Running and Testing the Sentiment Analysis Application in Docker

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. 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. For more details, see the [docker build CLI reference](/reference/cli/docker/buildx/build/). 2. Run the image as a container. In a terminal, run the following command. ```console $ docker run -it basic-nlp 01_sentiment_analysis.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. - `01_sentiment_analysis.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](/reference/cli/docker/container/run/). > [!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](/desktop/troubleshoot-and-support/troubleshoot/topics/#Unexpected-syntax-errors-use-Unix-style-line endings-for-files-in-containers). You will see the following in your console after the container starts. ```console Enter the text for semantic analysis (type 'exit' to end): ``` 3. Test the application. Enter a comment to get the sentiment analysis. ```console Enter the text for semantic analysis (type 'exit' to end): I love containers! Sentiment: Positive Enter the text for semantic analysis (type 'exit' to end): I'm still learning about containers. Sentiment: Neutral ``` ## Summary In this guide, you learned how to build and run a sentiment analysis application. You learned how to build the application using Python with NLTK, and then set up the environment and run the application using Docker. Related information: - [Docker CLI reference](/reference/cli/docker/) - [Dockerfile reference](/reference/dockerfile/) - [Natural Language Toolkit](https://www.nltk.org/) - [Python documentation](https://docs.python.org/3/) ## Next steps Explore more [natural language processing guides](./_index.md).

This section details the `docker run` command, explaining each option, including interactive mode and pseudo-TTY allocation. It specifies the Docker image name and the script to run. Additionally, it provides a note for Windows users about line endings in `entrypoint.sh`. The section then guides the user on how to test the sentiment analysis application by entering text and observing the output. Finally, it summarizes the guide's content, providing links to related resources like Docker CLI references, NLTK, and Python documentation, and suggests exploring more NLP guides.