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Sága

Video Analytics Made Easy

Sága purports to provide content creators with an in-depth reporting tool on how their product is received by their viewers. Sága targets specifically online video content creators (e.g YouTubers) and features visual representation of the average response from the audience.

To that end, and with the viewer's authorization, Sága captures emotional response to the content, and stores that data on the server, who then renders it to the content creator’s page.

The goal is to allow creators to have faster access to more accurate and better organized data on their viewers’ preferences, allowing them to better cater to said preferences.

Sága is a capstone project for Lighthouse Labs by Corrina Chow, Kaan Yalti, and Steve Abouem

The project was split as follows:

Fully collaborative effort in initial database planning (ERD), wireframing and design. Corrina focused primarily on the front-end logic and handling the video reactions logic on the back-end. She set up the all front-end routes, views & navigation, configured OAuth2 to work with the YouTube API to receive the user's videos on login/registration, made initial calls to Kairos, and Ziggeo, manipulated the emotion analysis data in the server, as well as parsing the data to create the Overall Reactions donut chart. She also created the mockups and leveraged the capabilities of Bootstrap 4 to produce fully responsive Home, Login, and Admin Dashboard pages.

Steve focused on setting up CanvasJS, retrieve the data from the server and filter it to render the time stamped emotion data. He also worked on the initial style for the Home, Admin and Dashboard pages, as well as created components for front end animaitons and transitions ont the login and home pages. Like the other members he participated in keeping our Trello up to data.

Kaan integrated the Kairos API, youtube Iframe API and the Ziggeo javascript recorder. Created the public video page where the viewers' reactions are recorded. Synchronized the video play and recording to collect accurate data. Setup the logic in the server to make calls to the Kairos API and did prelimenary data cleaning upon receiving the analysed data. Collaborated on the boilerplate. Worked on the syling of the public video page and the video details page.

Final Product

Sága Homepage Sága Homepage

Saga Login Sága Login

Saga Analytics Sága Overall and Filtered Analytics

Setup

  1. Clone this repository
  2. Run bundle install to install dependencies in the root directory
  3. Run npm install in the app/client directory
  4. Create a local_env.yml using local_env.example.yml as a template
  5. Run bin/rake db:reset in the root directory
  6. Sign up for a Kairos API key
  7. Sign up for a Ziggeo API key
  8. Add the Kairos App ID and Ziggeo API keys into the appropriate local_env.yml variables
  9. Obtain OAuth 2.0 client credientials from the Google API Console
  10. Obtain a YouTube API key
  11. Add the Ziggeo, OAuth2, and YouTube API keys into the appropriate env.js variables
  12. Run npm start in app/client to run the front-end server
  13. Run bin/rails s -b 0.0.0.0 -p 3001 to run the back-end server

React Server Dependencies

  • axios
  • canvasjs
  • lottie-web
  • moment
  • pg
  • react
  • react-css-transition
  • react-dom
  • react-google-login
  • react-moment
  • react-router-dom
  • react-scripts
  • react-ziggeo
  • reactstrap

Rails Server Dependencies

  • byebug
  • faker
  • rails
  • pg
  • puma
  • httparty
  • listen
  • spring
  • spring-watcher-listen

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