First install the necessary packages by running npm install in terminal. Then, run the app by calling command npx expo start. If it doesn't work, try npx expo start --tunnel
Students love finding and wearing cute school spirit, especially at games or tailgates. However, tailgating clothes specific to a college are quite hard to find, especially at a reasonable price or secondhand. We created a platform that makes it easy for students of a particular campus to trade and pass down their school spirit, building the community and helping the planet in the process.
A student creates and logs in with their account info to be taken to an interface of four pages. The first is a home page, where they can scroll through other listings and filter results based on their preferences. The second is a cart they can add to once they discover pieces they like. The third is a profile page, where they can access their information as well as pieces they've previously bought, listed, or sold. The fourth is a seller's page, where they can upload a picture and other information to be listed to other students.
Most of our work was built with React Native. A Firebase database stores account and item information and links relevant data to the specific user.
- Servers. We ran into trouble in hosting them both locally and on Google Cloud, which resulted in a significant setback for the incorporation of our various API's.
- Storing images. Storing user input for images in a database was surprisingly more complicated than expected. We first attempted to use Firebase's built in storage feature, but couldn't find a way to link the image with the rest of the item data. We then attempted to encode the user's image into a Base64, then decode it on the homepage. Unfortunately, the built in method for encoding an image failed to produce a valid encoding.
Pretty frontend and color schemes, a fully functioning and fully integrated database, ability to list items and add items to cart, individual users and unique logins, and more
Mobile is hard
AI feature that automatically tags pictures with their attributes, location filtering to find the closest listings to a user, filtering based on item attribute, and more!