Build a text summarization app
In this guide, you'll learn how to build and run a text summarization application. You'll build the application using Python with the Bert Extractive Summarizer, and then set up the environment and run the application using Docker.
The sample text summarization application uses the Bert Extractive Summarizer. This tool utilizes the HuggingFace Pytorch transformers library to run extractive summarizations. This works by first embedding the sentences, then running a clustering algorithm, finding the sentences that are closest to the cluster's centroids.
- 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.
Open a terminal, and clone the sample application's repository using the following command.
$ git clone https://github.com/harsh4870/Docker-NLP.git
Verify that you cloned the repository.
You should see the following files in your
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
The source code for the text summarization application is in the
Docker-NLP/04_text_summarization.py file. Open
04_text_summarization.py in a text or code editor to explore its contents in the following steps.
Import the required libraries.
from summarizer import Summarizer
This line of code imports the
Summarizerclass from the
summarizerpackage, essential for your text summarization application. The summarizer module implements the Bert Extractive Summarizer, leveraging the HuggingFace Pytorch transformers library, renowned in the NLP (Natural Language Processing) domain. This library offers access to pre-trained models like BERT, which revolutionized language understanding tasks, including text summarization.
The BERT model, or Bidirectional Encoder Representations from Transformers, excels in understanding context in language, using a mechanism known as "attention" to determine the significance of words in a sentence. For summarization, the model embeds sentences and then uses a clustering algorithm to identify key sentences, those closest to the centroids of these clusters, effectively capturing the main ideas of the text.
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.
Create an infinite loop for continuous input.
while True: input_text = input("Enter the text for summarization (type 'exit' to end): ") if input_text.lower() == 'exit': print("Exiting...") break
An infinite loop continuously prompts you for text input, ensuring interactivity. The loop breaks when you type
exit, allowing you to control the application flow effectively.
Create an instance of Summarizer.
bert_model = Summarizer()
Here, you create an instance of the Summarizer class named
bert_model. This instance is now ready to perform the summarization task using the BERT model, simplifying the complex processes of embedding sentences and clustering into an accessible interface.
Generate and print a summary.
summary = bert_model(input_text) print(summary)
Your input text is processed by the bert_model instance, which then returns a summarized version. This demonstrates the power of Python's high-level libraries in enabling complex operations with minimal code.
requirements.txt. The sample application already contains the
requirements.txtfile to specify the necessary modules that the application imports. Open
requirements.txtin a code or text editor to explore its contents.
... # 04 text_summarization bert-extractive-summarizer==0.10.1 ... torch==2.1.2
torchmodules are required for the text summarization application. The summarizer module generates a summary of the input text. This requires PyTorch because the underlying BERT model, which is used for generating the summary, is implemented in PyTorch.
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
Specify the base image.
This command sets the foundation for the build.
python:3.8-slimis 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.
Set the working directory.
WORKDIRsets the current working directory within the Docker image. By setting it to
/app, you ensure that all subsequent commands in the Dockerfile (like
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.
Copy the requirements file into the image.
COPY requirements.txt /app
COPYcommand transfers the
requirements.txtfile 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) to install these dependencies inside the image environment.
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
--no-cache-diroption disables the cache, which reduces the size of the Docker image by not storing the unnecessary cache data.
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_smmodel, 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.
Copy the application code into the image.
COPY *.py /app COPY entrypoint.sh /app
These commands copy your Python scripts and the
entrypoint.shscript into the image's
/appdirectory. This is crucial because the container needs these scripts to run the application. The
entrypoint.shscript is particularly important as it dictates how the application starts inside the container.
Set permissions for the
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.
Set the entry point.
ENTRYPOINTinstruction configures the container to run
entrypoint.shas its default executable. This means that when the container starts, it automatically executes the script.
You can explore the
entrypoint.shscript 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.
To run the application using Docker:
Build the image.
In a terminal, run the following command inside the directory of where the
$ 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
-tflag 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.
Run the image as a container.
In a terminal, run the following command.
$ docker run -it basic-nlp 04_text_summarization.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:
--interactive: This keeps the standard input (STDIN) open even if not attached. It lets the container remain running in the foreground and be interactive.
--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-nlpthat you created with the
04_text_summarization.py: This is the script you want to run inside the Docker container. It gets passed to the
entrypoint.shscript, which runs it when the container starts.
For more details, see the docker run CLI reference.
For Windows users, you may get an error when running the container. Verify that the line endings in the
\n) and not
\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 summarization (type 'exit' to end):
Test the application.
Enter some text to get the text summarization.
Enter the text for summarization (type 'exit' to end): Artificial intelligence (AI) is a branch of computer science that aims to create machines capable of intelligent behavior. These machines are designed to mimic human cognitive functions such as learning, problem-solving, and decision-making. AI technologies can be classified into two main types: narrow or weak AI, which is designed for a particular task, and general or strong AI, which possesses the ability to understand, learn, and apply knowledge across various domains. One of the most popular approaches in AI is machine learning, where algorithms are trained on large datasets to recognize patterns and make predictions. Artificial intelligence (AI) is a branch of computer science that aims to create machines capable of intelligent behavior. These machines are designed to mimic human cognitive functions such as learning, problem-solving, and decision-making.
In this guide, you learned how to build and run a text summarization application. You learned how to build the application using Python with Bert Extractive Summarizer, and then set up the environment and run the application using Docker.
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