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Emotion Reader

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What is Emotion Reader?

Emotion Reader is an application that takes photos and analyzes your face and outputs an emotion.

Get Started

Clone this repository to your local machine.

$ git clone

Once downloaded, change directory into the emotionreader directory.

$ cd emotion-reader

Begin a new virtual environment with Python 3 and activate it.

emotion-reader $ python3 -m venv ENV

emotion-reader $ source ENV/bin/activate

Install the application requirements with pip.

(ENV) emotion-reader $ pip install -r requirements.txt

Create a Postgres database for use with this application.

(ENV) emotion-reader $ createdb emotionreader

Export environmental variables pointing to the location of database, your username, hashed password, and secret.

(ENV) emotion-reader $ export SECRET_KEY='secret'

(ENV) emotion-reader $ export DB_NAME='emotionreader'

(ENV) emotion-reader $ export DB_USER='(your postgresql username)'

(ENV) emotion-reader $ export DB_PASS='(your postgresql password)'

(ENV) emotion-reader $ export DB_HOST='localhost'

(ENV) emotion-reader $ export DEBUG='True'

Then initialize the database with the migrate command from

(ENV) emotion-reader $ python emotionreader/ migrate

Once the package is installed and the database is created, start the server with the runserver command from

(ENV) emotion-reader $ python emotionreader/ runserver

Application is served on http://localhost:8000


You can test this application by first exporting an environmental variable pointing to the location of a testing database, then running the test command from

(ENV) emotion-reader $ export


(ENV) emotion-reader $ python

emotionreader/ test emotionreader

Influences and Attributions

Below are the libraries and technologies we used to make this project possible.




Get started with Django

pip install django==1.11

Django has three layers.

  • Model Layer

    • An abstraction layer where you can create your models
    from django.db import models
    class Person(models.Model):
        first_name = models.CharField(max_length=30)
        last_name = models.CharField(max_length=30)
  • View Layer

    • Where request and responses get handled.
    from django.conf.urls import url
    from . import views
    urlpatterns = [
        url(r'^articles/2003/$', views.special_case_2003),
        url(r'^articles/([0-9]{4})/$', views.year_archive),
        url(r'^articles/([0-9]{4})/([0-9]{2})/$', views.month_archive),
        url(r'^articles/([0-9]{4})/([0-9]{2})/([0-9]+)/$', views.article_detail),
  • Template Layer

    • How the information being passed gets served to the front end.
        TEMPLATES = [
            'BACKEND': 'django.template.backends.django.DjangoTemplates',
            'DIRS': [],
            'APP_DIRS': True,
            'OPTIONS': {
                # ... some options here ...

To dive deeper into the documentations for a more in depth idea of how we set everything up. Click Here


Microsoft API's

Microsoft has some pretty amazing API's in general that a user can hit, we were lucky enough to find two from Microsoft to use for our project.

Face API
  • Face API can detect up to 64 human faces then can be handle with the use of bytes or url.
  • Face API can compare 2 different faces and determine if they are the same person or not. This is what we used for Authentication and Authorization for our Face Login feature.
  • Face API has many more features. Check out the Face API and Face API Documenation.
Emotion API
  • Emotion API takes in an expression as an input, returns a bounded box using Face API, and returns a JSON object with 8 emotions.

Example of a single face.

    "faceRectangle": {
      "top": 114,
      "left": 212,
      "width": 65,
      "height": 65
    "scores": {
      "anger": 1.0570484E-08,
      "contempt": 1.52679547E-09,
      "disgust": 1.60232943E-07,
      "fear": 6.00660363E-12,
      "happiness": 0.9999998,
      "neutral": 9.449728E-09,
      "sadness": 1.23025981E-08,
      "surprise": 9.91396E-10

Check out Emotion API and Emotion API Documentation.



Amazon RDS

Relational Database Service

What is a Relational Database?

A relational database is a collection of items that hold data. These items have pre-defined relationships before they are put into a table with columns and rows to sort. The data holds information that relates to objects that are being represented. Each row and column has a unique identifier. We use this identifier to talk between pieces.

  • Amazon's version of a relational database in the cloud.
  • Simplicity and scalablity is always in mind when it comes to building out these apps. RDS gives us that.
  • We are able to have a built out front-end so we can manage and handle out databases when we need to but don't have to worry about maintaining them. They are automated.

Amazon S3

Simple Storage Service

  • S3 is a simple way to store data in the cloud and have the ability to access it any place and any time in the world.
  • We created a bucket(storage) and received a key that routes to that bucket. When ever we push data, it uses this key and pushes it to the bucket.
  • Using S3 allows our app to be smaller, compact, and more efficient.

Amazon EC2

Elastic Compute Cloud

  • EC2 is a simple way to compute data in the cloud.
  • EC2 is secure and resizeable.
  • Similar to S3 which setting up. Create an instance, set parameters, and get a key. Use that key to route information there, process it, and then your parameters push it somewhere else.

Amazon Route 53

Route 53

  • Cloud Domain Name System(DNS).
  • We are routing our EC2 instance to our domain.



  • Chart.js passes in data and organizes and displays the information int a chart.
  • These charts can vary in design and interaction.


  • Bootstrap is a design templating service.
  • Through the use of a design library with jQuery and SCSS as major players, we have an amazing design tool where we can plug and play chunks of code to have a polished front end.
  • Our specific bootstrap that we used was Light Bootsrap.



We used what Spotify calls "The Play Button". This is a widget where you can copy and paste in a playlist URI and spits out an iframe for you to use with your site.

Example of iframe:

<iframe src="" width="300" height="380" frameborder="0" allowtransparency="true"></iframe>

With the use of the iframe you can adjust the height, width, and border for the overall look.



  • Running Ansible Playbook

    ansible -playbook -i hosts playbooks/emotion_playbook.yml
  • Ansible automates tasks. Some of these tasks are:

    • Installs all the applications
    • Clones repo to EC2 instance
    • Creates all necessary files needed for the app


Emotion Reader is offered under the MIT license and shown in the LICENSE file.`