Throwing Shade is a foundation matching application that allows users to upload a photo and then recommends foundation products for the user's skin tone. Users are able to leave reviews for each brand, see each brand's latest media posts from Twitter, as well as favorite their preferred products.

I implemented k-means clustering in order to find the dominant color for each foundation product and the dominant color for the user's face. Foundation recommendations are based on the closest color distance between the user's skin color and each foundation shade. In the future, I hope to incorporate white balance and exposure adjustment--as well as convert the hex codes to lab space-- for more accurate recommendations.

Scikit Image, Flask, Python, PostgreSQL, SqlAlchemy, PIL(Python Image Library), Ajax, Jinja , Bootstrap, HTML, CSS, unittest, SciPy

APIs Used: Twitter, Sephora XHR

Clone repository:

$ git clone https://github.com/shirleyilenechan/Foundation_Project

Create a virtual environment:

$ virtualenv env

Activate the virtual environment:

$ source env/bin/activate

Install dependencies:

$ pip install -r requirements.txt

Get your own secret keys for Twitter. Save them to a file secrets.py. Your file should look something like this:

export TWITTER_CONSUMER_KEY="abc123"
export TWITTER_CONSUMER_SECRET="abc123"
export TWITTER_ACCESS_TOKEN_KEY="abc123"
export TWITTER_ACCESS_TOKEN_SECRET="abc123"

Run Model.py

$ python3 model.py

Run Seed.py to create the tables

$ python3 seed.py

Seed the tables with Foundation information

$ psql foundation_project < foundation_project.sql

Source Secrets File

$ source secrets.sh

Run server.py

$ python3 server.py