torrent-name-analyzer

A REST api to extract all possible media information from a torrent filename.

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

You also need python >= 3.6 up and running. If you OS does not have the appropriate python version, you could install pyenv and create a virtual environment with the proper python version. Also you will need an up to date pip installation (version 20.0.2 or greater is our recommendation). So once you have pyenv installed (see pyenv install instructions), make an virtual environment for the project (we will use python version 3.8):

pyenv virtualenv 3.8.1 tna-venv

Enter inside the python environment we recently created (tna-venv):

pyenv activate tna-venv

Upgrade pip package:

pip install --upgrade pip

Install poetry package:

pip install poetry

Installing

Once you have the prerequisites installed, you can proceed installing the project. The project uses an pyproject.toml file to manage the installation (PEP517) and also we will make use of the python package poetry as our build-system (PEP518). So, to do the install you only need to cd to the project folder:

cd torrent-name-analyzer

And run the install of the dependencies via poetry command:

poetry install

Running the tests

To run our project tests you can use pytest with coverage:

BOILERPLATE_ENV=test PYTHONPATH=. pytest tests/ --cov torrent_name_analyzer/

Docker

This project can be used via docker, the following sections describes the build/run instructions.

Build image

You can build the docker image with the command:

docker build -t torrent-name-analyzer:latest .

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TIP

To update requirements.txt file you can use:

poetry export -f requirements.txt -o requirements.txt

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Run image

To run the image without persistent database changes, use command:

docker run --rm torrent-name-analyzer

To run the image with persistent database changes, you can mount a volume inside docker image pointing to host's database folder with command:

docker run --rm -v <absolute-path-to-db-data>:/app/torrent_name_analyzer/db-data torrent-name-analyzer

Deploy Application Locally

You can deploy application locally using multiple configurations, but if you want the production mode, we recommend to use a wsgi server, we make use of gunicorn for such purpose, so to run it from the root of the project use:

BOILERPLATE_ENV=prod gunicorn --bind 0.0.0.0:5000 torrent_name_analyzer.wsgi:app

For developing purposes (don't recommend this method in a production environment), you can use the flask wsgi integrated server:

PYTHONPATH=. ./torrent_name_analyzer/app.py

Just noting that if you don't set BOILERPLATE_ENV environment variable it will default to dev.

Here you have all supported configurations:

• dev: To be used when developing
• test: to perform the tests
• prod: to use in a production environment

List of endpoints:

Endpoint to get the web site:

http://localhost:5000/

Endpoint to get the json response for all stored torrents:

http://localhost:5000/v1/torrents

Endpoint to to the swagger documentation, where you can check all the supported endpoints:

http://localhost:5000/v1/ui/

Deploy Application on Heroku

First of all we will use some services:

• Install heroku app, after creating an heroku account:

  curl https://cli-assets.heroku.com/install.sh | sh
  

Under construction

Built With

• Python 3 - The programming language
• Flask - A lightweight WSGI web application framework
• Connexion - Framework on top of Flask that automagically handles HTTP requests defined using OpenAPI
• Gunicorn - A Python WSGI HTTP Server for UNIX
• Poetry - Dependency Management

Contributing

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

Versioning

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

Authors

• Pol Canelles - API work and updates on top of Giorgio's work - opacam
• Giorgio Momigliano - Updates on top of Roi's work - platelminto
• Roi Dayan - Updates on top of Divij's work - roidayan
• Divij Bindlish - Initial work made with python - divijbindlish
• Jānis - Original idea made with javascript - jzjzjzj

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

• Template for README.md