Play Store Application link – Java to Python in 17 Steps – App on Google Play

Github Project link – https://github.com/kuldeep101990/Python_step17

In this blog post, we’ll look at how to deploy Python applications, focusing on packaging Python scripts, using Docker for deployment (with a comparison to Java). This post is designed for developers who are already familiar with Java, so we’ll highlight the differences and similarities between Python and Java deployment.

1. Packaging Python Scripts

Packaging is the first step in deploying Python applications. This step ensures that your application can be run on other machines or distributed for use by others.

Python Packaging vs. Java Packaging:

In Java, you use tools like Maven or Gradle to package your application into a .jar file, which can then be run on any machine with the appropriate Java Runtime Environment (JRE).

In Python, packaging is typically done by creating a Python package using a setup.py script or wheel files. Python has an easy way to distribute packages via PyPI (Python Package Index).

Packaging a Simple Python Script:

Let’s consider a simple Python script app.py that contains a function to print a greeting.

# app.py
def greet(name):
    print(f"Hello, {name}!")

You can create a basic setup.py to package this script.

# setup.py
from setuptools import setup
setup(
    name='greet-app',
    version='0.1',
    py_modules=['app'],
    install_requires=[],
    entry_points={
        'console_scripts': [
            'greet = app:greet',  # This will map the script function to a CLI
        ],
    },
)

Steps:

1. Create a virtual environment (optional but recommended): python -m venv myenv source myenv/bin/activate # On Windows: myenv\Scripts\activate
2. Install setuptools: pip install setuptools
3. Package the Python application: python setup.py sdist

You can then distribute this .tar.gz file or upload it to PyPI for others to install using pip.

2. Using Docker with Python Apps

Docker is an essential tool for packaging and deploying applications, both for Python and Java. It helps ensure your application runs in the same environment, regardless of where it is deployed.

Java Docker vs. Python Docker:

In Java, you typically run your app inside a Docker container with the help of a Dockerfile. For example, your Dockerfile may look like this:

# Java Dockerfile
FROM openjdk:11
COPY . /app
WORKDIR /app
RUN javac App.java
CMD ["java", "App"]

In Python, the process is very similar. You can run your Python script in a Docker container by writing a Dockerfile:

# Python Dockerfile
FROM python:3.9-slim
# Set the working directory inside the container
WORKDIR /app
# Copy the current directory contents into the container
COPY . /app
# Install any needed dependencies
RUN pip install --no-cache-dir -r requirements.txt
# Run the application
CMD ["python", "app.py"]

Steps to Dockerize a Python App:

1. Install Docker (if not already installed). You can download Docker from Docker’s official site.
2. Build the Docker image: In the same directory where your Dockerfile is located, run: docker build -t python-app .
3. Run the container: After building the image, you can run the container: docker run -it python-app

This will run your Python script inside a Docker container, ensuring that it works the same way on all machines.

Complete Python Program to Test the Process

Here’s a complete Python program that can be used to test the deployment process:

# app.py
def greet(name):
    return f"Hello, {name}!"
if __name__ == "__main__":
    name = input("Enter your name: ")
    print(greet(name))

Creating a Dockerized Version

1. Dockerfile:

FROM python:3.9-slim
WORKDIR /app
COPY . /app
RUN pip install --no-cache-dir -r requirements.txt
CMD ["python", "app.py"]

2. requirements.txt:

# Add any required dependencies here

Deployment Steps:

1. Docker Build: docker build -t python-app .
2. Run the Container: docker run -it python-app

Conclusion

By now, you should be comfortable with the process of deploying Python applications, using Docker for containerization deployment.

This blog post showed you the entire process, from packaging and deployment. Now it’s time to take the code, modify it for your needs, and deploy it.