Build a text recognition app

Table of contents

Overview

In this guide, you'll learn how to create and run a text recognition application. You'll build the application using Python with scikit-learn and the Natural Language Toolkit (NLTK). Then you'll set up the environment and run the application using Docker.

The application analyzes the sentiment of a user's input text using NLTK's SentimentIntensityAnalyzer. It lets the user input text, which is then processed to determine its sentiment, classifying it as either positive or negative. Also, it displays the accuracy and a detailed classification report of its sentiment analysis model based on a predefined dataset.

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

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 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 text classification application is in the Docker-NLP/03_text_classification.py file. Open 03_text_classification.py in a text or code editor to explore its contents in the following steps.

Import the required libraries.
import nltk from nltk.sentiment import SentimentIntensityAnalyzer from sklearn.metrics import accuracy_score, classification_report from sklearn.model_selection import train_test_split import ssl
- nltk: A popular Python library for natural language processing (NLP).
- SentimentIntensityAnalyzer: A component of nltk for sentiment analysis.
- accuracy_score, classification_report: Functions from scikit-learn for evaluating the model.
- train_test_split: Function from scikit-learn to split datasets into training and testing sets.
- ssl: Used for handling SSL certificate issues which might occur while downloading data for nltk.
Handle SSL certificate verification.
try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context
This block is a workaround for certain environments where downloading data through NLTK might fail due to SSL certificate verification issues. It's telling Python to ignore SSL certificate verification for HTTPS requests.
Download NLTK resources.
nltk.download('vader_lexicon')
The vader_lexicon is a lexicon used by the SentimentIntensityAnalyzer for sentiment analysis.
Define text for testing and corresponding labels.
texts = [...] labels = [0, 1, 2, 0, 1, 2]
This section defines a small dataset of texts and their corresponding labels (0 for positive, 1 for negative, and 2 for spam).
Split the test data.
X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
This part splits the dataset into training and testing sets, with 20% of data as the test set. As this application uses a pre-trained model, it doesn't train the model.
Set up sentiment analysis.
sia = SentimentIntensityAnalyzer()
This code initializes the SentimentIntensityAnalyzer to analyze the sentiment of text.

Generate predictions and classifications for the test data.

vader_predictions = [sia.polarity_scores(text)["compound"] for text in X_test]
threshold = 0.2
vader_classifications = [0 if score > threshold else 1 for score in vader_predictions]

This part generates sentiment scores for each text in the test set and classifies them as positive or negative based on a threshold.

Evaluate the model.

accuracy = accuracy_score(y_test, vader_classifications)
report_vader = classification_report(y_test, vader_classifications, zero_division='warn')

This part calculates the accuracy and classification report for the predictions.

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 classification (type 'exit' to end): ")

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

This while loop runs indefinitely until it's explicitly broken. It lets the user continuously enter text for entity recognition until they decide to exit.

Analyze the text.

        input_text_score = sia.polarity_scores(input_text)["compound"]
        input_text_classification = 0 if input_text_score > threshold else 1

Print the VADER Classification Report and the sentiment analysis.

        print(f"Accuracy: {accuracy:.2f}")
        print("\nVADER Classification Report:")
        print(report_vader)

        print(f"\nTest Text (Positive): '{input_text}'")
        print(f"Predicted Sentiment: {'Positive' if input_text_classification == 0 else 'Negative'}")

Create requirements.txt. The sample application already contains the requirements.txt file to specify the necessary packages that the application imports. Open requirements.txt in a code or text editor to explore its contents.
# 01 sentiment_analysis nltk==3.6.5 ... # 03 text_classification scikit-learn==1.3.2 ...
Both the nltk and scikit-learn modules are required for the text classification 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.

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.
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.
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.
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.
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.
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.
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.
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:

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.
Run the image as a container.

In a terminal, run the following command.
$ docker run -it basic-nlp 03_text_classification.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.
- 03_text_classification.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 classification (type 'exit' to end):

Test the application.

Enter some text to get the text classification.

Enter the text for classification (type 'exit' to end): I love containers!
Accuracy: 1.00

VADER Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         1
           1       1.00      1.00      1.00         1

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2

Test Text (Positive): 'I love containers!'
Predicted Sentiment: Positive

Summary

In this guide, you learned how to build and run a text classification application. You learned how to build the application using Python with scikit-learn and NLTK. Then you learned how to set up the environment and run the application using Docker.

Related information:

Next steps

Explore more natural language processing guides.