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This repo is supposed to get statistics from Uber, Bold, Uklon to calculate performance of car cross this aggregators for fleet owners.

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Universal Auto

This repo is supposed to get statistics from Uber, Bold, Uklon to calculate performance of car cross this aggregators for fleet owners and provide reports via telegram bit for Drivers, Fleet Managers and Fleet Owners.

How to run a project on your local machine?

  1. Install Docker https://docs.docker.com/engine/install/
  2. Create a telegram bot using https://t.me/BotFather and get TELEGRAM_TOKEN
  3. Rename docker-compose_example.yml to docker-compose.yml
  4. Replace with token given by telegram
  5. Run docker-compose up --build pgadmin
  6. Open http://localhost:5050/browser/ with password: universal_auto_dev and create DB universal_auto_dev
  7. Run docker-compose up --build
  8. Run migrations by docker exec -it universal_auto_web_1 python manage.py migrate
  9. Run to create admin user docker exec -it universal_auto_web_1 python manage.py createsuperuser
  10. Open http://localhost:8080/admin/ in browser and auth with user created at step 9

How to run report and see results in console?

docker exec -it universal_auto_web_1 python manage.py runscript weekly

How to start contribute?

  1. Take an issue from the list https://github.com/SergeyKutsko/universal_auto/issues and ask questions
  2. Fork project and create a new branch from a master branch with the name in the format: issues-12-your_last_name
  3. After work is finished and covered by tests create a Pull Request with good description what exactly you did and how and add Sergey Kutsko as reviewer.
  4. After review fix found problems
  5. Manual QA stage need to be done by other person to confirm solutions works as expected
  6. We will deploy to staging server to confirm it works in pre-pod ENV
  7. Merge into master and deploy to production instance.

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This repo is supposed to get statistics from Uber, Bold, Uklon to calculate performance of car cross this aggregators for fleet owners.

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  • Python 81.3%
  • PowerShell 13.0%
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