Recommendations Service

This repo contains details of our Recommendations Service. The resource model has no persistence to keep the application simple. It's purpose is to show the correct API and return codes that should be used for a Recommendations Service API.

Prerequisite Installation using Vagrant

The easiest way to use this service is with Vagrant and VirtualBox. if you don't have this software the first step is to download and install it.

Download VirtualBox

Download Vagrant

Clone the project to your development folder and create your Vagrant VM

$ git clone https://github.com/NYU-DevOps-S18-Recommendations/recommendations.git
$ cd recommendations
$ vagrant up

Once the VM is up you can use it with:

$ vagrant ssh
$ cd /vagrant
$ python service.py

When you are done, you can use Ctrl+C to stop the server and then exit and shut down the vm with:

$ exit
$ vagrant halt

If the VM is no longer needed you can remove it with:

$ vagrant destroy

Manually running the Tests

Run the tests using nosetests and coverage

$ nosetests
$ coverage report -m --include=service.py

API Calls with specified inputs available within this service

GET  /recommendations - Retrieves a list of recommendations from the database
GET  /recommendations/{id} - Retrieves a recommendation with a specific id
POST /recommendations - Creates a recommendation in the datbase from the posted database
PUT  /recommendations/{id} - Updates a recommendation in the database from the posted database
PUT  /recommendations/{id}/likes - Updates the count of likes for a given product id from the posted database
DELETE /recommendations{id} - Removes a recommendation from the database that matches the id

Valid content description of JSON file

{
  "id": <int> #the id of a recommendation 
  "product_id": <int> #the id of the product in the recommendation 
  "recommended_product_id": <int> #the id of the product that's being recommended with a given product
  "recommendation_type": <string> #describes the type of recommendation(Up-sell, Cross-Sell, Accessory)
  "likes" : <int> #a count of the number of people who like the recommendation 
}

What's featured in the project?

* service.py -- the main Recommendation Service using Python Flask
* models.py -- the data model using in-memory model
* tests/test_service.py -- test cases against the service
* tests/test_models.py -- test cases against the Recommendation model