DEV Community: Bidut Sharkar Shemanto

GitHub Action CI/CD using UV Package Manager (requirements.txt not required)

Bidut Sharkar Shemanto — Fri, 09 May 2025 20:06:37 +0000

I was working in django backend project and wanted to setup github action using uv package manager because I have shifted from pip to uv.

The default template of Django github action is using pip and need requirements.txt file.

The problem is that after add new packages you have to add dependencies each time in requirements.txt file. uv solve this problem.

But I did not find any blog that shows how to write the yml file only using uv. This blog solve this problem, enjoy!:

name: Backend CI/CD Pipeline

on:
  push:
    branches: [ "main" ]
  pull_request:
    branches: [ "main" ]

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version-file: backend/.python-version

      - name: Install uv
        uses: astral-sh/setup-uv@v5

      - name: Create virtual environment
        working-directory: ./backend
        run: uv venv .venv

      - name: Install dependencies with uv
        working-directory: ./backend
        run: uv sync

      - name: Run Django tests
        working-directory: ./backend
        run: uv run python manage.py test

uv sync command install the dependencies using the uv.lock file

you have to add uv run in every command related to django, because we are not activating the virtual environment

Questions for React job interview

Bidut Sharkar Shemanto — Fri, 25 Apr 2025 20:15:19 +0000

Explain the difference between request and response interceptors in Axios.
How would you add a custom header to all Axios requests using interceptors?
What is a JWT, and what are its three components?
Explain the difference between signed and encrypted JWTs.
How do you store JWTs on the client side? What are the trade-offs between localStorage and cookies?
What measures can you take to prevent JWT from being tampered with?
How can you prevent a JWT from being stolen (e.g., token hijacking)?
What is JWT, and why is it used?
What are the three parts of a JWT? Explain their purpose.
How does JWT differ from session-based authentication?
How can you include a JWT in the headers of an Axios request?

How to Build and Publish a Python Package on PyPI: My Experience with `my_eda_helper`

Bidut Sharkar Shemanto — Fri, 07 Feb 2025 07:38:10 +0000

Publishing your first Python package on PyPI is an exciting milestone for any developer. It’s a great way to share your work with the world, contribute to the open-source community, and showcase your skills. In this blog, I’ll walk you through the step-by-step process I followed to publish my package, my_eda_helper, which simplifies Exploratory Data Analysis (EDA). I’ll also share tips on handling errors, publishing updates, and best practices. Let’s dive in! 🚀

What is `my_eda_helper`?

my_eda_helper is a Python package designed to streamline the EDA process. It provides helper functions for tasks like:

✅ Missing data analysis

✅ Correlation heatmaps

✅ Hypothesis testing

✅ Interactive visualizations

The goal is to reduce repetitive tasks and allow users to focus on deriving insights rather than writing boilerplate code.

Step-by-Step Guide to Publishing a Python Package

1. Plan Your Package

Before writing any code, plan what your package will do. For my_eda_helper, I wanted to automate common EDA tasks and make them reusable.

2. Organize Your Project Structure

A well-structured project is crucial for maintainability. Your project should have the following structure:

my_eda_helper/                  <-- Root folder
├── my_eda_helper/              <-- Package folder
│   ├── __init__.py             <-- Makes it a package
│   └── core.py                 <-- Main code
├── setup.py                    <-- Package metadata
├── README.md                   <-- Documentation
├── LICENSE                     <-- License file
└── tests/                      <-- Optional: Tests

📌 Explanation:

__init__.py: Marks the folder as a Python package. Add from .core import * to expose functions.
core.py: Contains the main code (e.g., functions for EDA).
setup.py: Defines package metadata and dependencies.
README.md: Provides installation and usage instructions.
LICENSE: Defines the package license.
tests/: (Optional) Contains unit tests for your package.

3. Write `setup.py`

The setup.py file is crucial for packaging your project. Here’s what mine looks like:

from setuptools import setup, find_packages

setup(
    name="my_eda_helper",
    version="0.1.0",  # Start with 0.1.0 for the first release
    author="Your Name",
    author_email="your.email@example.com",
    description="A helper package for Exploratory Data Analysis (EDA)",
    long_description=open("README.md").read(),
    long_description_content_type="text/markdown",
    url="https://github.com/shemanto27/eda-helper-py",
    packages=find_packages(),
    install_requires=[
        "numpy",
        "pandas",
        "seaborn",
        "matplotlib",
        "scipy",
        "statsmodels",
        "scikit-learn",
        "wordcloud",
        "IPython",
    ],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
    python_requires=">=3.6",
)

📌 Why?

This file tells PyPI how to install and use your package.

4. Write a `README.md`

Your README.md should include:

A short description
Installation instructions
Usage examples

Here’s a snippet from mine:

# My EDA Helper

A Python package to simplify Exploratory Data Analysis (EDA).

## Installation

bash
pip install my_eda_helper


## Usage

python
import my_eda_helper as eda

Missing data analysis

missing_data = eda.missing_info(df)
print(missing_data)

5. Build Your Package

Before uploading to PyPI, build your package:

pip install build
python -m build

This creates a dist/ folder containing .tar.gz and .whl files.

6. Upload to PyPI

Step 1: Create a PyPI Account

Step 2: Generate an API Token

Go to Account Settings > API Tokens
Create a new token

Step 3: Upload Your Package

pip install twine
python -m twine upload dist/*

Use __token__ as the username.
Paste your API token as the password.

✅ Your package is now live on PyPI! 🎉

7. Test Your Package

To confirm the installation works, install and import it:

pip install my_eda_helper
python -c "import my_eda_helper; print(dir(my_eda_helper))"

Handling Errors and Publishing Updates

Common Errors

❌ Package Not Found:

Ensure the package name is unique on PyPI.
Double-check the name field in setup.py.

❌ Missing Files:

Ensure all necessary files (__init__.py, core.py, README.md) are included.

❌ Version Conflict:

Increment the version number in setup.py before uploading a new version.

Publishing a New Version

If you want to update your package:

1️⃣ Update the version number in setup.py (e.g., 0.1.1).

2️⃣ Rebuild the package:

python -m build

3️⃣ Upload the new version:

python -m twine upload dist/*

✅ Done! Your new version is live.

Final Thoughts

Publishing a Python package is a rewarding experience. Whether it’s a small utility or a full-fledged library, your work can help others and grow your skills.

If you found this guide helpful, follow me for more tips and projects:

🔗 LinkedIn: https://www.linkedin.com/in/shemanto/

🐍 GitHub: http://github.com/shemanto27/

Happy coding! 🚀

How to Upload Folder Files from Google Colab to GitHub for Continuous Machine Learning Project

Bidut Sharkar Shemanto — Sun, 02 Feb 2025 06:33:57 +0000

Google Colab provides a simple way to write and execute Python code using cloud resources. However, for continuous machine learning, it's essential to keep your code version-controlled and synced with GitHub. This tutorial shows how to upload a folder from Google Colab to GitHub effortlessly.

Prerequisites

A GitHub account (Sign up here)
A Google Drive account
A GitHub repository where you want to upload files

Steps to Upload a Folder from Colab to GitHub

1. Generate a GitHub Token

To push code to GitHub from Colab, you need a Personal Access Token.

Go to GitHub Tokens
Click Generate new token (classic)
Select necessary permissions: repo (for full repo access)
Generate and copy the token (you will need it later)

2. Set Up Google Colab for GitHub Upload

Copy and run the following code in a Colab notebook:

# Replace with your details
token = 'your_github_token'
user_name = 'your_github_username'
repo_name = 'your_repository_name'
code_folder = '/content/drive/MyDrive/Colab Notebooks/your_project_folder'

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive', force_remount=True)

# Check Git version
!git version

# Set up Git configuration
!git config --global user.email "your_email@example.com"
!git config --global user.name "{user_name}"

# Clone GitHub repo
!git clone https://{token}@github.com/{user_name}/{repo_name}.git

# Sync Colab folder with GitHub repo
!rsync -av "{code_folder}/" "/content/{repo_name}"

# Push to GitHub
%cd /content/{repo_name}
!git add .
!git commit -m "Initial commit"
!git remote add origin https://{token}@github.com/{user_name}/{repo_name}.git
!git push -u origin main

Explanation of the Code

Mounts Google Drive: Allows access to files stored in Google Drive.
Sets Git Configuration: Defines user details for Git operations.
Clones the GitHub repository: Ensures the local version is up-to-date.
Copies files using rsync: Syncs the specified folder from Google Drive to the cloned repo.
Commits and Pushes changes: Updates GitHub with the latest changes.

Why Use This Method?

Automates code synchronization between Colab and GitHub.
Allows continuous machine learning workflows.
Keeps project versions safe and trackable.

Follow Me

LinkedIn: https://www.linkedin.com/in/shemanto/
GitHub: http://github.com/shemanto27

Happy coding! 🚀

Building a Cooking Expert AI Agent with Phidata and Django Rest Framework (DRF)

Bidut Sharkar Shemanto — Fri, 20 Dec 2024 18:33:53 +0000

Artificial Intelligence (AI) is reshaping industries, and the concept of AI agents is leading this transformation. Imagine a world where billions of AI agents assist us in every field of work – from healthcare and education to cooking and transportation. Even Mark Zuckerberg has spoken about the rise of AI agents and their potential to revolutionize how we interact with technology. In this blog, I’ll walk you through the process of building a custom AI agent api using Phidata and Django Rest Framework (DRF). This agent will act as a cooking and food expert, answering your culinary questions and providing helpful tips and recipes.

Github Repo Link for requirement file and code

What is an AI Agent?

Imagine you have a virtual assistant in your kitchen named ChefBot. You can ask ChefBot, "How do I bake a chocolate cake?" or "What can I cook with chicken and spinach?" ChefBot understands your questions, uses its knowledge of recipes, and provides accurate and helpful answers. That’s what an AI agent does.

An AI agent is a software entity that performs tasks intelligently by interacting with users, tools, and other systems. Unlike an AI model, which is just a mathematical function trained on data, an agent is a complete system that leverages one or more models to achieve specific objectives. It combines the following components:

AI Model: The underlying machine learning model, such as GPT or Groq, that generates intelligent responses.
Tools: Plugins or functionalities the agent can use, such as web search or APIs.
Knowledge Base: A storage system for the agent’s memory or external knowledge.
Storage: A backend system, like a database, for session management or historical data.

In this project, we’ll use Phidata to build an AI agent specialized in food and cooking. Our agent will provide recipes, cooking tips, and answer food-related queries.

What Makes Phidata Special?

Phidata simplifies the creation of AI agents by providing:

Model Integration:
- Easily integrate pre-trained models like OpenAI GPT or Groq.
- Customize model behavior using descriptions and instructions.
Tools:
- What are tools? Tools extend the agent’s abilities by connecting it to external functionalities. For example:
  - A web search tool can help the agent look up recipes or cooking methods online if its internal knowledge is insufficient.
  - A unit conversion tool could allow the agent to convert between cups and grams seamlessly.
- Analogy: Think of tools as specialized gadgets your ChefBot uses to enhance its cooking knowledge, like a spice rack or a cookbook.
Knowledge Base:
- What is a knowledge base? It is where the agent stores and retrieves information. For example:
  - A vector database like PgVector can store recipes and their ingredients in a way that the agent can search and retrieve related information efficiently.
  - Semantic search in the knowledge base enables the agent to answer "What desserts can I make with chocolate?" by looking up stored recipes.
- Analogy: Imagine ChefBot has a personal diary of recipes and tips it can flip through to answer your questions.
Storage:
- What is storage? This refers to databases used for session management or storing interaction history. For example:
  - A Postgres database can store past interactions, so the agent remembers what you asked last time.
  - This is useful for continuity in conversations or analyzing frequently asked questions.
- Analogy: ChefBot keeps a logbook of all your past recipe requests to serve you better in the future.

By combining these features, Phidata allows developers to build comprehensive, intelligent agents tailored to specific needs.

Project Overview

We’ll build a Cooking Expert AI Agent that:

Accepts cooking-related instructions from the user via a REST API.
Processes the instruction using a pre-trained AI model (e.g., Groq).
Returns the agent’s intelligent response with helpful food-related information.

Technologies Used

Django: Web framework for building the API.
Django REST Framework (DRF): For creating and managing API endpoints.
Phidata: To define and interact with the AI agent.

Step-by-Step Guide

0. create python environment

Open your terminal or command prompt.

Create the environment,I am using conda package manager:

conda create -n myenv python=3.12 -y

activate the environment

conda activate myenv```
{% endraw %}


### **1. Setting Up the Project**
Install the required dependencies:
{% raw %}

```bash
pip install django djangorestframework phidata python-dotenv groq

Start by creating a new Django project and app:

django-admin startproject my_project .
django-admin startapp api

Add rest_framework and api to your INSTALLED_APPS in settings.py:

INSTALLED_APPS = [
    ...
    'rest_framework',
    'api',
]

2. Defining the AI Agent

Create a folder named agents/ to store the agent logic. In agents/agent.py, define your custom agent:

from phi.agent import Agent
from phi.model.groq import Groq

from dotenv import load_dotenv
load_dotenv()

chef_agent = Agent(
    model=Groq(id='llama-3.3-70b-versatile'),
    tools=[],
    show_tool_calls=True,
    description="You are a recipe expart. You will be given a recipe and you have to answer the questions related to it.",
    instructions=[""],
)

def process_instruction(instruction: str) -> str:
    response = chef_agent.run(instruction, stream=False)
    return response.content

3. Creating the API

Serializers

In api/serializers.py, define the input and output serializers:

from rest_framework import serializers

class InstructionSerializer(serializers.Serializer):
    instruction = serializers.CharField(max_length=500)

class ResponseSerializer(serializers.Serializer):
    response = serializers.CharField(max_length=10000)

Views

In api/views.py, implement the API view:

from django.shortcuts import render

# Create your views here.
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status

from .serializers import *
from agents.agent import process_instruction

class AgentView(APIView):
    def post(self, request):
        serializer = instruction_serializer(data=request.data)
        # input validation
        if serializer.is_valid():
            instruction = serializer.validated_data['instruction']

            #process the instruction using agent
            try:
                agent_response = process_instruction(instruction)

            except Exception as e:
                return Response(str(e), status=status.HTTP_500_INTERNAL_SERVER_ERROR)
            #return the response
            response = {"response" : agent_response}
            response_data = response_serializer(data=response)
            if response_data.is_valid():
                return Response(response_data.data, status=status.HTTP_200_OK)
        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)

Routing

In api/urls.py, define the endpoint:

from django.urls import path
from .views import AgentView

urlpatterns = [
    path('agent/', AgentView.as_view(), name='agent-api'),
]

Include this in the main urls.py:

from django.urls import path, include

urlpatterns = [
    path('api/', include('api.urls')),
]

4. Running the Project

Run the Django development server:

python manage.py runserver

Send a POST request to http://127.0.0.1:8000/api/agent/ with a JSON body:

{
    "instruction": "How do I bake a chocolate cake?"
}

The response will be:

{
    "response": "Baking a chocolate cake requires the following steps: ..."
}

5. Key Learnings

Phidata: Simplifies AI integration with pre-built tools and models.
Django REST Framework: Provides a robust and scalable way to create APIs.
Dotenv: Keeps sensitive configurations secure and manageable.

Next Steps

Add tools to your agent for more functionality (e.g., web search, database queries).
Deploy your application using ASGI servers like Uvicorn or Daphne for better scalability.
Extend the agent to include memory or knowledge base integration.

Building an AI agent with Phidata and Django REST Framework unlocks a wide range of possibilities for creating intelligent web applications. Let your imagination run wild and start building AI-powered tools today!

Do you have questions or ideas for improvement? Feel free to share your thoughts in the comments!

(Help of ChatGPT was taken to write this blog, code is written and tested all by myself.There may be some mistakes in writings, follow this github link for actual codes)

How to Check Whether Data is Linear or not? 😕

Bidut Sharkar Shemanto — Mon, 02 Dec 2024 06:52:06 +0000

*Linear function: *
Can be simply defined as a function which always follows the principle of :

input/output = constant.

A linear equation is always a polynomial of degree 1 (for example x+2y+3=0). In the two dimensional cases, they always form lines; in other dimensions, they might also form planes, points, or hyperplanes. Their “shape” is always perfectly straight, with no curves of any kind. This is why we call them linear equations.

Non-linear function:
Any function that is not linear is simply put, Non-linear. Higher degree polynomials are nonlinear. Trigonometric functions (like sin or cos) are nonlinear. Square roots are nonlinear.

In case you are dealing with predicting numerical value, the technique is to use scatter plots and also apply simple linear regression to the dataset, and then check the least square error. If the least square error shows high accuracy, it can be implied that the dataset is linear in nature, else the dataset is non-linear.

Setting Up MLflow in Google Colab: A Beginner-Friendly Guide

Bidut Sharkar Shemanto — Sat, 30 Nov 2024 08:56:48 +0000

Tracking machine learning experiments is essential for improving model performance and maintaining reproducibility. MLflow is a powerful tool that helps track experiments, log parameters, metrics, and artifacts, and even deploy models. In this blog, I'll guide you through setting up MLflow in Google Colab, complete with an accessible dashboard using ngrok.

Why Use MLflow in Google Colab?

Google Colab offers a free environment to run Python notebooks. Combining it with MLflow enables you to:

Track your model's performance over time.
Save and organize metrics, parameters, and artifacts for various experiments.
Share your experiment dashboard with collaborators using ngrok.

Step 1: Installing MLflow and Dependencies
First, we need to install MLflow and ngrok. Run the following commands in your Colab notebook:

!pip install mlflow -q
!pip install pyngrok -q

Step 2: Starting the MLflow Server
Google Colab doesn’t natively support localhost connections. To overcome this, we’ll use ngrok to expose the MLflow UI on the internet.

Here’s the complete setup:

import mlflow
import subprocess
from pyngrok import ngrok, conf
import getpass

# Define the MLflow tracking URI with SQLite
MLFLOW_TRACKING_URI = "sqlite:///mlflow.db"

# Start the MLflow server using subprocess
subprocess.Popen(["mlflow", "ui", "--backend-store-uri", MLFLOW_TRACKING_URI, "--port", "5000"])

# Set MLflow tracking URI
mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

# Set or create an experiment
mlflow.set_experiment("BD House Price Prediction")

# Set up ngrok for exposing the MLflow UI
print("Enter your authtoken, which can be copied from https://dashboard.ngrok.com/auth")
conf.get_default().auth_token = getpass.getpass()

# Expose the MLflow UI on port 5000
port = 5000
public_url = ngrok.connect(port).public_url
print(f' * ngrok tunnel "{public_url}" -> "http://127.0.0.1:{port}"')

we will get an ngrok tunnel url after running this command.go to that url and you will find your experiment has been setup.from there you can see all your expected results

Step 3: Logging Experiments
You can log metrics, parameters, and even artifacts (e.g., models or plots) in MLflow. Here’s an example:

# Start an MLflow run
with mlflow.start_run():
    # Log parameters and metrics
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("rmse", 0.25)
    print("Run logged successfully!")

Congratulations! You’ve successfully set up MLflow in Google Colab. With this setup, you can track experiments, log results, and share your work effortlessly. MLflow’s flexibility and ease of use make it a must-have tool for any data scientist or machine learning enthusiast.

Follow me on LinkedIn and GitHub

Have fun experimenting and tracking your models like a pro! 🚀

New Look of my GitHub README Profile✨

Bidut Sharkar Shemanto — Tue, 20 Aug 2024 06:41:53 +0000

🚀 Just redesigned my GitHub Profile README! 🎨 Kept it simple yet catchy, with a focus on showcasing my skills. Check it out and let me know your thoughts!
👉 www.github.com/shemanto27

GitHub #Coding #Developer

HTML, CSS Interview Question&Answer

Bidut Sharkar Shemanto — Sat, 06 Jul 2024 12:20:49 +0000

1) What Do You Understand By The Universal Selector?

Answer:
The universal selector (*) in CSS applies styles to all elements on a web page. It is used to set a common style for all elements without needing to specify each one individually.

2) Differentiate Between The Use Of ID Selector And Class Selector.

Answer:

ID Selector (#): Used to style a single, unique element. Each ID must be unique within a document.
Class Selector (.): Used to style multiple elements that share the same class. Multiple elements can have the same class.

3) How Can You Use CSS To Control Image Repetition?

Answer:
CSS property background-repeat controls image repetition. For example:

background-repeat: repeat; repeats the background image both horizontally and vertically.
background-repeat: no-repeat; prevents the background image from repeating.
background-repeat: repeat-x; repeats the image horizontally.
background-repeat: repeat-y; repeats the image vertically.

4) Are The HTML Tags And Elements The Same Thing?

Answer:
No, HTML tags and elements are not the same.

Tags: The code surrounded by angle brackets, such as <div>.
Elements: The combination of the opening tag, content, and closing tag, such as <div>Content</div>.

5) Difference Between Inline, Block, And Inline-Block Elements. Is It Possible To Change An Inline Element Into A Block-Level Element?

Answer:

Inline Elements: Do not start on a new line and only take up as much width as necessary (e.g., <span>, <a>).
Block Elements: Start on a new line and take up the full width available (e.g., <div>, <p>).
Inline-Block Elements: Behave like inline elements but can have width and height set like block elements.

Yes, it is possible to change an inline element into a block-level element using CSS:

.element {
    display: block;
}

Git And Github Interview Questions (& Answer) For Beginners

Bidut Sharkar Shemanto — Wed, 03 Jul 2024 16:05:46 +0000

Question-1: What is Git? What are the advantages of using Git?

Answer:
Git is a distributed version control system used to track changes in source code during software development. It allows multiple developers to work on a project simultaneously. Advantages include version tracking, branching and merging capabilities, collaboration facilitation, and offline work support.

Question-2: What do you understand by the term "Version Control System"?

Answer:
A Version Control System (VCS) is software that helps manage changes to documents, computer programs, large websites, and other collections of information. It allows multiple users to collaborate, track history, revert to previous versions, and manage branches of projects.

Question-3: What is the difference between Git and GitHub?

Answer:
Git is a version control system used for tracking changes in files, while GitHub is a web-based platform that uses Git for version control and provides a collaborative environment for hosting Git repositories, project management, and other features.

Question-4: Name a few Git commands with their functions.

Answer:

git init: Initializes a new Git repository.
git clone: Clones an existing repository.
git add: Adds changes to the staging area.
git commit: Records changes to the repository.
git push: Uploads local repository content to a remote repository.
git pull: Fetches and merges changes from a remote repository to the local repository.
git status: Shows the status of changes in the working directory and staging area.
git branch: Lists, creates, or deletes branches.

Question-5: What is the difference between git fetch and git pull?

Answer:
git fetch downloads changes from a remote repository but does not merge them into the local repository. It updates the remote tracking branches. git pull fetches changes from a remote repository and immediately merges them into the local branch.

My Web Development Journey Day-3: Git & Github 🎨

Bidut Sharkar Shemanto — Wed, 03 Jul 2024 15:19:32 +0000

In my third day of mission webdevelopment, I learned about Git and Git hub.To be honest I already had idea about github and already worked with it.But in this module I get to know how to make brach and work with it.

Then I learned more about CSS gradient.I learned-
1)Adding google font in css
2)CSS Gradients
3)Transitions
4)Flex Box
5) Justify-content vs align-item

I will write a separate blog on interview questions on Git and Github.

My Web Development Journey Day-2: CSS 🎨

Bidut Sharkar Shemanto — Tue, 02 Jul 2024 11:09:32 +0000

In module-2 I learned CSS.The topics that I covered:

1)3 way of CSS
2)CSS Syntex
3)Text-alignment
4)CSS Structure
5)CSS Selectors
6)ID vs Class
7)CSS Background Image
8)CSS Margin and Padding
9)CSS Box Shadow
10)CSS Box Model
11)Display: Inline, Block, Inline-block

I practised this by my own:

This the practise code:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>My Reading List</title>
    <link rel="shortcut icon" href="./images/book.png" type="image/x-icon">
    <link rel="stylesheet" href="./styles/book.css">
    <link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&display=swap" rel="stylesheet">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Playwrite+US+Modern:wght@100..400&display=swap" rel="stylesheet">
</head>
<body>
    <div id="portion_1">
    <div class="section_1">
        <h1 id="name">Hi, I'm Shemanto Sharkar 👋</h1>
        <p id="welcome">welcome to my website of Book List!</p>

    </div>
    <div class="section_2">
        <button class="button"><a href="">My LinedIn</a></button>
        <button class="button"><a href="">My Facebook</a></button>
        <button class="button"><a href="">My Youtube</a></button>
    </div>
    <div class="section_3">
        <img id="profile" src="./images/profile.png" alt="">
    </div>
</div>

    <div id="portion_2">
        <p>I am studying Energy Science and Engineering at KUET. I am in my last year.Now I am taking web development couse by programming Hero.I took this course becouse it is by Jhonkar Mahbub bhai.It will help me to get a job after I am graduated in 2025.</p>
    </div>

    <div id="portion_3">
        <img class="book" src="./images/download (1).jfif" alt="">
        <img class="book" src="./images/download.jfif" alt="">
        <img class="book" src="./images/images.jfif" alt="">
    </div>
</body>
</html>

name {

font-family: "Playwrite US Modern", cursive;
text-align: center;
color: #000000;
font-weight: bold;
font-size: 40px;
margin-bottom: 0%;

}

welcome{

text-align: center;
font-family: "Poppins", sans-serif;
font-weight: bold;
color: #595959;
margin-top: 0%;
font-size: 30px;
}

.section_2{
text-align: center;
margin: 5%;

}

.button{
background-color: #ffea3d;
color: #ffffff;
font-family: "Poppins", sans-serif;
font-weight: bold;
font-size: 20px;
padding: 10px 20px;
border: none;
border-radius: 10px;
cursor:pointer;
}

a{
text-decoration: none;
color: #000000;
}

profile{

display: block;
width: 300px;
margin: 0 auto;

}

portion_2{

background-color: #ffea3d;
font-family: "Poppins", sans-serif;
font-size: medium;
padding: 2%;
margin: 5%;
border-radius: 10px;
text-align: center;

}

.book{
display: inline-block;
width: 200px;
padding: 10px;
margin-left: auto;
margin-right: auto;
border: 10px solid #ffea3d;
border-radius: 5%;
}

portion_3{

text-align: center;

}

DEV Community: Bidut Sharkar Shemanto

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2. Organize Your Project Structure

3. Write setup.py

4. Write a README.md

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6. Upload to PyPI

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Step 3: Upload Your Package

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My Web Development Journey Day-2: CSS 🎨

name {

welcome{

profile{

portion_2{

portion_3{

What is `my_eda_helper`?

3. Write `setup.py`

4. Write a `README.md`