Up until now we have been putting together infrastructure to run the application on. We will now get to building the root of the application. I start by breaking out the task for creating a docker container into it’s own task. To do that I ask the following of ChatGPT, I would like to work on Master Plan Step 1 - "Create a Docker container for running your application." I will be building the application from a laptop. Break this task down into simple steps that I can follow. Refer to this task list as the "Create Docker Container Plan".

Sure, let's break down the "Create a Docker container for running your
application" task into a step-by-step plan, which we'll refer to as
the "Create Docker Container Plan":

1. **Prepare Your Application:**
   - Ensure your application is ready for containerization. It should
     be in a directory with all necessary files and dependencies.

2. **Install Docker:**
   - Make sure Docker is installed on your laptop. You can download
     and install Docker Desktop for Windows or macOS, or Docker Engine
     for Linux.

3. **Write a Dockerfile:**
   - Create a `Dockerfile` in the root directory of your
     application. The `Dockerfile` is a text file that contains
     instructions to build the Docker image for your application.

4. **Choose a Base Image:**
   - Decide on a suitable base image for your application. This image
     will provide the foundation for your Docker container. Common
     choices include official language-specific images (e.g., Python,
     Node.js) or lightweight Alpine Linux images.

5. **Copy Application Files:**
   - Use the `COPY` instruction in your `Dockerfile` to copy your
     application files from the local directory to the Docker image.

6. **Install Dependencies:**
   - If your application has any dependencies, use the appropriate
     package manager in your `Dockerfile` (e.g., `pip`, `npm`, `yarn`)
     to install them inside the Docker image.

7. **Expose Ports (if needed):**
   - If your application needs to listen on specific ports for
     communication, use the `EXPOSE` instruction in your `Dockerfile`
     to specify the port(s) to be exposed.

8. **Set the Startup Command:**
   - Use the `CMD` or `ENTRYPOINT` instruction in your `Dockerfile` to
     specify the command that will run when the container starts. This
     command should start your application.

9. **Build the Docker Image:**
   - Open a terminal or command prompt, navigate to your application's
	 directory, and run the command to build the Docker image using
	 the `Dockerfile`. For example:
     
     docker build -t your-image-name .
     
10. **Verify the Docker Image:**
    - After the image is built, verify that it includes all the
      necessary files and dependencies by running a new container
      based on this image locally.

11. **Test Your Application:**
    - Launch a container from the built image and verify that your
      application runs as expected within the container.

12. **Push the Docker Image (Optional):**
    - If you have a Docker Hub account (or any other container
      registry), you can push your Docker image to the registry so
      that it can be accessed and deployed on other systems.

With the "Create Docker Container Plan," you can containerize your
application, making it easier to run consistently on different
environments, including your Digital Ocean instance. Each step
provides incremental progress towards successfully building and
deploying your Docker container.

This makes sense, however I don’t have an application. These steps only tell me how to package it together into a Docker container. I think I need to ask ChatGPT to build this application for me now. I need more details for the first step of the Create Docker Container Plan. Please provide me with a simple Python based application using Flask that will respond to a deep health check request. It will respond with JSON if the application is healthy.

Sure! Here's a simple Python-based application using Flask that will
respond to a deep health check request with JSON if the application is
healthy:

1. **Prepare Your Application:**

First, create a new directory for your Flask application and navigate
to it in your terminal.

2. **Create a Python File:**

Create a new Python file (e.g., `app.py`) in the directory you just
created.

3. **Install Flask:**

Make sure you have Flask installed in your Python environment. If you
don't have it installed, you can install it using `pip`:

pip install Flask

4. **Write the Flask Application:**

In the `app.py` file, add the following code to create a Flask
application:

from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello, World!'

@app.route('/health')
def health_check():
    # Perform health check logic here (e.g., check database connection, dependencies, etc.)
    # If the application is healthy, respond with a JSON indicating its health status.
    health_status = {
        "status": "healthy",
        "message": "Application is running smoothly."
    }
    return jsonify(health_status)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

5. **Run the Flask Application:**

To test the Flask application locally, run the following command in
your terminal:

python app.py

This will start the Flask development server, and you can access your
application at `http://localhost:5000/`.

6. **Deep Health Check Request:**

To perform a deep health check on your Flask application, you can make
an HTTP GET request to the `/health` endpoint:

curl http://localhost:5000/health

You should receive a JSON response indicating that the application is
healthy, like this:

{
    "status": "healthy",
    "message": "Application is running smoothly."
}

This simple Flask application responds to the `/health` endpoint with
a JSON message when the application is healthy. You can modify the
`health_check` function to add more complex health checks based on
your application's requirements, such as checking database
connections, external service dependencies, or other critical
components.

This looks reasonable except for the fact that I think that using a virtual env would be best. Modify this task list to add the use of a virtual env to make working with Python easier.

Absolutely! Using a virtual environment is a good practice in Python
development to manage dependencies and keep your project
isolated. Here's the updated "Create Docker Container Plan" with the
addition of setting up a virtual environment:

1. **Prepare Your Application:**
   - Create a new directory for your Flask application and navigate to
     it in your terminal.

2. **Set Up a Virtual Environment:**
   - Create a virtual environment within your application directory to
     manage Python dependencies.

   On macOS/Linux:

   python3 -m venv venv
   source venv/bin/activate

   On Windows:

   python -m venv venv
   venv\Scripts\activate

3. **Install Flask and Dependencies:**
   - With the virtual environment activated, install Flask and any
     other required dependencies using `pip`.

   pip install Flask

4. **Create a Python File:**
   - Create a new Python file (e.g., `app.py`) in the application
     directory.

5. **Write the Flask Application:**
   - In the `app.py` file, add the Python code to create a Flask
     application.

6. **Run the Flask Application:**
   - Test the Flask application locally with the virtual environment
     activated.

   python app.py

7. **Deep Health Check Request:**
   - Perform a deep health check on your Flask application by making
     an HTTP GET request to the `/health` endpoint.

   curl http://localhost:5000/health

   You should receive a JSON response indicating that the application
   is healthy.

8. **Dockerize the Application:**
   - Write a `Dockerfile` in the root directory of your application to
     containerize the Flask application.

9. **Build the Docker Image:**
   - Open a terminal or command prompt, navigate to your application's
     directory, and build the Docker image using the `Dockerfile`.

   docker build -t your-image-name .

10. **Verify the Docker Image:**
    - After the image is built, verify that it includes all the
      necessary files and dependencies by running a new container
      based on this image locally.

11. **Test Your Application in the Docker Container:**
    - Launch a container from the built image and verify that your
      application runs as expected within the container.

12. **Push the Docker Image (Optional):**
    - If you have a Docker Hub account (or any other container
      registry), you can push your Docker image to the registry so
      that it can be accessed and deployed on other systems.

By using a virtual environment, you can manage Python dependencies
separately for each project, ensuring that the required packages are
isolated and don't interfere with other projects on your laptop. This
approach enhances the reproducibility and maintainability of your
Python applications.

This is a step better but still misses the critical piece of information – what is the application code. What are the contents of the app.py file in step 5?

Apologies for the oversight in my previous responses. In Step 5, the
`app.py` file should contain the code for your Flask application,
including the routes for the root (`/`) and health (`/health`)
endpoints. Here's the correct content of the `app.py` file:

from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello, World!'

@app.route('/health')
def health_check():
    # Perform health check logic here (e.g., check database connection, dependencies, etc.)
    # If the application is healthy, respond with a JSON indicating its health status.
    health_status = {
        "status": "healthy",
        "message": "Application is running smoothly."
    }
    return jsonify(health_status)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

This code sets up a basic Flask application with two routes:

1. The `'/'` route responds with the string `'Hello, World!'` when
   accessed. You can customize this route to handle other requests or
   render HTML templates for more complex applications.

2. The `'/health'` route handles the deep health check request. In
   this example, it responds with a JSON message indicating that the
   application is healthy. You can add more complex health check logic
   to this route, depending on your application's needs.

Please use this updated content for your `app.py` file when working on
Step 5 of the "Create Docker Container Plan."

This is working. The application works as you would expect. Now, what are the contents of the Dockerfile in Step 8?

Sure! In Step 8, the `Dockerfile` is used to define the instructions
for building the Docker image for your Flask application. Here's the
content of the `Dockerfile`:

# Use a base image with Python installed
FROM python:3.9-slim

# Set the working directory inside the container
WORKDIR /app

# Copy the application files to the container's working directory
COPY app.py /app/app.py

# Install Flask and any other required dependencies
RUN pip install Flask

# Expose port 5000 (the same port we specified in app.py)
EXPOSE 5000

# Set the startup command to run the Flask application
CMD ["python", "app.py"]

Explanation of the Dockerfile contents:

- `FROM python:3.9-slim`: This line specifies the base image to use
  for building your Docker image. We're using the official Python 3.9
  slim image as our base, which includes only essential packages to
  minimize the image size.

- `WORKDIR /app`: This sets the working directory inside the container
  to `/app`. This is where our application code will be copied.

- `COPY app.py /app/app.py`: This line copies the `app.py` file from
  the current directory (where the Dockerfile resides) to the `/app`
  directory inside the container.

- `RUN pip install Flask`: This command installs Flask and any other
  required dependencies inside the container. It's good practice to
  separate the installation of dependencies from copying the
  application code to leverage Docker's layer caching for faster
  builds.

- `EXPOSE 5000`: This line exposes port 5000, which is the port our
  Flask application will be running on. This is necessary so that
  external services can communicate with the application running in
  the container.

- `CMD ["python", "app.py"]`: This specifies the command that will be
  executed when the container starts. In this case, it runs the
  `app.py` file using the Python interpreter, which will start the
  Flask application.

With this Dockerfile, you can now build a Docker image containing your
Flask application using the `docker build` command as mentioned in
Step 9 of the "Create Docker Container Plan."

Continuing I build the Docker image. But I don’t want to have to remember the magic sauce so I write a quick build script.

#!/bin/sh
set -ex
docker build -t manager-blind-spot-application-server .

This quickly builds the Docker image for me. Then I can run the Docker image. I’m trying to think like someone who’s never done this before and so I ask ChatGPT. How do I run the docker image your-image-name?

To run the Docker image you built with the tag `your-image-name`, you can use the `docker run` command. Here's the command to run the Docker image:

docker run -p 5000:5000 your-image-name

Let's break down the options used in the `docker run` command:

- `-p 5000:5000`: This option maps port 5000 from the container to
  port 5000 on your host machine. This is necessary to access your
  Flask application running inside the container.

- `your-image-name`: Replace this with the actual name you gave to
  your Docker image when you built it. For example, if you used
  `docker build -t my-flask-app .`, then the image name would be
  `my-flask-app`.

After running the `docker run` command, your Flask application will be
running inside the Docker container. You can access it from your host
machine by visiting `http://localhost:5000/` in your web browser.

Keep in mind that if your Flask application needs to access any
external resources, such as a database or other services, you might
need to set up additional networking options or environment variables
to connect to those resources from within the container. But for a
basic Flask application, the above command should work fine to run it
in the Docker container.

One thing to notice is that ChatGPT is very verbose. This would be helpful for people that have little experience. Not sure I like it but that’s not the point of this exercise. Again, I want to write all of this down so that tired me six months from now doesn’t need to remember anything. So I write a run script. One thing to note is I’ve named my application manager-blind-spot-application-server.

There is a problem with ChatGPT’s response, I want the image to run disconnected. I didn’t ask for that but I would have liked ChatGPT to catch the context. I add the -d option.

#!/bin/sh
set -ex
docker run -d -p 5000:5000 manager-blind-spot-application-server

The next step is to push the image to a repository. It will be a little expensive once I get past a single image but for now I’ll use Digital Ocean.

docker login registry.digitalocean.com
docker tag manager-blind-spot-application-server:latest registry.digitalocean.com/manager-blind-spot/application-server:latest
docker push registry.digitalocean.com/manager-blind-spot/application-server:latest

And then I need to change how I create the image. I need to login to my container repository, pull the image, and launch it. The new script looks like this.

import digitalocean
import os
from time import sleep

# Set up your Digital Ocean API token
api_token = os.environ.get('DIGITALOCEAN_APIKEY')

# Create a Droplet instance
manager = digitalocean.Manager(token=api_token)

# Get the SSH keys
sshKeys = manager.get_all_sshkeys()

# Define the Cloud-Init script
cloudConfig = f'''
#cloud-config

packages:
  - docker.io

# create the docker group
groups:
  - docker

# Add default auto created user to docker group
system_info:
  default_user:
    groups: [docker]

runcmd:
  - 'docker login --username "{api_token}" --password "{api_token}" registry.digitalocean.com'
  - 'docker pull registry.digitalocean.com/manager-blind-spot/application-server:latest'
  - 'docker run -d -p 80:5000 registry.digitalocean.com/manager-blind-spot/application-server:latest'
'''

# Define the parameters for creating the droplet
droplet_params = {
    'name': 'ubuntu-s-1vcpu-1gb-tor1-01',  # Set your desired droplet name
    'region': 'tor1',  # Set your desired region
    'size': 's-1vcpu-1gb',  # Set the droplet size
    'image': 'ubuntu-22-04-x64',  # Set the droplet image
    'ssh_keys': sshKeys,  # Set the droplet SSH keys
    'backups': False,
    'ipv6': False,
    'user_data': None,
    'private_networking': None,
    'volumes': None,
    'user_data': cloudConfig
}

# Create the droplet
droplet = digitalocean.Droplet(token=api_token, **droplet_params)
droplet.create()

print(f"Waiting for droplet to become active...")
while True:
    droplet.load()
    print(droplet.status)
    if droplet.status == "active":
        break
    sleep(10)

print(f"Created droplet with ID: {droplet.id}")
print(f"Droplet IP: {droplet.networks['v4'][0]['ip_address']}")

Launching the instance is the same, and the result is that I can now check the health of the service.

 curl 137.184.172.82/health
{"message":"Application is running smoothly.","status":"healthy"}