DataAnalyticsService

A RESTful API for recommending movies to users

Description

This service provides a fast, reliable means for users to find movies they may be interested in watching. The service provides a RESTful API for movie recommendation, as well as a client program for ease of user browsing. The recommender system is build on Python 3 using machine learning algorithms provided by the Scikit-learn (http://scikit-learn.org/stable/) library.

Getting Started

Prerequisites

It is assumed that:

• Python 3.6.5 or above is installed (https://www.python.org/downloads/)
• Heroku CLI 7.16.8 is installed (https://devcenter.heroku.com/articles/heroku-cli#download-and-install)

Installation

pip3 install -r requirements.txt

Usage

Refer to README.md files in the individual project directories for details

Deployment

The API and client application are deployed in separate instances on the Heroku cloud platform as a service:

• API is hosted at: https://movie-recommender-api.herokuapp.com/
• Client is hosted at: https://movie-recommender-app.herokuapp.com/

Heroku Configuration

# Login to Heroku CLI
heroku login

# Ensure you are listed as a collaborator:
# https://dashboard.heroku.com/apps/movie-recommender-api/access
# https://dashboard.heroku.com/apps/movie-recommender-app/access

# Add the Heroku remotes
git remote add heroku-api https://git.heroku.com/movie-recommender-api.git
git remote add heroku-app https://git.heroku.com/movie-recommender-app.git

# Set the private key for use by the API
heroku config:set PRIVATE_KEY=`python3 api/generate_private_key.py /tmp/.private_key` --remote heroku-api

# Set environment variables for the type of deploy
heroku config:set DEPLOY=api --remote heroku-api
heroku config:set DEPLOY=app --remote heroku-app

Deploy API/Client

# Set environment variable to be the API Heroku remote
export REMOTE=heroku-api

# Set environment variable to be the client Heroku remote
export REMOTE=heroku-app

# Deploy from master branch
git push $REMOTE master

# Deploy from branch 'foo'
git push $REMOTE foo:master

# Scale the deploy to use one dyno
heroku ps:scale web=1 --remote $REMOTE

# Restart the server if needed
heroku restart --remote $REMOTE

# View the server logs
heroku logs --tail --remote $REMOTE

# View the server environment variables
heroku config --remote $REMOTE

Project Structure

• ml/ - backend Machine Learning algorithms consumed by API
• api/ - RESTful API based on Flask-RESTPlus
• client/ - client GUI for the API built on Flask and Materialize
• data/ - datasets used by ML algorithms

Contributing

1. Clone the repository (git clone git@github.com:costaparas/DataAnalyticsService.git)
2. Create a new feature branch (git checkout -b foobar-feature)
3. Commit new changes (git commit -a -m 'add foobar')
4. Push to the branch (git push origin foobar-feature)
5. Create a new Pull Request (https://github.com/costaparas/DataAnalyticsService/pulls)
6. Merge the Pull Request once it is approved by at least one other contributor

License

Copyright (C) 2018 Benjamin Liew, Costa Paraskevopoulos, Dankoon Yoo, Saffat Akanda, Sharon Park

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.