CSV file served as input for our AI model, which generated event recommendations based on user interests.
  3. Frontend Development: Using JavaScript, HTML, and CSS, we developed the frontend interface for users to input their interests and receive event recommendations.
  4. Deployment: The entire system, including the frontend, backend scripts, and AI models, was deployed on AWS Lightrail for hosting.

Challenges we ran into

  • Working with difficult APIs
  • Time Constraint
  • Lack of experience with web development and interacting with APIs

Accomplishments that we're proud of

  • The amount of AWS and OpenAI we were able to use in this project
  • The amount of APIs we learned in this short time
  • Our productivity

What we learned

  • We need to use less APIs
  • Managing time properly
  • How we can develop a product from an idea to its finished

What's next for Club Terp

The next steps for this product is fixing the final bug fixes we didn't get to as well as integrating more UMD websites so we can find more events to recommend. We also want to create a better UI/UX design

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