Inspiration
Video Link: https://drive.google.com/file/d/19genGPoy917-LoSaF_Y3JPAa_e8C2uRN/view?usp=sharing (We couldn't upload as Youtube video in time) Living as a college student in LA means walking to morning class through a chilly breeze and walking back with the blazing sun bearing down on you. Of course, the dilemma arises, how to dress for such unpredictable weather? Well you don't have your Mom here to dress you everyday, but we present the next best thing -- DayKit: your personalized outfit picker!
What it does
Daykit takes in the user's wardrobe and uses the daily weather highs, lows, and chance of rain to generate a suitable outfit to wear for the day. The user can add clothes as their wardrobe grows, and our app adjusts accordingly.
How we built it
We used Openmeteo API to get the daily weather highs, lows, and chance of rain. Using this data, we determine the types of articles of clothing that are appropriate for the day and randomly generate an outfit from the user's wardrobe following those guidelines. We have a MongoDB database to store the user's wardrobe. We used Swift UI for the front-end and Flask for the back-end, creating a mobile app that utilizes a web server back-end.
Challenges we ran into
- Joining our front-end and back-end
- Dealing with merge conflicts from having multiple GitHub branches
- Setting up and using APIs
Accomplishments that we're proud of
- Built a functioning app =D
What we learned
- How to work on a project in GitHub with multiple contributors
- How to set up a MongoDB Database
- How to create a project using Flask and Swift
- How to use APIs
What's next for DayKit
Next we want to use machine learning so our app can automatically recognize and categorize users' clothes. We want to use OpenAI API to take in the user's current wardrobe status and recommend more clothes for them based on their current preferences. We also want to implement a wish list feature where users who are friends with each other can buy their friend an item off their wish list.
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