Code_G_Final_Project

This was developed with an Anaconda installation of Python 3.7

Installation Steps:

1. OPTIONAL create and activate a new virtual environment

   With Anaconda:

   conda create -n ve python=3.7
   conda activate ve

2. Install requirements. As some of the dependencies are not available in the conda repo, we use pip to install all libraries.

pip install -r requirements.txt

3. Run the FLASK application

   Change directory to Code_G_Final_Project. For debugging, you can add export FLASK_DEBUG=1

Code_G_Final_Project$ export FLASK_APP=server.py
Code_G_Final_Project$ flask run

Scripts

To generate cleaned employee_reviews.csv for NLP:

Code_G_Final_Project/reviews_app/model$ python parse_kaggle.py

This generates a file called employee_reviews.cleaned.csv

To generate word cloud images from employee_reviews

Note: you will need to use pythonw instead of python for Anaconda installations of python.

To install pythonw in Anaconda environment, run conda install python.app

Code_G_Final_Project/reviews_app/model$ pythonw review_wordcloud.py

This generates a 1000 x 1000 word cloud based on the summary, pros, and cons reviews for each company in the dataset. Word cloud image files are downloaded to reviews_app/model/images

Testing

We have set up some unit tests with the pytest library

To run the tests you will have to install the test requirements:

pip install -r test_requirements.txt

Then to run these tests, go to the top level directory of the project then issue the following command:

python -m pytest .

Writing a test

Pytest will discover test cases by first looking for files in the tests/ directory then looking within this directory for test files that start with the prefix test_.

All functions that start with test will then be run by the Pytest runner. (for example test_homepage function in tests/test_homepage.py will be automatically run.`