Perfect Memory Training

This project is part of the perfect memory developer training.

This project parsers the data from a csv file, transform it into a rdfs graph and send to the exchange manager api to insert in the knowledge base. It uses an existing process called insertGraph to make this operation.

Setup

To set up the project, follow these steps:

Step 1

First of all, clone this project in your local machine.

$ git clone https://github.com/JuninhoCarlos/perfect-memory-training.git
$ cd perfect-memory-training

Step 2

Create a virtual environment and activate it, and install the dependencies.

$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

To install developer dependencies like pylint, black formatter and ipdb debugger uses de requirements-dev.txt file

Step 3

Create a .env file in /lib folder. There is a env.sample file in the /lib folder that you can fill with your own parameters and credentials.

$ cat lib/env.sample > lib/.env

The variable are the following:

• EM_BASE_URL : base url from the exchange manager api
• EM_API_KEY : api key from the exchange manager api
• EM_CLIENT_NAME : name of the exchange manager client

Running the application

To run the application execute:

$ python3 lib/main.py -p <path_to_your_csv>

You can use the csv that is in the repo and run python3 lib/main.py -p pizzas.csv

Running the tests and check coverage

To run the unit test execute the following command:

$ pytest

To run test and check coverage analisys you can run the following commands:

$ coverage run -m pytest
$ coverage report