TravSum

TravSum transforms your travel photos into one cinematic highlight per destination, collecting your journey as a visual story that lets you revisit your travels anytime.

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Inspiration

TravSum was inspired by the desire to transform everyday travel memories into cinematic stories. We wanted to create an app that not only preserved adventures but also enhanced them with creative themes, making every trip unforgettable.

What it does

TravSum lets users upload multiple images from a specific trip and choose a theme (e.g., foodie, fun, nature, play). The app retrieves metadata from each image, categorizes them using AI, and then combines the relevant images into one dynamic video highlighting that journey.

How we built it

  • UI/UX: Designed intuitive and visually appealing interfaces in Figma.

  • Front-End: Developed using React Native, enabling smooth image uploads, theme selection, and a feed that showcases completed journeys.

  • Back-End: Built an API with Node.js and Express to:

    • Extract metadata (map location, date) from each image.
    • Utilize the Claude API to label each image by theme.
    • Filter images based on the user-selected theme.
    • Combine filtered images into a single video using ffmpeg.

Challenges we ran into

  • Integrating the client (React Native) with the back-end (Node.js/Express) for seamless communication.
  • Handling diverse media formats and ensuring consistency during video processing with ffmpeg.
  • Accurately extracting and utilizing image metadata and AI categorization to match user-selected themes.

Accomplishments that we're proud of

  • Successfully combining advanced video processing with a user-friendly mobile interface.
  • Implementing a dynamic backend that not only processes image metadata but also leverages AI for smart categorization.
  • Creating a cohesive system that brings together front-end design, server logic, and multimedia processing.

What we learned

  • The importance of full-stack integration—from designing an engaging UI in Figma to building robust back-end APIs.
  • Hands-on experience with media processing using ffmpeg and overcoming challenges related to format consistency.
  • The value of iterative development and troubleshooting, especially when working with AI-driven image categorization and complex client-server interactions.

What's next for TravSum

  • Enhance the AI categorization to support more themes and improve accuracy.
  • Optimize video processing for faster and more efficient rendering.
  • Expand the mobile app features, such as social sharing options and personalized travel feeds.
  • Gather user feedback and iterate on both design and functionality to make TravSum even more engaging.
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