## Inspiration

We admired the convenience Honey provides for finding coupon codes. We wanted to apply the same concept except towards making more sustainable purchases online.

## What it does

Recommends sustainable and local business alternatives when shopping online.

## How we built it

Front-end was built with React.js and Bootstrap. The back-end was built with Python, Flask and CockroachDB.

## Challenges we ran into

Difficulties setting up the environment across the team, especially with cross-platform development in the back-end. Extracting the current URL from a webpage was also challenging.

## Accomplishments that we're proud of

Creating a working product!
Successful end-to-end data pipeline.

## What we learned

We learned how to implement a Chrome Extension. Also learned how to deploy to Heroku, and set up/use a database in CockroachDB.

## What's next for Conscious Consumer

First, it's important to expand to make it easier to add local businesses. We want to continue improving the relational algorithm that takes an item on a website, and relates it to a similar local business in the user's area. Finally, we want to replace the ESG rating scraping with a corporate account with rating agencies so we can query ESG data easier.
# Delta Draw

Watch the [video](https://www.youtube.com/watch?v=hDX5sQmFqY8)

## Project Description:

1. A **mechanical shell** with 3D printed gondolas and a motor-guided marker
2. An **image-processing pipeline** that determines how to draw uploaded images start-to-finish
3. A **Web-based HMI** that allows images to be uploaded & drawn

The whiteboard was scavenged from McMaster's IEEE Student Branch
## Inspiration

Learning about some environmental impact of the retail industry led us to wonder about what companies have aimed for in terms of sustainability goals. The textile industry is notorious for its carbon and water footprints with statistics widely available. How does a company promote sustainability? Do people know and support about these movements?

With many movements by certain retail companies to have more sustainable clothes and supply-chain processes, we wanted people to know and support these sustainability movements, all through an interactive and fun UI :)

## What it does

We built an application to help users select suitable outfit pairings that meet environmental standards. The user is prompted to upload a picture of a piece of clothing they currently own. Based on this data, we generate potential outfit pairings from a database of environmentally friendly retailers. Users are shown prices, means of purchase, reasons the company is sustainable, as well as an environmental rating.

## How we built it

**Backend**: Google Vision API, MySQL, AWS, Python with Heroku and Flask deployment

Using the Google Vision API, we learn of the features (labels, company, type of clothes and colour) from pictures of clothes. With these features, we use Python to interact with our MySQL database of clothes to both select a recommended outfit and additional recommended clothes for other potential outfit combinations.

To generate more accurate label results, we additionally perform a Keras (with Tensorflow backend) image segmentation to crop out the background, allowing the Google Vision API to extract more accurate features.

**Frontend**: JavaScript, React, Firebase

We built the front-end with React, using Firebase to handle user authentications and act as a content delivery network.

## Challenges we ran into

The most challenging part of the project was learning to use the Google Vision API, and deploying the API on Heroku with all its dependencies.

## Accomplishments that we're proud of

Intuitive and clean UI for users that allows ease of mix and matching while raising awareness of sustainability within the retail industry, and of course, the integration and deployment of our technology stack.

## What we learned

After viewing some misfit outfit recommendations, such as a jacket with shorts, had we added a "seasonal" label, and furthermore a "dress code" label (by perhaps, integrating transfer learning to label the images), we could have given better outfit recommendations. This made us realize importance of brainstorming and planning.

## What's next for Fabrical

Deploy more sophisticated clothes matching algorithms, saving the user's outfits into a closet, in addition to recording the user's age, and their preferences as they like / dislike new outfit combinations; incorporate larger database, more metrics, and integrate the machine learning matching / cropping techniques.