Prometeo rules and decision engine

This repository contains the Prometeo solution application for determining thresholds in toxin exposure measured by the sensor device and sent to the cloud from the Samsung smartphone carried by the firefighters.

This service wakes up every minute and calculates time weighted average exposures for all fire fighters and compares them to the configured limits.

Contents

• Prometeo rules and decision engine
  • Contents
  • Prerequisites
  • Run locally with Python
  • Run locally with Docker
  • Run on Kubernetes
  • Troubleshooting
  • Built with
  • Contributing
  • License

Prerequisites

1. Docker
2. IBM CLI
3. Kubectl
4. Helm
5. Skaffold

Run locally with Python

You can run this solution locally in docker as follows

1. Set up environment variables in the src/.env file
2. Install mariadb locally
   1. pull mariadb from dockerhub

       docker pull mariadb
   

   2. run the image

       docker run -p 3306:3306 --name prometeo-mariadb -e MYSQL_ROOT_PASSWORD='' -d mariadb
   

   3. Test the image - TBD
3. Create python virtual environment

        python3 -m venv python3
   

4. Activate virtual environment

        source python3/bin/activate
   

5. Run the application

        python src/core_decision_flask_app.py 8080
   

6. You should see the following output

        starting application
        * Serving Flask app "core_decision_flask_app" (lazy loading)
        * Environment: production
        WARNING: Do not use the development server in a production environment.
        Use a production WSGI server instead.
        * Debug mode: off
   

• Running on http://0.0.0.0:8080/ (Press CTRL+C to quit)

Run locally with Docker

1. Build the image

       docker build . -t rulesdecision
   

2. Run the image

        docker run -p8080:8080 -t rulesdecision
   

3. You should see the application logs

        starting application
        * Serving Flask app "core_decision_flask_app" (lazy loading)
        * Environment: production
        WARNING: Do not use the development server in a production environment.
        Use a production WSGI server instead.
        * Debug mode: off
        * Running on http://0.0.0.0:8080/ (Press CTRL+C to quit)
   

Run on Kubernetes

You can run this application on Kubernetes. The skaffold.yaml file let's you quickly run the application on the cluster by using Skaffold. There are two profiles provided. To run the solution on the test namespace use: skaffold dev -p test

Troubleshooting

1. Database does not connect
   1. ensure .env file has the correct values for database connection
2. Change the db password

Built with

• Docker
• MariaDB
• Flask
• IBM Kubernetes Service

Contributing

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

License

This project is licensed under the Apache 2 License - see the LICENSE file for details.