DEV Community: Rutik Bhoyar

Smart Contract in Ethereum

Rutik Bhoyar — Wed, 29 Dec 2021 14:48:09 +0000

Now-a-days Web3.0 is trending and there are lot of stuffs also in talks like blockchain, NFT, Ethereum, Solidity, etc. etc..

As many of us would know about the blockchain, let's just see it's definition

Blockchain is a continuously growing list of records or blocks you can say, which are completely decentralized with distributed databases and immutable ledger meaning continuous blocks linked to each other.

So now let's move towards our topic Smart Contract.

Example

Let's take an example for this. Suppose there is company which helps various NGO's to collect the funds for their campaigns. Various NGO's files pledged amount to their site and rest of the work is done by that company, which can also be called as "crowd-funding".

If the amount contributed by various people is equal to or greater than the pledged amount the NGO's will get the amount by deducting the some commission by the platform provider company.

Else if the money funded is not equal to the pledged amount, the money will be returned to the people.

Similarly, the smart contract's work. See the formal definition of

"Smart Contract"...

"smart contract" is simply a program that runs on the Ethereum blockchain. It's a collection of code (its functions) and data (its state) that resides at a specific address on the Ethereum blockchain.

Smart contracts are a type of Ethereum account. This means they have a balance and they can send transactions over the network. However they're not controlled by a user, instead they are deployed to the network and run as programmed. User accounts can then interact with a smart contract by submitting transactions that execute a function defined on the smart contract.

In simple language, it is a certain set of rules written with the help of scripting language like Solidity in Ethereum , which can be considered as JavaScript.

Where the smart contract will be present in blockchain?

So when there will be a Peer-to-Peer network of blockchain where all the nodes are interconnected to each other i.e. ledger. Each node will contain a separate blockchain. So where does the smart contract will be present?.

It will be present alongside with the blockchain of node.

Real-World Smart Contract in use....

So when the Jack Dorsey's first tweet was sold to Person A as NFT(Non-fungible Token) ,all the transaction was noted in smart contract and money was transferred to the original owner.
Now when that Person A will sell that tweet to Person B all the transaction will be noted and maintained by the "Smart Contract" and the royalty amount will be sent to the Jack Dorsey.

Conclusion

Smart contracts can define rules, like a regular contract, and automatically enforce them via the code. Smart contracts cannot be deleted by default, and interactions with them are irreversible.

JavaScript Using RegEx

Rutik Bhoyar — Sat, 11 Dec 2021 05:46:34 +0000

Problem:

Return the number (count) of vowels in the given string.
We will consider a, e, i, o, u as vowels.

The input can consist of Lower case and upper case letters so make sure to count both of them

function getCount(str) {
    return str.match(/[aeiou]/gi).length
}

This will give you the count of vowels in javascript.

Stop gninnipS My sdroW!

Problem:
Write a function that takes in a string of one or more words, and returns the same string, but with all five or more letter words reversed (Just like the name of this Kata). Strings passed in will consist of only letters and spaces. Spaces will be included only when more than one word is present.

function spinWords(string) {

 return string.replace(/\w{5,}/g, 
    function(w) {return w.split('').reverse().join('')}
  )
}

This will be give correct output like below.

spinWords(“Hey fellow warriors”) //returns “Hey wollef sroirraw"
spinWords("This is a test") //returns "This is a test"
spinWords("This is another test") //returns "This is rehtona test"

List of Open Source Projects ML

Rutik Bhoyar — Mon, 21 Jun 2021 18:18:05 +0000

List of Open Source Projects ML
Open source is an vast ocean if you are willing to dive in there, here is a list of some open-source projects to which you can contribute as an open-source contributor. Anyone can contribute to these repositories from beginners to experts based on their understanding and skills.

I have not explained about the projects, attaching GitHub links of the repositories where you will find all the necessary information and steps for contribution.

For getting started with open source , make sure you are sure about your skills and confident enough to contribute and solve the issues out there. And most important is you have to be patient for grabbing the issues and also for doing changes which are requested by admins.

Anyone reading this post knows about more repositories can comment and increase this list.

Thank You. Hope it will help you !.

What is Word2Vec?

Rutik Bhoyar — Tue, 15 Jun 2021 17:47:06 +0000

Why we need word2vec?

As both TFIDF and BOW do not store semantic information. TFIDF gives importance to uncommon words.
Hence there is definitely a chance of overfitting.

What word2vec does?

In this word2vec specific model, each word is basically represented as a vector of 32 or more dimension instead of single number.

Here the semantic information and relation between different word is also preserved.

Word2vec is applied to huge amount of data mostly.

How to use?

from gensim.model import word2vec

After tokenizing and stopwords write the following code...

model=word2vec(senetnces,min_count=1)
words=model.wv

Thank You!

How to send email using PHP?

Rutik Bhoyar — Sat, 15 May 2021 08:26:22 +0000

Recently I started learning PHP. I was building a practice project where I want to send the email to my email address from the contact us page on my site. Then I wrote the following code.

I know many of the beginners struggle in this part so I am sharing the code so that one can use it with any HTML form they have on their site.

So the steps are:

Step 1: Create a HTML contact form

Step 2: Create the .php file when someone clicks the submit button it will go to the .php file which we defined in the form action.

<?php
//get data from form  
$name = $_POST['name'];
$email= $_POST['email'];
$message= $_POST['message'];
$to = "youremail@mail.com";
$subject = "Mail From website";
$txt ="Name = ". $name . "\r\n  Email = " . $email . "\r\n Message =" . $message;
$headers = "From: noreply@yoursite.com" . "\r\n" .
"CC: somebodyelse@example.com";
if($email!=NULL){
    mail($to,$subject,$txt,$headers);
}
//redirect
header("Location:thankyou.html");
?>

Hope you will find it helpful.

Types of Clustering Algorithms...

Rutik Bhoyar — Thu, 15 Apr 2021 11:57:18 +0000

We all know what is clustering. For revision.....

Clustering:It is the task of dividing the population or data points into several groups.

There are two types of clustering 1. Hard Clustering 2. Soft Clustering.

And there are four types of algorithms also which are...

1) Connectivity Model: As the name suggests, these are the models based on the definition that the data points closer in data space exhibit more similarity to each other than the data points lying farther away.

There are two approaches in this type...

I. Start with classifying all data points into separate clusters and then aggregating them as distance increases.

II. All the data points are classified as a single cluster and then partitioned as the distance increases.

Ex: Hierarchical Algorithm, etc.

2) Centroid Models: These are the iterative clustering algorithms in which the notion of the similarity is derived by the closeness of data points to the centroid of clusters.

Ex: K-Means Algorithm, etc.

3) Distribution Models: These clustering models are based on the definition of how probable is it that all the data points in the cluster belong to the same distribution. This type of model often leads to overfitting.

Ex: Expectation-Maximization Algorithms, etc.

4) Density Models: These models search the data space for areas of the varied density of data points in that data space.

Ex: DBSCAN & OPTICS, etc.

The short explanation for hard and soft clustering can be..

Hard Clustering: In this, each data point either belongs to a cluster complexity or not.
Soft Clustering: In this, instead of putting each data points into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.

Assumptions of Linear Regression

Rutik Bhoyar — Thu, 25 Mar 2021 16:56:25 +0000

Assumptions of Linear Regression

Due to the parametric nature of regression, it is restrictive in nature. Hence due to this, it fails to deliver good results with data sets which doesn't fulfil its assumptions.
Following are the assumptions:

Linear Relationship
No correlation of error terms
Constant Variance of error terms
No correlation among independent variables
Error normally distributed.

Now let's understand what this terms actually mean....

Linear or Additive Model: If you fit a model to a non-linear, non-additive dataset, the regression algorithm would fail to capture the trend mathematically, thus result in an inefficient model. Also, this will lead to an erroneous prediction on an unseen dataset.
No correlation of error terms: The presence of correlation in error terms drastically reduces model's accuracy. This is usually occurs in time series models where the next instant is dependent on previous instant. If the error terms are correlated , the estimated standard errors tend to underestimate the true standard error.
- This is also known as autocorrelation.
Constant Variance of error terms: This phenomenon exists when the independent variables are found to be moderately or highly correlated. In the model with correlated variables, it becomes difficult to find out which variable is actually contributing to predict the response variable.
- It also leads to increase in standard error.
- This is also called as Multicollinearity.
No Correlation among independent variables: The presence of non-constant variance in the error terms results in heteroskedasticity.
- Generally , non-constant variance arises in presence of outliers or extreme leverage values. Look like , these values get too much weight, thereby disproportionality influences the model's performance. When this phenomenon occurs, the confidence interval for out of sample prediction tends to be unrealistically wide or narrows.
- This is also known as heteroskedasticity.
Normal Distribution: If error terms are non-normally distributed , confidence intervals may become too wide or narrow.
- Once confidence interval becomes unstable, it leads to difficulty in estimating coefficients based on minimization of least squares.
- Presence of non-normal distribution suggests that there are a few unusual data points which must be studied closely to make a better model.

These are the assumptions you need to get a better linear model.

ANCOVA - Machine Learning

Rutik Bhoyar — Wed, 17 Mar 2021 16:49:57 +0000

What is ANCOVA?

ANCOVA is a blend of analysis of variance (ANOVA) and regression. It is similar to factorial ANOVA, in that it can tell you what additional information you can get by considering one independent variable (factor) at a time, without the influence of the others. It can be used as:

An extension of multiple regression to compare multiple regression lines,
An extension of analysis of variance.

Although ANCOVA is usually used when there are differences between your baseline groups (Senn, 1994; Overall, 1993), it can also be used in pretest/posttest analysis when regression to the mean affects your posttest measurement (Bonate, 2000). The technique is also common in non-experimental research (e.g. surveys) and for quasi-experiments (when study participants can’t be assigned randomly). However, this particular application of ANCOVA is not always recommended (Vogt, 1999).

Multiple Regression Analysis

Multiple regression analysis is used to see if there is a statistically significant relationship between sets of variables. It’s used to find trends in those sets of data.

Multiple regression analysis is almost the same as simple linear regression. The only difference between simple linear regression and multiple regression is in the number of predictors (“x” variables) used in the regression.

Simple regression analysis uses a single x variable for each dependent “y” variable. For example: (x1, Y1).
Multiple regression uses multiple “x” variables for each independent variable: (x1)1, (x2)1, (x3)1, Y1).
In one-variable linear regression, you would input one dependent variable (i.e. “sales”) against an independent variable (i.e. “profit”). But you might be interested in how different types of sales effect the regression. You could set your X1 as one type of sales, your X2 as another type of sales and so on.

Extension of Multiple Regression

When used as an extension of multiple regression, ANCOVA can test all of the regression lines to see which have different Y intercepts as long as the slopes for all lines are equal.

Like regression analysis, ANCOVA enables you to look at how an independent variable acts on a dependent variable. ANCOVA removes any effect of covariates, which are variables you don’t want to study. For example, you might want to study how different levels of teaching skills affect student performance in math; It may not be possible to randomly assign students to classrooms. You’ll need to account for systematic differences between the students in different classes (e.g. different initial levels of math skills between gifted and mainstream students).

Example

You might want to find out if a new drug works for depression. The study has three treatment groups and one control group. A regular ANOVA can tell you if the treatment works. ANCOVA can control for other factors that might influence the outcome. For example: family life, job status, or drug use.

Extension of ANOVA

As an extension of ANOVA, ANCOVA can be used in two ways (Leech et. al, 2005):

To control for covariates (typically continuous or variables on a particular scale) that aren’t the main focus of your study.
To study combinations of categorical and continuous variables, or variables on a scale as predictors. In this case, the covariate is a variable of interest (as opposed to one you want to control for).

Within-Group Variance

ANCOVA can explain within-group variance. It takes the unexplained variances from the ANOVA test and tries to explain them with confounding variables (or other covariates). You can use multiple possible covariates. However, more you enter, the fewer degrees of freedom you’ll have. Entering a weak covariate isn’t a good idea as it will reduce the statistical power. The lower the power, the less likely you’ll be able to rely on the results from your test. Strong covariates have the opposite effect: it can increase the power of your test.

Assumptions for ANCOVA

Assumptions are basically the same as the ANOVA assumptions. Check that the following are true before running the test:

Independent variables (minimum of two) should be categorical variables.
The dependent variable and covariate should be continuous variables (measured on an interval scale or ratio scale.)
Make sure observations are independent. In other words, don’t put people into more than one group.
Software can usually check the following assumptions.

Normality: the dependent variable should be roughly normal for each of category of independent variables.
Data should show homogeneity of variance.
The covariate and dependent variable (at each level of independent variable) should be linearly related.
Your data should be homoscedastic of Y for each value of X.
The covariate and the independent variable shouldn’t interact. In other words, there should be homogeneity of regression slopes.

Multiple regression with categorical independent variables or factors: Analysis of covariance (ANCOVA)

Analysis of covariance (ANCOVA) is a linear model that blends ANOVA and linear regression. ANCOVA evaluates whether population means of a dependent variable (DV) are equal across levels of a categorical independent variable (IV) often called a treatment, while statistically controlling for the effects of other quantitative or continuous variables that are not of primary interest, known as covariates (CV).

Now lets understand this with the help of example..
Step-1: Reading the dataset and importing required libraries.

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

try:
    df = pd.read_csv("../datasets/salary_table.csv")
except:
    url = 'https://github.com/duchesnay/pystatsml/raw/master/datasets/salary_table.csv'
    df = pd.read_csv(url)

print(df.head())

Step-2: Calculating Normality Test p-value.

import statsmodels.formula.api as smf
import statsmodels.stats.api as sms

lm = smf.ols('salary ~ experience', df).fit()
df["residuals"] = lm.resid

print("Normality test p-value %.5f" % sms.jarque_bera(lm.resid)[1])

ax = sns.displot(df, x='residuals', kind="kde", fill=True)
ax = sns.displot(df, x='residuals', kind="kde", hue='management', fill=True)

After the output you can see that, Normality assumption of the residuals can be rejected (p-value < 0.05). There is an effect of the "management" factor, take it into account.

Step-3: One-way ANCOVA

ANOVA: one categorical independent variable, i.e. one factor.
ANCOVA: ANOVA with some covariates.

import statsmodels.api as sm
oneway = smf.ols('salary ~ management + experience', df).fit()
df["residuals"] = oneway.resid
sns.displot(df, x='residuals', kind="kde", fill=True)
print(sm.stats.anova_lm(oneway, typ=2))
print("Normality test p-value %.3f" % sms.jarque_bera(oneway.resid)[1])

Distribution of residuals is still not normal but closer to normality. Both management and experience are significantly associated with salary.

Step-4:
Two-way AN(C)OVA

Ancova with two categorical independent variables, i.e. two factors.

twoway = smf.ols('salary ~ education + management + experience', df).fit()

df["residuals"] = twoway.resid
sns.displot(df, x='residuals', kind="kde", fill=True)
print(sm.stats.anova_lm(twoway, typ=2))

print("Normality test p-value %.3f" % sms.jarque_bera(twoway.resid)[1])

Normality assumption cannot be rejected. Assume it. Education, management and experience are significantly associated with salary.

Step-5:
Comparing two nested models

oneway is nested within twoway. Comparing two nested models tells us if the additional predictors (i.e. education) of the full model significantly decrease the residuals. Such comparison can be done using an 𝐹 -test on residuals:

print(twoway.compare_f_test(oneway)) # return F, pval, df

twoway is significantly better than one way

print(twoway.model.data.param_names)

print(twoway.model.data.exog[:10, :])

Step-6: Contrasts and post-hoc tests

Post hoc tests are an integral part of ANOVA. When you use ANOVA to test the equality of at least three group means, statistically significant results indicate that not all of the group means are equal. However, ANOVA results do not identify which particular differences between pairs of means are significant. Use post hoc tests to explore differences between multiple group means while controlling the experiment-wise error rate.

ttest_exp = twoway.t_test([0, 0, 0, 0, 1])
ttest_exp.pvalue, ttest_exp.tvalue
print(ttest_exp)

# Alternatively, you can specify the hypothesis tests using a string
twoway.t_test('experience')

# Post-hoc is salary of Master different salary of Ph.D? 
# ie. t-test salary of Master = salary of Ph.D.
print(twoway.t_test('education[T.Master] = education[T.Ph.D]'))

The code and the dataset is derived from the notebook of duchesnay. Hope it will help you because there is very few material regarding ANCOVA on the internet.

Machine Learning Resources..

Rutik Bhoyar — Mon, 08 Mar 2021 17:39:09 +0000

When the pandemic was taking over the world, 'Thanking God for whatever he has given to me' I started exploring Machine Learning. Fortunately with help of my friend I got an entry in one of the courses on Machine Learning for a month.

I was familiar with the Python language, so it helps me to identify the role of python in the domain. Then started to follow the way to study machine learning told by the tutor. But like other guys, I was also unable to keep myself on track.

But one thing was decided in me, that I have to dive into the world of machine learning. After two months, I got myself back in the business and started exploring the ML world. This motivation made me search for a way to study as I don't have any senior or friend around me who is keen towards Machine Learning World and also I belong to tier-3 college in India(joke part 😅).

I got some useful resources to help out other students like me, it's totally up to you guys to choose these resources or not, these resources are helping in my journey so I am sharing them with you.

Websites:
* Analytics Vidhya (complete roadmap for begineers).
* GeekforGeeks (Machine Learning Section).
* Google Developers site(tensorflow).
* freecodecamp(tensorflow).
* Course by Stanford University.
* Courses on Udemy and Coursera.

Blogs:
* Blogs on Analytics Vidhya.
* Blogs on Medium and DEV platforms.
* Blogs on towardsdatascience.

Join Twitter and follow #machinelearning. 
Follow some good accounts...
Like 1.Santiago
     2.Pratham Masoom, etc.

Join Machine Learning Communities on LinkedIn.
Like 1.Machine Learning India, etc.

Read research papers on Machine Learning field to keep motivate.

Join SlideShare.net
And read some useful slides on the platform regarding ML.

YouTube Channels:
* Krish Naik(I am following his path)
* CodeBasics
* sentdex
* Nptel Courses

Tip: Try to read documentations on various topics.

Excuse me if I have left any useful resource. Hope it will help you also.

Also I am not master in Machine Learning , I am just sharing the resources which are being helpful to me in studying machine learning.

Thank You!!...

Getting Started with Node.js

Rutik Bhoyar — Sun, 07 Mar 2021 16:49:23 +0000

I hope you guys enjoyed my last post on transparent simple html form. In this post we will talk about one of the most popular backend server environment.

What is Node.js?

Node.js is an open-source, cross-platform, JavaScript runtime environment that executes JavaScript code outside of a web browser.

Node.js runs single-threaded, non-blocking, asynchronously programming, which is very memory efficient.

Use Cases of Nodejs:

Real-Time Apps
Data-Intensive Apps
To build Scalable Backend
Non-Blocking Asynchronous Thread

Getting Started

Now after downloading Nodejs on the computer let's try to create hello world program. Remember, Node.js files must be initiated in the "Command Line Interface" program of your computer. For Windows users, press the start button and look for "Command Prompt", or simply write "cmd" in the search field.

var http = require('http');

http.createServer(function (req, res) {
  res.writeHead(200, {'Content-Type': 'text/html'});
  res.end('Hello World!');
}).listen(8080);

You can choose any port available on computer to listen.
Now start your command line interface, write node myfirst.js and hit enter:
C:\Users\User Name>node myfirst.js

Now let's try to understand the terms in our first program.

Modules(require):

A set of functions you want to include in your application.
Node.js has a set of built-in modules which you can use without any further installation.

To include a module, use the require() function with the name of the module.
var http = require('http');
The first argument of the res.writeHead() method is the status code, 200 means that all is OK, the second argument is an object containing the response headers.

Now your application has access to the HTTP module, and is able to create a server.

http.createServer(function (req, res) {
  res.writeHead(200, {'Content-Type': 'text/html'});
  res.end('Hello World!');
}).listen(8080);

So this was the introduction to Nodejs using simple Hello World program.

Most of the contents pf this post are from w3schools.com so for any elaborated information you can https://www.w3schools.com/nodejs/nodejs_intro.asp refer this page.
I hope it will help you to quickly get started learning Node.js.

Happy Coding!!...

HTML Transparent Form

Rutik Bhoyar — Sun, 28 Feb 2021 16:02:47 +0000

Every time I used to see any transparent form somewhere in websites , always desired to built one by own. I know there can many budding developers are out there like me to help them here is one simple project for them.

Tags Used
1.The <div> tag defines a division or a section in an HTML document.

2.The <form> tag is used to create an HTML form for user input.

3.The <script> tag is used to embed a client-side script (JavaScript).

HTML CODE

<html>
<head>
    <title>Form Html</title>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <script src="https://kit.fontawesome.com/f7a6f8989f.js" crossorigin="anonymous"></script>
    <link rel="stylesheet" type="text/css" href="style.css">
</head>
<body>
<form id="myForm" action="#">
<div class="container">
    <h1>Login</h1>
    <div class="box">
        <i class="fa fa-user"></i>
        <input type="text" name="email" id="email" placeholder="Enter Your Name">
    </div>
    <div class="box">
        <i class="fa fa-unlock"></i>
        <input type="password" name="password" id="password" placeholder="Enter Your Password">
    </div>
    <button class="btn" onclick="myFunction()">Submit  </button>

</div>
</form>
<script>
function myFunction() {
  document.getElementById("myForm").submit();
}
</script>

</body>
</html>

Now we will do some CSS stuff as follows. You can choose image of your choice. I will not explain CSS attributes for that you can refer (https://w3schools.com).

*{
    margin: 0;
    padding: 0;
}
@media only screen and (max-width: 1800px){
body{
    background:url('pexels.jpg') no-repeat center center fixed;
    background-size: cover;

}
}

.container{
    /*border:4px solid black;*/
    color: black;
    position: absolute;
    top: 28%;
    left: 20%;

}

.container h1{
    width: 50%;
    display: inline-block;
    font-size: 35px;
    border-bottom: 5px solid purple;
    padding: 12px;
    margin-bottom: 25px;
    text-align: center;
}

.box{
    width: 100%;
    /*border: 2px solid grey;*/
    border-bottom: 2px solid purple;
    margin: 12px 0;
}

.box input{
    background: none;
    width: 90%;
    padding: 10px 0;
    margin:11px 0; 
    font-size: 20px;
    border:none;
    outline:none;
    color:black;      
}

.btn{
    font-size: 15px;
    font-weight: bold;
    color: grey;
    background: none;
    cursor: pointer;
    outline:none;
    padding: 10px 21px;
    margin: 12px 0;
    border-radius: 10px;
}

box i{
    /*padding: 0 12px;*/
    width: 25px;
    float: left;
    text-align: center;
}

.btn:hover{
    opacity: 0.5;
    background: white;
    color: skyblue;
}

And now we are ready with our transparent html form.

Hope it will help you.

Password Creator GUI using PyCharm

Rutik Bhoyar — Sun, 21 Feb 2021 11:45:18 +0000

It is a tool that generates passwords based on the given guidelines that you set to create an unpredictable strong password for your accounts.

The Password generator tool creates a random and customized password for users that helps them to create a strong password which provides greater security.

Prerequisites
To build this project we will use the basic concept of python and libraries – Tkinter, pyperclip, random, string.

1.Tkinter is a standard GUI library and is one of the easiest ways
to build a GUI application.
2.pyperclip module allows us to copy and paste text to and from
the clipboard to your computer
3.The random module can generate random numbers
4.string module contains a number of functions to process the
standard python string.

pip install tkinter
pip install pyperclip
pip install random
pip install strings

Steps

Import Libraries
The first step is to import libraries
Initialize Window
Tk() initialized tkinter which means window created
geometry() set the width and height of the window
resizable(0,0) set the fixed size of the window
title() set the title of the window
Label() widget use to display one or more than one line of
text that users can’t able to modify.
root is the name which we refer to our window
text which we display on the label
font in which the text is written
pack organized widget in block
Select Password Length
Function to Generate Password
Function to Copy Password

from tkinter import *
import random, string
import pyperclip
rutik_root = Tk()
#width x height
rutik_root.geometry("400x400")
#width, height
rutik_root.minsize(200,100)
rutik_root.maxsize(1200,800)
rutik_root.title("Rutik - Password Generator")

Label(rutik_root,text="Password Generator", font="arial 15 bold").pack()
Label(rutik_root,text="Rutik", font="arial 15 bold").pack(side=BOTTOM)

pass_label=Label(rutik_root,text="Password Length", font="arial 15 bold").pack()
pass_len=IntVar()
length=Spinbox(rutik_root,from_ =8, to_ = 32, textvariable = pass_len, width = 15).pack()

pass_str = StringVar()
def Generator():
    password = ''

    for x in range(0,4):
        Password = random.choice(string.ascii_uppercase)+random.choice(string.ascii_lowercase)+random.choice(string.digits)+random.choice(string.punctuation)
    for y in range (pass_len.get()- 4):
        password = password+random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits + string.punctuation)
    pass_str.set(password)

Button(rutik_root,text =" Generate Password", command = Generator).pack(pady=5)
Entry(rutik_root, textvariable = pass_str).pack()

def Copy_password():
    pyperclip.copy(pass_str.get())
Button(rutik_root,text =" Copy to Clipboard", command = Copy_password).pack(pady=5)
rutik_root.mainloop()

And finally you will be getting an GUI Window like below.