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We successfully launched API service using container.

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And other than that, we launched one more container and you've seen that it is possible to run API

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service in two different containers and access both those API services simultaneously and in such case,

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internally in each container Flask web server will be running at the same port 5050.

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But you need to expose those internal ports to two different ports on your local computer.

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For instance, in one container, this port could be 5050, but in another it must be different.

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For instance, 50, 51 or 6000 or something else.

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Anything you want.

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All right.

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Now let's jump inside of the container and have a look at its file system and find inside of the container

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working directory that we defined here on this line using workdir instruction and also analyze which

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processes are running inside of this container, Please ensure that this container is up and running

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and you are able to make some requests to the API server like that.

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And now in the new tab, please have a look at the list of containers using Docker PS Command.

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Here you'll find the name of the running container and such names are generated automatically by Docker.

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In my case, I see such name.

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In your case you will see a completely different name, but it is located at the end of this output.

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Now, using this name, we will connect to this container using bash process.

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One more process will be started and we will be able to get inside of the container using shell.

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Let me do that in separate tab.

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And if you want to start another process in running container, you need to use command Docker exec.

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Next, please add options Dash.

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It stands for Interactive and T stands for Terminal and afterwards will be name of the running container.

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And in my case, this name is Frosty Beverage.

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Let me copy this name from here and paste here.

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And after space, please add Bash because we want to run one more process that is called bash.

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It is Shell.

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Let's now execute such command, and I'm inside of the container.

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Here is container ID, and I was placed inside of the working directory slash app.

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Let me list files and folders here.

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And such files as docker file file file dot log main.py and requirements.txt.

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And basically those four files are the same files that are located in the folder in our project here.

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Here are those files.

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Also accept those four files inside of the app folder should be those hidden files and folders.

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Hidden folder and hidden file dot env dot local.

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Let's have a look at them as well.

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And yes, here is dot vnv folder and dot env dot local file.

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But please notice that we discussed before that you should not make dot env dot local file and other

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files with environment variables public.

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You should keep them only locally on the computer where you run corresponding service.

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For instance, front end react application or back end python project doesn't matter.

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You should keep them private.

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But we see that we actually added such invaded local file to the Docker image and Docker images could

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be very easily published, for instance to Docker Hub and anyone might have access to your published

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Docker image.

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And certainly it's not a good practice to put such sensitive files as files with environment variables

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inside of the Docker image.

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And if you don't want to do that, you need to use Docker ignore file with list of files that Docker

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will ignore and don't add to the Docker image.

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But now we are using this Docker image only locally just for development process.

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We don't publish such image to Docker hub or other hosting services.

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That's why we will keep this file inside of the image and we will not add Docker, ignore and ignore

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such file in order to avoid adding it to the image.

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This move, when we keep this file inside of the image, allows us to access our API service after we

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run it like that with Docker run command and here we only expose port, we publish internal port to

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one of the external ports on our computers and we here specify only name of the image.

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And this image contains all necessary files, including file with environment variables in order to

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be able to run API service as it was run before.

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We are still able to access images retrieved from the Unsplash API.

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It means that key that is located inside of the local file works as expected.

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We use it for request to unsplash API.

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Also notice that here inside of this app folder there is sub folder dot env and such folder we created

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on purpose before creation of the python virtual environment locally on our computers and such folder

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contains such sub folders as being lib and different files and basically inside of the image.

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We don't require this dot env folder because as I've told you before, all packages were installed globally

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here inside of the container.

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And in a moment we will verify that.

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That's why it makes sense to add such folder to Docker ignore, but for now we will not do that to keep

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it simple.

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But please keep in mind that this dot env folder is not required for running our flask web server inside

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of the container.

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All right.

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Key point here is that there is app folder inside of the container.

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We could move one level up here and have a look at files and folders here and there are such folders

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as home lib media and so on, all folders that are usually present on any Linux like operating system.

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And here, along with home dev boot and bin folders, there is app folder with all our project files.

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That's why I told you before that you should add such instruction as workdir inside of the Docker file

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to put all your application files into separate folder.

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Otherwise if you don't add such instruction, all your application files and folders will be placed

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here near such folders as home and so on.

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And it's not a good practice to do so because at least you could override some folders that are already

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present in this root directory here.

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All right, now let's analyze Python related installations here.

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And first let's have a look at version of the python by entering command Python version and see version

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3.9.4.

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And that's that exact version, that tech 3.9.2.

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We verified it here at Docker Hub.

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Tech 3.9 points at the same image as tech three latest and 3.9.4.

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That's why we see here exact Python version 3.9.4.

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Here it is.

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Also could have a look at version of the pap pap version.

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And this version is 21 .0.1.

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Also told you that Pap installed all dependencies globally inside of the container and that means that

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we should be able to access such dependency as flask for instance, globally.

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Here inside of the container.

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Let's try to check version of the Flask flask dash dash version and get result version of the flask

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is 1.1.2.

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Same applies to dot env package.

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It is also present globally here and it is accessible globally.

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If I try to do the same on my computer and enter dot env dash dash version I'll get error command not

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found because such package dot env was installed on my computer only inside of the virtual environment.

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That's why it is not accessible like that.

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But inside of the container we are able to access flask and dot env globally because single purpose

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of this container is to run python flask application.

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All right, now let's have a look at the processes that are currently running inside of this container.

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Please type dash x and you should find list of currently running processes inside of the container and

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there are only four of them.

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First process with process ID one is called root process, and that is that process that was started

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by container initially.

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And in our example, command Python plus Main.py was used.

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That's why this process is called root process.

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And here you see the same command.

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This command was entered by us here in the Docker file as last instruction cmd.

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Also one more process started here and this process is the same as this one Python Main.py difference

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here is that here you see absolute paths and the reason for that is that this process is multithreaded

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process.

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L here stands for that and this process is root process is session leader.

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This small s tells us that also accept Python processes here and here you should see bash process here

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on the list.

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And we actually started this additional process inside of the container because we opened Bash Shell

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here.

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That's what you see right now.

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It is.

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Bash Shell.

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Also, we just entered Command X and that's why one more process was created here.

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And these are all processes that are running inside of the container.

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It is just a single purpose container for running our Python application.

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No other processes are present here.

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That's the beauty of Docker.

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You could create as many containers you want, but each container is single purposed.

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Let's now back again to app folder and let's now also have a look at the Requirements.txt file that

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is present here in the app directory and recap that this Requirements.txt file was created during image

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build process by pipenv.

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So let's have a look at it.

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Cut requirements dot txt and here are contents of this file and you might find here such dependencies

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as flask dash course or flask.

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Also there are such dependencies as python dot env and request, but dev packages such as pylint and

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black code formatter are absent here in this list.

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And I told you before that env does not include dev packages when you generate such txt file out of

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file using env LOC requirements command.

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But as we verified we were able to successfully run our application because such dependencies as Flask

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and flask course are available here and listed in the requirements.txt file.

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Great.

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Let me show you one more thing.

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Let's have a look at main.py file.

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And in this file there is such route as new image.

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And of course now such main.py file is fully synchronized with Main.py file here in the folder.

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But let's try to modify this route for instance, to new image to like that save changes here in this

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main.py file.

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Go back to container and again have a look at contents of the main.py file and contents here remained

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unchanged.

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Reason is very simple.

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Such main.py file inside of the container is completely different from the main.py file here in the

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folder.

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And if you want to apply such changes that you made in the folder, you need to rebuild image and rerun

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container.

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Let's get back to new image root here, as was before.

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And now you understand that Main.py file inside of the container is different from the main.py file

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inside of the folder in our Visual Studio code project.

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All right.

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That's how container looks like from inside.

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And now let's exit from here.

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I hope that idea is fully clear.

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And now you understand what happens under the hood when you actually run specific container.

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And let me close this tab.

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All right.

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That's it for this lecture.

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And in the next one, let's quickly summarize what we did here, because we are actually done with localization

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of the API service.

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I'll see you guys after the small pause.