Docker Hashtagger

This repository is a dockerised image of a Python file hashtagger, based on Linux Ubuntu. The image is hosted on the Docker Hub and can be found here.

Technologies

Below are the main technologies used for this project. Take some time to familiarise yourself.

Natural Language Toolkit: an open source platform for analysing human language data with Python.

Apache Tika: a toolkit that detects and extracts metadata and text from over a thousand different file types.

How to run

First, you need to install and configure Docker on your system following this installation guide.

Once Docker is successfully installed and configured on your system (you should be able to run $ docker run hello-world), you are ready to download a copy of this docker image.

Clone project

Choose a directory for this project and clone the github repo:

$ git clone https://github.com/nichelia/docker-hashtagger.git

Downlaod local copy of docker image

Download the Docker image by using the following (replace :version with desired tag or empty for latest).

$ docker pull nichelia/hashtagger[:version]

According the docker-compose file, the image can be run as an interactive-shell for nltk

docker-compose.yml

nltk:
  image: nichelia/hashtagger
  command: bash
  volumes:
    - .:/code
  working_dir: /code
  restart: always

Run python script

First, make sure you are in the same directory as the cloned git project. Then, run the docker image as an interactive-shell (provides an environment with all the packages required for you to run the python script).

$ cd [path_to_cloned_project]
$ docker-compose run --rm nltk

Before running the Python script, you need to run Apache Tika REST API service first in the background. It will be used in the Python script for any file types other than text files [.txt]. To do so, run the following:

$ java -jar /tika-server-1.15.jar -h 0.0.0.0 &

Now you are ready to run the python script. It takes as a first and only parameter the directory of the files to read from. The following command assumes you want to run it against some sample documents and output the results in a text file, out.txt.

$ python hashtagger/hashtagger.py  hashtagger/templates > out.txt

Clean-up

Current project will use Docker containers, images and volumes. Check out this handy cheat sheet on how to clean-up after you finished.