## Inspiration

The difficulty in deciding outfits when shopping—how do I know if something matches what I already currently own?

## What it does

Scans a QR code linked to the clothing item, and pulls up the image on the app. The user is free to scan multiple images and mix and match clothing items.

## How we built it

In the eve of the second to last day, we learned that our original idea was taken, won, and already a functioning startup in the UPenn community (XpressCart). We formulated a version of this for clothes (with/the ability to view custom outfits).

## Challenges we ran into

The login kept crashing. We also faced many memory issues which would cause the application to crash.

## Accomplishments that we're proud of:

We have drastically increased the efficiency of common digital transactions by reducing the amount of clicks from 5 to 1 (in order to perform a transaction).

## What we learned

This was more of an application after learning experience (except for the newbie on our team). Basically everyone had some general idea of what to do; and if we didn't know, we knew exactly what to search for in those moments.

## What's next for FitMe

More features, such as usability
## Inspiration

We were inspired by the difficulty of filtering out the clothes we were interested in from all the available options. As a group of motivated undergraduate software developers, we were determined to find a solution.

## What it does

MatchMyStyle intelligently filters brand catalogs using past items from your personal wardrobe that you love. It aims to enhance the shopping experience for both brands and their consumers.

## How we built it

We split our tech stack into three core ecosystems. Our backend ecosystem hosted its own API to communicate with the machine learning model, Cloud Firestore, Firebase Storage and our frontend. Cloud Firestore was used to store our user dataset for training purposes with the ability to add additional images and host them on Firebase Storage.

The ML ecosystem was built using Google Cloud's Vision API and fetched images using Firebase Storage buckets. It learns from images of past items you love from your personal wardrobe to deliver intelligent filters.

Finally, the frontend ecosystem demonstrates the potential that could be achieved by a fashion brand's catalog being coupled with our backend and ML technology to filter the items that matter to an individual user.

## Challenges we ran into

We knew going into this project that we wanted to accomplish something ambitious that could have a tangible impact on people to increase productivity. One of the biggest hurdles we encountered was finding the appropriate tools to facilitate our machine learning routine in a span of 24 hours. Eventually we decided on Google Cloud's Vision API which proved successful.

Our backend was the glue that held the entire project together. Ensuring efficient and robust communication across frontend and the machine learning routine involved many endpoints and moving parts.

Our frontend was our hook that we hoped would show brands and consumers the true potential of our technology. Featuring a custom design made in Figma and developed using JavaScript, React, and CSS, it attempts to demonstrate how our backend and ML ecosystems could be integrated into any brand's pre-existing frontend catalog.

## Accomplishments that we're proud of

We're proud of finishing all of the core ecosystems and showing the potential of MatchMyStyle.

## What we learned

We learned tons in the 24 hours of development. We became more familiar with Google Cloud's Vision API and the services it offers. We worked on our Python / Flask skills and familiarized ourselves with Cloud Firestore, and Firebase Storage. Finally, we improved our design skills and got better at developing more complex frontend code to bring it to life.

## What's next for MatchMyStyle

We would love to see if fashion brands would be interested in our technology and any other ideas they may have on how we could offer value to both them and their consumers.
**Inspiration**
Currently, society faces many environmental challenges with the fashion industry known as one of the most polluting industries in the world. Fast fashion produces clothing that isn't made to last since they are made with cheap materials that harm the environment (landfill impact, pesticides in growing cotton, and toxic chemicals making their way into water). However, sustainable clothing use materials that are made to last longer and are non-harmful to the environment. By choosing to have sustainable clothing, one can reduce their waste significantly and spend less money when shopping for clothes. We believe that users would benefit greatly from an intuitive app that can be used to track their wardrobe that can let them know how sustainable their wardrobe is and also predict the type of clothing they have using Tangram.

**What it does**
The app allows users to explore their closet by letting the user add clothing that they already have in their closet by selecting the type of clothing (shirt, pants, shorts, jacket, etc.) and then picking the brand of clothing. The user can then take a picture of the clothing item that they wish to upload and can add more items if they wish to. After they are done adding their items, the app then generates their sustainability score and gives them outfit recommendations as well that are more sustainable for their closet, which would take them to the store’s website and purchase it on-app. Users can also get points from buying from a sustainable brand which they can redeem through gift cards from some of their favorite sustainable brands. The model that was built using Tangram recognizes the type of clothing the user has based on what the user takes a picture of.

**How we built it**
For the model: we learned how to work with Tangram, using a CSV to create a .tangram model, and then using that model to test individual data and judge the overall accuracy. Then, we were able to find a large dataset online that had thousands of images of different clothing, all labeled for type (i.e. shirts, shoes, pants). We converted those images into binary strings and created a new CSV with those strings and the corresponding types. That was then used to train a Tangram model and we came out with about 61% accuracy, which is slightly better than a completely random guess would be expected to be, with so many clothing options.

**Challenges we ran into**

Tangram does not have support yet for image recognition, so we did have to think of a way to pass in the images to test the library in use of that. When training, there were issues in the inclusion of commas in the binary strings (messing up the CSV formatting) and the cleansing of data we performed to remove clothing we deemed irrelevant.

**Accomplishments that we're proud of**

Although the model is not the most accurate, we are proud of trying to find a way to apply this tool, Tangram, to a new purpose in image recognition. Also, learning how to train and use this machine learning library was a useful skill that multiple people on our team can use in the future.

**What we learned**

We learned how to use an intuitive machine learning library and a little about how image recognition and data cleansing work.

**What's next for Sound of Sustainability**

The development of a full front-end using some cross-platform tool such as React Native, and the connection of that with our user interface and machine learning model to create a fully functioning app.