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DEVELOPMENT.md

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Running the Stack Analyzer locally

Setting Up the Project locally

Prerequisites

  • Python 3.11 or newer
  • pip and pipenv

Installation

  1. Clone the Project

First, clone the repository to your local machine:

git clone https://github.com/warestack/stack-analyzer.git
cd stack-analyzer
  1. Set up your virtual environment

Use pipenv to create a virtual environment and install dependencies:

pipenv install

This command reads the Pipfile in the project root and installs all necessary packages, creating a virtual environment.

Running the Project

  1. Activate the Virtual Environment

Activate the pipenv-created virtual environment:

pipenv shell
  1. Setting up your PAT token

The Stack Analyzer utilises the github API to analyze a repository's structure, so you'd need to create a .env file on the project root directory with the following content:

GITHUB_PAT=your_pat_token
  1. Start the Flask Application

The repository includes a flask application for testing purposes. This means you can send curl commands to test various repositories while you develop more analyzers.

Run the Flask web service:

flask run

Alternatively, you can run the application on debug (and live reload) mode:

flask --app app.py --debug run

Testing the Service

To test the tech stack analyzer service, you can use curl to send a POST request to the /analyze endpoint. Replace http://127.0.0.1:5000 with the appropriate URL if you've configured the service to run elsewhere.

curl -X POST http://127.0.0.1:5000/analyze -H "Content-Type: application/json" -d "{\"repo_url\":\"https://github.com/example/repo\"}"

This command simulates sending a repository URL to the analyzer. Given the project's current setup, it will return a JSON response based on the hardcoded analysis within the Flask application.

Unit tests

To run the unit tests (included within tests), you can use the following command while being at the project root directory:

python -m unittest discover tests

Or if you want to run a specific unit test you could do so as such:

python -m unittest tests/test_analyze_node.py

Updating the library

Once we some changes that we want to publish to PyPI (or test.PyPi), we can build a new library version as such:

python setup.py sdist bdist_wheel # build a new version
twine upload --repository testpypi dist/* # publish to test PyPI