"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.

2. Specify the main execution block.

   ```python
   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.

   ```python
      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.

4. Create an instance of Summarizer.

   ```python
         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.

5. Generate and print a summary.

   ```python
   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.

6. 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.

   ```text
   ...

   # 04 text_summarization
   bert-extractive-summarizer==0.10.1

   ...

   torch==2.1.2
   ```

   The `bert-extractive-summarizer` and `torch` modules 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.

## 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](/reference/dockerfile/).

1. Specify the base image.

   ```dockerfile
   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.

   ```dockerfile
   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