jaunesarmiento / loki

Proof-of-concept of emotion-targeted content delivery using machine learning and ARKit.

Loki presents a news feed to the user much like other popular social networking apps. However, in the background, it uses iOS' ARKit to gather the user's facial data. This data is piped through a neural network model we trained to map facial data to emotions. We use the currently-detected emotion to modify the type of content that gets loaded into the news feed.

We were inspired to build Loki to illustrate the plausibility of social media platforms tracking user emotions to manipulate the content that gets shown to them.

Loki was a hackathon project created by Lansi Chu, Kevin Yap, Nathan Tannar, and Patrick Huber during nwHacks 2018.

For more info, please see the Medium post here

Demos and Screenshots

Usage

Running the backend server requires Python 2 and Postgres 9.4+. The backend expects a local Postgres database called loki; otherwise, the URL to a remote database instance must be provided via an environment variable.

$ export DATABASE_URL="postgresql://user:pass@host:port/dbname"
$ cd flask-backend
$ pip install -r requirements.txt
$ python app.py

Tech Stack

Credits

Logo courtesy of Casmic Lab.