Build a language translation app

Table of contents

Overview

This guide walks you through building and running a language translation application. You'll build the application using Python with Googletrans, and then set up the environment and run the application using Docker.

The application demonstrates a simple but practical use of the Googletrans library for language translation, showcasing basic Python and Docker concepts. Googletrans is a free and unlimited Python library that implements the Google Translate API. It uses the Google Translate Ajax API to make calls to such methods as detect and translate.

Prerequisites

  • You have installed the latest version of Docker Desktop. Docker adds new features regularly and some parts of this guide may work only with the latest version of Docker Desktop.
  • You have a Git client. The examples in this section use a command-line based Git client, but you can use any client.

Get the sample application

  1. Open a terminal, and clone the sample application's repository using the following command.

    $ git clone https://github.com/harsh4870/Docker-NLP.git

  2. Verify that you cloned the repository.

    You should see the following files in your Docker-NLP directory.

    01_sentiment_analysis.py
02_name_entity_recognition.py
03_text_classification.py
04_text_summarization.py
05_language_translation.py
entrypoint.sh
requirements.txt
Dockerfile
README.md

Explore the application code

The source code for the application is in the Docker-NLP/05_language_translation.py file. Open 05_language_translation.py in a text or code editor to explore its contents in the following steps.

  1. Import the required libraries.

    from googletrans import Translator

    This line imports the Translator class from googletrans. Googletrans is a Python library that provides an interface to Google Translate's AJAX API.

  2. Specify the main execution block.

    if __name__ == "__main__":

    This Python idiom ensures that the following code block runs only if this script is the main program. It provides flexibility, allowing the script to function both as a standalone program and as an imported module.

  3. Create an infinite loop for continuous input.

       while True:
      input_text = input("Enter the text for translation (type 'exit' to end): ")

      if input_text.lower() == 'exit':
         print("Exiting...")
         break

    An infinite loop is established here to continuously prompt you for text input, ensuring interactivity. The loop breaks when you type exit, allowing you to control the application flow effectively.

  4. Create an instance of Translator.

          translator = Translator()

    This creates an instance of the Translator class, which performs the translation.

  5. Translate text.

          translated_text = translator.translate(input_text, dest='fr').text

    Here, the translator.translate method is called with the user input. The dest='fr' argument specifies that the destination language for translation is French. The .text attribute gets the translated string. For more details about the available language codes, see the Googletrans docs.

  6. Print the original and translated text.

          print(f"Original Text: {input_text}")
      print(f"Translated Text: {translated_text}")

    These two lines print the original text entered by the user and the translated text.

  7. Create requirements.txt. The sample application already contains the requirements.txt file to specify the necessary modules that the application imports. Open requirements.txt in a code or text editor to explore its contents.

    ...

# 05 language_translation
googletrans==4.0.0-rc1

    Only googletrans is required for the language translation application.

Explore the application environment

You'll use Docker to run the application in a container. Docker lets you containerize the application, providing a consistent and isolated environment for running it. This means the application will operate as intended within its Docker container, regardless of the underlying system differences.

To run the application in a container, a Dockerfile is required. A Dockerfile is a text document that contains all the commands you would call on the command line to assemble an image. An image is a read-only template with instructions for creating a Docker container.

The sample application already contains a Dockerfile. Open the Dockerfile in a code or text editor to explore its contents.

The following steps explain each part of the Dockerfile. For more details, see the Dockerfile reference.

  1. Specify the base image.

    FROM python:3.8-slim

    This command sets the foundation for the build. python:3.8-slim is a lightweight version of the Python 3.8 image, optimized for size and speed. Using this slim image reduces the overall size of your Docker image, leading to quicker downloads and less surface area for security vulnerabilities. This is particularly useful for a Python-based application where you might not need the full standard Python image.

  2. Set the working directory.

    WORKDIR /app

    WORKDIR sets the current working directory within the Docker image. By setting it to /app, you ensure that all subsequent commands in the Dockerfile (like COPY and RUN) are executed in this directory. This also helps in organizing your Docker image, as all application-related files are contained in a specific directory.

  3. Copy the requirements file into the image.

    COPY requirements.txt /app

    The COPY command transfers the requirements.txt file from your local machine into the Docker image. This file lists all Python dependencies required by the application. Copying it into the container lets the next command (RUN pip install) install these dependencies inside the image environment.

  4. Install the Python dependencies in the image.

    RUN pip install --no-cache-dir -r requirements.txt

    This line uses pip, Python's package installer, to install the packages listed in requirements.txt. The --no-cache-dir option disables the cache, which reduces the size of the Docker image by not storing the unnecessary cache data.

  5. Run additional commands.

    RUN python -m spacy download en_core_web_sm

    This step is specific to NLP applications that require the spaCy library. It downloads the en_core_web_sm model, which is a small English language model for spaCy. While not needed for this app, it's included for compatibility with other NLP applications that might use this Dockerfile.

  6. Copy the application code into the image.

    COPY *.py /app
COPY entrypoint.sh /app

    These commands copy your Python scripts and the entrypoint.sh script into the image's /app directory. This is crucial because the container needs these scripts to run the application. The entrypoint.sh script is particularly important as it dictates how the application starts inside the container.

  7. Set permissions for the entrypoint.sh script.

    RUN chmod +x /app/entrypoint.sh

    This command modifies the file permissions of entrypoint.sh, making it executable. This step is necessary to ensure that the Docker container can run this script to start the application.

  8. Set the entry point.

    ENTRYPOINT ["/app/entrypoint.sh"]

    The ENTRYPOINT instruction configures the container to run entrypoint.sh as its default executable. This means that when the container starts, it automatically executes the script.

    You can explore the entrypoint.sh script by opening it in a code or text editor. As the sample contains several applications, the script lets you specify which application to run when the container starts.

Run the application

To run the application using Docker:

  1. Build the image.

    In a terminal, run the following command inside the directory of where the Dockerfile is located.

    $ docker build -t basic-nlp .

    The following is a break down of the command:

    • docker build: This is the primary command used to build a Docker image from a Dockerfile and a context. The context is typically a set of files at a specified location, often the directory containing the Dockerfile.
    • -t basic-nlp: This is an option for tagging the image. The -t flag stands for tag. It assigns a name to the image, which in this case is basic-nlp. Tags are a convenient way to reference images later, especially when pushing them to a registry or running containers.
    • .: This is the last part of the command and specifies the build context. The period (.) denotes the current directory. Docker will look for a Dockerfile in this directory. The build context (the current directory, in this case) is sent to the Docker daemon to enable the build. It includes all the files and subdirectories in the specified directory.

    For more details, see the docker build CLI reference.

    Docker outputs several logs to your console as it builds the image. You'll see it download and install the dependencies. Depending on your network connection, this may take several minutes. Docker does have a caching feature, so subsequent builds can be faster. The console will return to the prompt when it's complete.

  2. Run the image as a container.

    In a terminal, run the following command.

    $ docker run -it basic-nlp 05_language_translation.py

    The following is a break down of the command:

    • docker run: This is the primary command used to run a new container from a Docker image.
    • -it: This is a combination of two options:
      • -i or --interactive: This keeps the standard input (STDIN) open even if not attached. It lets the container remain running in the foreground and be interactive.
      • -t or --tty: This allocates a pseudo-TTY, essentially simulating a terminal, like a command prompt or a shell. It's what lets you interact with the application inside the container.
    • basic-nlp: This specifies the name of the Docker image to use for creating the container. In this case, it's the image named basic-nlp that you created with the docker build command.
    • 05_language_translation.py: This is the script you want to run inside the Docker container. It gets passed to the entrypoint.sh script, which runs it when the container starts.

    For more details, see the docker run CLI reference.

    Note

    For Windows users, you may get an error when running the container. Verify that the line endings in the entrypoint.sh are LF (\n) and not CRLF (\r\n), then rebuild the image. For more details, see Avoid unexpected syntax errors, use Unix style line endings for files in containers.

    You will see the following in your console after the container starts.

    Enter the text for translation (type 'exit' to end):

  3. Test the application.

    Enter some text to get the text summarization.

    Enter the text for translation (type 'exit' to end): Hello, how are you doing?
Original Text: Hello, how are you doing?
Translated Text: Bonjour comment allez-vous?

Summary

In this guide, you learned how to build and run a language translation application. You learned how to build the application using Python with Googletrans, and then set up the environment and run the application using Docker.

Related information:

Next steps

Explore more natural language processing guides.