My APParel

Inspiration

We actually all realized in our rush to make it to HackDavis, we didn't have time to choose a decent outfit. On many occasions in the past, we've also not dressed for the right weather in a hurry. With an innovation like this, students can also realize how much clothes they actually use over time to save shelf space, cut down on spending, and get rid of articles of clothing they've been hoarding.

What it does

My APParel takes a data frame of clothing items as your "virtual wardrobe" and uses str.contains() functions to detect if a certain attribute of the clothing is met. If so, it remains as a possible selection for the day. If not, it is removed from the df and you are left with your filtered clothing items. In addition to sorting by color and article of clothing, this takes advantage of the outside temperature data provided by the sustainability challenge to filter by forecast prediction based on recorded measurements from the previous year.

How we built it

We used python in Jupyter notebook to display the filtering prompts.

Challenges we ran into

We initially intended on creating a GUI for My APParel, but the outdated method of tkinter combined with limited memory space on the machine and python expertise among our team(none of use used python extensively, if at all, in the past) didn't allow us to proceed.

Accomplishments that we're proud of

Applying the OSIsoft sustainability outside temperature data for UC Davis as a filtering option and definitely pushed our logical knowledge in manipulating large data frames.

What we learned

We basically all learned and applied the basics of Python in 24 hours and performed pretty extensive research on GUI application for our code.

What's next for My APParel

If provided time in the future, this idea definitely shows promise to be turned into a mobile app for easier, more accessible, use, further filtering options, such as texture, and wardrobe manipulation, like acknowledging if clothes are in the laundry.