/ExpertSystem

Primary LanguagePython

Expert System

REPO-TYPE

The Expert System is a set of tools that allow the end-user to execute dynamically created processes and potentially unlimited workflows.

The Expert System is executed on a closed dockerized environment (i.e., uses the Docker container platform) and uses the Python programming language and the FastAPI web framework. The Expert system contains various tools and libraries based on python packages and, that can manage workflows, workflow designer tools to create BPMN dynamic diagrams, and other components implemented via JSON standards.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

The requirements to run, test, and develop the Expert System are:

Installing

There are two different ways to install the application. If you want to develop some new features, or fix some bugs, it is highly recommended to use a python environment first and then build and test with a docker image.

But wait! Before doing anything, create an environment file based on the example and edit the appropriate variables.

cp .env.example .env
nano .env

Python-based

First, be sure to install Python and activate version 3.10 and Pipenv:

sudo apt update
sudo apt install python3.10 python3.10-venv python3.10-dev

# If necessary, run the line bellow
sudo apt build-dep python3.10 python3-tk

Then check the version to be at 3.10 or greater.

python --version

Now it's time to install Pipenv:

pip install pipenv

And check if everything works as expected, by executing the following command:

pipenv --version

To start developing, you first instantiate the environment, install the dependencies and start the server:

pipenv --python 3.10
pipenv install
pipenv run uvicorn src.main:main --host 0.0.0.0 --port 80 --reload

Docker-based

According to the documentation of sail, first, add the following line in the ~/.bashrc file, close the terminal and open it again.

alias sail='[ -f sail ] && bash sail'

Run the following command to create and run the project's docker images. This assumes that both Docker and docker-compose are installed.

sail up -d

Running the tests

There are two possible ways to execute the tests. The first one is directly through the virtual environment and the second via the sail command.

pipenv run pytest

or

sail up -d
sail pytest

Break down into end to end tests

Explain what these tests test and why

Give an example

And coding style tests

To validate the coding style and be consistent, we use Black. Run the following command to format your code:

pipenv run black

Built With

To build the docker image of this repo, run the following command:

sail build

Run Database Migrations

All database changes are monitored by a python module called "Alembic". All changes in the "./src/models" folder will result in a revision of the database model by typing the following:

sail alembic revision --autogenerate -m "Type message here."

Then sync the changes with the connected database with the command below:

sail alembic upgrade head

Production build and versioning

For a production build, create a starter .env from the example and run the previous command with cache disabled:

cp .env.example .env
sail build --no-cache

After the build is complete, tag the docker image with the desired version and push it to the registry.

docker tag username/exampleimage username/exampleimage:v1.0.0
docker push username/exampleimage:v1.0.0

Deployment

To deploy the application in a staging or production environment, you pull the version you want from your docker registry and use it accordingly. Keep a note that every time you make a version update, you have to run the migration script to update the database up to date.

🚨 In order to start the app you need to generate a cryptographic key, by executing the command below in a python3 environment and pasting it in the appropriate env variable:

from cryptography.fernet import Fernet
print(Fernet.generate_key().decode("utf-8"))

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments