DEV Community: Soumya kumar naik

Kubernetes

Soumya kumar naik — Wed, 30 Jun 2021 15:42:37 +0000

!/usr/bin/python3

import subprocess
import cgi

print("content-type: text/html")
print()

f = cgi.FieldStorage()
cmd = f.getvalue("var")

lower_cmd = cmd.lower()
l_str = lower_cmd.split()

Filter=["k8s","kubernetes","provide","for","just","up","me","my","to","or","plzz","please","do","using", "is","which","whose","has","can","could","pods","pod","you","u","port","number","no.","deployment","hi","a",
",","no","with","the","all","having","name","named","hello","hey","display","print","of","upto","deployments",
"image"]

for i in range(0,len(l_str)):
if l_str[i] in Filter:
l_str[i] = ""

j_str = " ".join(l_str)

j_str = j_str.split()

if ("create" in j_str or "deploy" in j_str or "launch" in j_str or "make" in j_str) and len(j_str)<4:
command = "kubectl create deployment " + j_str[1] + " --image=httpd --kubeconfig admin.conf"

elif ("create" in j_str or "deploy" in j_str or "launch" in j_str) and (len(j_str)<5 and "id" in j_str):
command = "kubectl create deployment " + j_str[1] + " --image=" + j_str[3] + " --kubeconfig admin.conf"

elif ("get" in j_str or "show" in j_str) and len(j_str)<2:
command = "kubectl get pods --kubeconfig admin.conf"

elif "describe" in j_str:
command = "kubectl describe pods --kubeconfig admin.conf"

elif "expose" in j_str:
command = "kubectl expose deployment " + j_str[1] + " --type='NodePort' --port " + j_str[2] + " --kubeconfig admin.conf"

elif "cluster" in j_str or "cluster's" in j_str or "cluster-info" in j_str or "info" in j_str or "details" in j_str:
command = "kubectl cluster-info --kubeconfig admin.conf"

elif ("replicas" in j_str or "replica" in j_str or "copies" in j_str or "copy" in j_str) and len(j_str)>3:
command = "kubectl scale deployment " + j_str[3] + " --replicas=" + j_str[1] + " --kubeconfig admin.conf"

elif "scale" in j_str:
command = "kubectl scale deployment " + j_str[1] + " --replicas=" + j_str[2] + " --kubeconfig admin.conf"

elif "delete" in j_str and ("svc" in j_str or "service" in j_str):
command = "kubectl delete svc " + j_str[2] + " --kubeconfig admin.conf"

elif "delete" in j_str or "remove" in j_str:
command = "kubectl delete pod " + j_str[1] + " --kubeconfig admin.conf"

elif "service" in j_str or "svc" in j_str or "services" in j_str:
command = "kubectl get svc --kubeconfig admin.conf"

else:
print("Check your command")

output = subprocess.getoutput(command)
print(output)

Docker web application

Soumya kumar naik — Fri, 25 Jun 2021 16:02:57 +0000

body {
background-image: url("whale.gif");
margin-left:150px;

margin-right:250px
}

h1{
margin-top: 30px;
margin: 1em 0 0.5em 0;
font-weight: 600;
font-family: 'Titillium Web', sans-serif;
position: relative;

font-size: 36px;
line-height: 50px;
padding: 47px 15px 15px 30%;
color: #141314;
box-shadow:
inset 0 0 0 1px rgba(38, 39, 41, 0.4),
inset 0 0 5px rgba(52, 54, 56, 0.5),
inset 0px 0 30px rgb(17, 17, 17);
border-radius: 0 10px 0 10px;
background: url("images (1).png") no-repeat center left;
}

#screen {
background: rgb(8, 8, 8);
width: 90%;
height: 500px;
border-style:solid;
border-color: #474545;
margin: 0 auto;
padding: 10px;
opacity: 0.9;
overflow:inherit;
}
#textBox {
width: auto;
color: #f2f3ed;
border: none;
box-sizing: border-box;
font-size: 20px;
vertical-align: middle;
background-color: #000;
opacity: inherit;}

#injected {
  padding-left: 10px;
  font-size: 18px;
  color: white;
  text-align: justify;
  margin-top: 5px;
}

#injected ul {
  list-style: none;
}

#injected li,
th,
td {
  padding-right: 45px;
  padding-left: 25px;
  padding-top: 7px;
  text-align: left;
}

#userInput {
  margin-top: 30px;
  padding: 10px;
}

Docker Web Application

autofocus>
RUN



      OUTPUT COMES HERE..

function lw() {
alert("request");
var xhr = new XMLHttpRequest();
xhr.open("GET","http://17.132.48.54/cgi-bin/docker.py?x="+ document.getElementById("textarea").value,true);
alert("sending request");
xhr.send();
xhr.onload =  function()  {
document.getElementById("injected").innerHTML = xhr.responseText ;

}
}
</script>

What is JavaScript ? It’s Industry Use Cases

Soumya kumar naik — Mon, 21 Jun 2021 14:43:32 +0000

Uses of JavaScript:-
Javascript is one of the most used languages in the market these days. Below graph shows a graphical representation of a company for all languages. JavaScript stands second in the lineup. It is mainly used in building websites and web applications. The other application of JavaScript is listed below.

How 10 Major Companies Are Using JavaScript:-

Microsoft:-

Okay, so you’re probably not going to find JavaScript powering Windows anytime soon, but Microsoft relies on JavaScript for a whole lot else.

First off, Microsoft needs to work closely with JavaScript to built its Edge web browser. All browsers need to process and execute JavaScript efficiently, so Microsoft has developed and maintains its own JavaScript engine for Edge. Actually, there has been talk of them creating an alternate version of NodeJS with the Edge engine.

Recently, Microsoft has really embraced NodeJS. They thoroughly support Node on the Azure cloud platform. Its one of Azure’s major features, and they’ve integrated Visual Studio support for Node.

Microsoft has also developed a version of Node for Internet Of Things(IoT) applications. NodeJS is great of IoT because it’s light weight and efficient.

PayPal:-

PayPal has obviously been using JavaScript on the front end of their website for a long time, but that’s only the beginning.

The online payment giant was one of the earliest adopters of NodeJS. During an overhaul of their account overview page, they decided to try building the page in Node at the same time as their usual Java development. The NodeJS version worked out so well, that they chose to use it in production and build all client-facing applications in Node going forward. That means that most of what you see in your account is running on Node.

PayPal even went as far as to create and maintain their own version of Express, called KrakenJS. It’s pretty obvious that they like JavaScript over at PayPal.

Netflix:-

Like PayPal, Netflix started out using Java for just about everything. They too ran into problems with Java’s size and the time it required to develop.

Over time, Netflix moved away from its more traditional structure into the cloud and started to introduce NodeJS. With Node, Netflix was able to break down pieces of their user interface into individual services. This more distributed approach was able to speed things up an alleviate stress on their servers. Today, a large portion of Netflix’s interface is running on Node.

Groupon:-

Groupon used to be infamously slow. Why? They were powered by Ruby on Rails. That’s the same framework that brought you the Twitter fail whale.

Groupon was all one gigantic Ruby on Rails application. Thanks to difficulties in speed and maintainability, they decided to change over to NodeJS. Node allowed Groupon to rebuild their entire US website by breaking down everything into individual NodeJS web applications. Now, Groupon is made up of over 20 Node applications, and it moves much faster as a whole.

Groupon is currently moving all of their international sites to NodeJS and has joined the NodeJS Foundation. It’s pretty clear that they are happy with their change.

Uber:-

Uber needs to handle loads of data in real time. They have millions of requests coming in continuously, and that’s not just hits on a page. Uber needs to track driver locations, rider locations, and incoming ride requests. It has to seamlessly sort that data and match riders as fast as possible.

All of that plays to NodeJS’s and JavaScript’s strengths. Node is designed to handle requests and hand off data quickly. It’s asynchronous capabilities are a huge part of that. Node is central to Uber’s user facing stack for just that reason.

Facebook:-

You’re probably aware that Facebook uses JavaScript. It’s kind of hard to miss. What’s probably not as obvious is exactly how much JavaScript goes into making Facebook and how much Facebook is involved in JavaScript development.

Try disabling JavaScript in your web browser and going to Facebook. The website will actually stop you from logging in because it won’t work without JavaScript.

You may have noticed the way that Facebook loads. Each piece of the page is separate. Facebook has invented its own way of breaking down and delivering sections of JavaScript separately. In an odd way, each section of your Facebook page is a collection of independent JavaScript applications.

It doesn’t stop there. Facebook created React, one of the most popular front end frameworks. Facebook uses React on Facebook.com as well as Instagram and WhatsApp.

Google:-

How doesn’t Google use JavaScript? Seriously, it’s everywhere. Google’s search results that spring up as your typing get there with JavaScript. The Gmail web client is powered by JavaScript. Google Docs? Yeah, that’s JavaScript too.

Google develops and usually open sources it’s own JavaScript tools. The most obvious example is AngularJS. Angular is used most prominently in Google’s DoubleClick advertising platform, but it’s also one of the most popular front end frameworks available. It’s even part of the MEAN stack.

Google’s more intensive services, like Google Docs, use Closure Tools. This set of tools compiles JavaScript into a lower-level faster form more suited for rich and highly responsive web applications.

There’s another big point to touch on. Google developed Chrome. Chrome, being a web browser, needed a JavaScript engine, so Google also made V8. V8 not only powers Chrome, it’s at the heart of NodeJS. So, without Google, there would be no Node.

eBay:-

eBay’s story is a lot like Netflix’s. For a long time, just about everything in eBay’s tech stack was based on Java. A few years ago, eBay encountered a problem that Java wasn’t the right solution for. They decided to give NodeJS a shot instead.

Node worked so well that eBay not only kept using it for that particular service, they began migrating their entire user facing stack to NodeJS. Now, just about everything that you interact with on eBay is powered by Node. Sure, beneath Node, Java is still dealing with their databases, but eBay still places a lot of trust in NodeJS.

Walmart:-

Most people probably don’t think of Walmart as a tech company, but because they’re one of the largest retailers in the world, their online retail business is gigantic. It’s not much of a stretch to see how they need build a technologically advanced web application do drive their online business.

Walmart started out with Java. It’s a solid enterprise-grade platform that has been the de facto choice for years. However, Walmart needed something faster and lighter weight for their mobile site. So, they turned to NodeJS.

Once again, Walmart began to see Node as a valid Java replacement in loads of other places. Today, the Walmart.com that you see is powered by Node. NodeJS was also the ideal choice for other web applications within their marketplace that require multiple users to be able to access management interfaces simultaneously.

LinkedIn:-

LinkedIn relies on NodeJS for its mobile site. A few years back, LinkedIn used Rails for its mobile site. As with other other large Rails applications, it was slow, monolithic, and it scaled poorly.

LinkedIn switched over to NodeJS to solve its scaling problems. Node’s asynchronous capabilities allowed the LinkedIn mobile site to perform more quickly than before while using fewer resources. Node also made data sharing and building APIs easier for the LinkedIn developers.

Creating-Live-Streaming-Video-Chat-App-without-voice-using-cv2-module-of-Python

Soumya kumar naik — Sat, 12 Jun 2021 12:50:44 +0000

For creating live streaming video chat we have to install the the library that are mentioned below:

1.pip install opencv-python
2.pip install sockets
3.pip install pickle-mixin
4.pip install imutils

First we have to write the server program:

import socket, cv2, pickle,struct,imutils

Socket Create

server_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
host_name = socket.gethostname()
host_ip = socket.gethostbyname(host_name)
print('HOST IP:',host_ip)
port = 9999
socket_address = (host_ip,port)

Socket Bind

server_socket.bind(socket_address)

Socket Listen

server_socket.listen(5)
print("LISTENING AT:",socket_address)

Socket Accept

while True:
client_socket,addr = server_socket.accept()
print('GOT CONNECTION FROM:',addr)
if client_socket:
vid = cv2.VideoCapture(0)

    while(vid.isOpened()):
        img,frame = vid.read()
        frame = imutils.resize(frame,width=320)
        a = pickle.dumps(frame)
        message = struct.pack("Q",len(a))+a
        client_socket.sendall(message)

        cv2.imshow('TRANSMITTING VIDEO',frame)
        key = cv2.waitKey()
        if key ==ord('return'):
            client_socket.close()

Client side Program :

lets mke the client code

import socket,cv2, pickle,struct

create socket

client_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
host_ip = '192.168.43.35' # paste your server ip address here
port = 9999
client_socket.connect((host_ip,port)) # a tuple
data = b""
payload_size = struct.calcsize("Q")
while True:
while len(data) < payload_size:
packet = client_socket.recv(4*1024) # 4K
if not packet: break
data+=packet
packed_msg_size = data[:payload_size]
data = data[payload_size:]
msg_size = struct.unpack("Q",packed_msg_size)[0]

while len(data) < msg_size:
    data += client_socket.recv(4*1024)
frame_data = data[:msg_size]
data  = data[msg_size:]
frame = pickle.loads(frame_data)
cv2.imshow("RECEIVING VIDEO",frame)
key = cv2.waitKey(1) & 0xFF
if key  == ord('q'):
    break

client_socket.close()

After writing the code First we have run the server side program then we run client side Program.

How to create own image by python code

Soumya kumar naik — Wed, 09 Jun 2021 11:24:10 +0000

To create our own image:
-For that we have to install the python.
-Then on top of the python we have to install numpy and opencv library .

For creating own image:
https://github.com/soumyanaik717/Image-creation-

For image crop and swap:
https://github.com/soumyanaik717/crop-the-image-and-swap-it

For collage image:
https://github.com/soumyanaik717/collage-image

Confusion Matrix use cases in Cyber Crime

Soumya kumar naik — Sun, 06 Jun 2021 02:59:27 +0000

What is the Confusion Matrix?

The confusion matrix is a useful tool used for classification tasks in machine learning with the primary objective of visualizing the performance of a machine learning model.

In a binary classification setting where the negative class is 0 and the positive class is 1, the confusion matrix is constructed with a 2x2 grid table where the rows are the actual outputs of the data, and the columns are the predicted values from the model.

How to interpret a Confusion Matrix?

I will start by first describing some terminology, if you are able to work out where they would go then you are halfway there:

True Positive: The model predicted positive and the label was actually positive.

True Negative: The model predicted negative and the label was actually negative.

False Positive: The model predicted positive and the label was actually negative — I like to think of this as falsely classified as positive.

False Negative: The model predicted negative and the label was actually positive— I like to think of this as falsely classified as negative.

Our confusion matrix has two red patches on our grid, which infer to us the type of errors that our model is making.
Type-1 => False positive
Type-2 => False negative

How we use the confusion matrix in cyber crime?

Let's take a case study of this:
We are tasked with building a classifier that predicts fraudulent or non fraudulent for different transactions. The data was handed to us by a major bank in the UK with very tight security – the personal details of all customers were encrypted for privacy concerns – so fraudulent transactions do not happen very often. In fact, the data that they had given us had 10,000 Negative cases (non-fraudulent) and 1000 positive cases(fraud). The below Figure shows the results of our classifier.

In the above image there are 11000 cases of fraud and non-fraudulent among them our ML model find the 10000 cases are not-Fraudulent i.e negative .But actually 9900 cases among them are True Negative(TN) and the rest 100 cases are False Negative(FN).here our model will predict the wrong output.so our model will not alert the Bank and The bank also don't the fraud cases. Because the model predict 10000 cases are not-fraudulent.so this type of error FN(Type-2) is very dangerous in this case. The rest 1000 cases are fraud cases but among them 800 cases are not fraud but the model gives us this is as a fraud cases i.e
False Negative(Type-1).in this case the Type-1 error are not so dangerous. but the Type-2 error are so dangerous.

Launching a GUI application inside the docker container

Soumya kumar naik — Tue, 01 Jun 2021 16:05:48 +0000

STEP 1:- Launching Container
docker run -it --name ml --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" centos

After running this command it will lunch docker container name as ml

STEP 2:- Installing python3
yum install python3

STEP 3:- Installing jupyter notebook
pip3 install jupyter

STEP 4:- Installing Firefox
yum install firefox

STEP 5:- Launching jupyter notebook
jupyter notebook

Deploy an ML model inside docker container

Soumya kumar naik — Mon, 31 May 2021 03:22:19 +0000

For installing the docker, I'm using the REDHAT 8 operating system. By default the REDHAT 8 doesn't come with the docker software. So first we have to download the docker software in side the REDHAT 8.
For this we have to configure the yum repository.
First of all login with root account and then go to:
-> cd /etc/yum.repos.d/

Create the file for docker repo with any name. The extension of file should be repo only.
Then open the vim editor with file that you have created .then write the code that are given below:
[docker]
baseurl=https://download.docker.com/linux/centos/7/x86_64/stable/
gpgcheck=0
EOF
Now update the yum repository using command:
-> yum repolist

Now Download the docker-ce software that is community version. Use command –
yum install docker-ce –nobest
After installing docker if we check docker is install or not then run the command:
-> rpm -q docker-ce (it will show the software name )

Now start all the services of Docker through the below command:
->systemctl enable docker
If we check the status of the docker then run the command:
->systemctl status docker

Now we can move towards deployment of ML Model. So let's pull a centos docker image for building own docker image top of it.
->docker pull centos:latest
To check the images, run the below command, you can see your all the docker images like this:
->docker images

Then we have to start our docker using the command:
-> docker run -i -t centos:latest
Then it will gives us new terminal for centos like this:

Then inside the docker container we have to deploy our ML model
For this we have install the python and the libraries that are require for our ML model.
First inastall the python using the command:
-> yum install python3

Then we have to install the libraries using the command:
->pip3 install numpy
->pip3 install pandas
->pip3 install scikit-learn
For ML model we require the data set.so for this we have to transfer or import the data set from the base operating system(REDHAT 8) to docker container.
For this we have to use the command:
-> docker cp (file name) (image name of container):/

Here the command will be:
-> docker cp SalaryData.csv busy_Matsumoto :/

After that you will see your file that would be transferm from base operating system to docker container.

Then we have to lunch our vim editor in docker container to run the ML code:
->vim file name
->vim salary.py

After writing the code in vim editor we have to run the command in docker container.so for that we have to use the command:
-> python3 (file name)
-> python3 salary.py

Like this we have run the ML model inside the docker container.

Hopefully, you enjoy it