DEV Community: Ankit Sahu

Unit Testing in Python

Ankit Sahu — Mon, 31 Mar 2025 07:29:34 +0000

A concise guide to writing unit tests, mocking, and measuring test coverage in Python using unittest library.

Unit testing ensures code reliability. This guide covers:
✅ Writing unit tests with unittest
✅ Mocking dependencies
✅ Measuring test coverage

📂 Project Structure

Organize test cases effectively:

project_root/
│── src/
│   ├── utils.py  # Utility functions
│   ├── operations.py  # Business logic
│── tests/
│   ├── test_operations.py  # Test cases

✅ Writing Tests

📌 `src/operations.py`

def add(a, b):
    return a + b

📌 `tests/test_operations.py`

import unittest
from src.operations import add

class TestMathOperations(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(5, -12), -7)

if __name__ == "__main__":
    unittest.main()

Run tests:

python -m unittest discover -s tests

🔄 Setup & Teardown

import unittest
from io import StringIO
from src.utils import say_hello
from unittest.mock import patch

class TestGreetings(unittest.TestCase):
    def setUp(self):
        print("SETUP")

    def tearDown(self):
        print("TEARDOWN")

    def test_say_hello(self):
        with patch('sys.stdout', new=StringIO()) as fake_out:
            say_hello()
            self.assertEqual(fake_out.getvalue().strip(), "Hello World")

🔍 Mocking with `unittest.mock`

# src/data_fetcher.py
def get_status():
    return "Live"

def fetch_data():
    return {"status": get_status()}

# tests/test_data_fetcher.py
import unittest
from unittest.mock import patch
from src.data_fetcher import fetch_data

class TestDataFetcher(unittest.TestCase):
    @patch("src.data_fetcher.get_status")
    def test_fetch_data(self, mock_get_status):
        mock_get_status.return_value = "Mocked Status"
        self.assertDictEqual(fetch_data(), {"status": "Mocked Status"})

Run tests:

python -m unittest tests.test_data_fetcher

📈 Measuring Test Coverage

pip install coverage
coverage run -m unittest discover -s tests
coverage html  # Generates an HTML report

View htmlcov/index.html for insights.

🚀 Conclusion

unittest helps write reliable tests. Use mocking to isolate dependencies and coverage to track completeness.

Start writing better tests today! 🧪✨

Installing ML-Agents on macOS M1: A Troubleshooting Guide 👨‍💻

Ankit Sahu — Wed, 23 Aug 2023 16:09:44 +0000

If you're an AI and ML enthusiast like me, you might have encountered challenges while trying to install the ML-Agents Python library on your Mac M1. Fear not, I've gone through the pains 🥲👍 and want to share my experience to make this process smoother for you. 🤖

Step 1: Creating a Virtual Environment

First, let's create a virtual environment using the venv tool. Open your terminal and execute the following command:

python -m venv rl-env

Step 2: Installing ML-Agents

Now, let's install ML-Agents using pip. Run this command within your virtual environment:

python -m pip install mlagents==0.30.0

Step 3: Checking Installation

To ensure ML-Agents is correctly installed, run the following command:

mlagents-learn --help

If you encounter no errors, consider yourself lucky 🦄!
But, if you face an error like the one below, don't worry, I've got your back 😎.

Traceback (most recent call last):
  File "/Users/alpha/miniforge3/envs/rl-venv/bin/mlagents-learn", line 5, in <module>
    from mlagents.trainers.learn import main
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents/trainers/learn.py", line 2, in <module>
    from mlagents import torch_utils
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents/torch_utils/__init__.py", line 1, in <module>
    from mlagents.torch_utils.torch import torch as torch  # noqa
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents/torch_utils/torch.py", line 6, in <module>
    from mlagents.trainers.settings import TorchSettings
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents/trainers/settings.py", line 25, in <module>
    from mlagents.trainers.cli_utils import StoreConfigFile, DetectDefault, parser
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents/trainers/cli_utils.py", line 5, in <module>
    from mlagents_envs.environment import UnityEnvironment
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents_envs/environment.py", line 12, in <module>
    from mlagents_envs.side_channel.side_channel import SideChannel
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents_envs/side_channel/__init__.py", line 5, in <module>
    from mlagents_envs.side_channel.default_training_analytics_side_channel import (  # noqa
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents_envs/side_channel/default_training_analytics_side_channel.py", line 7, in <module>
    from mlagents_envs.communicator_objects.training_analytics_pb2 import (
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/mlagents_envs/communicator_objects/training_analytics_pb2.py", line 35, in <module>
    _descriptor.FieldDescriptor(
  File "/Users/alpha/miniforge3/envs/rl-venv/lib/python3.9/site-packages/google/protobuf/descriptor.py", line 561, in _new_
    _message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates

Step 4: Downgrading Protobuf

To fix the protobuf issue, you need to downgrade it. Run the following command:

pip install protobuf==3.20.0

Step 5: Rechecking Installation

Now, check if ML-Agents is installed correctly again:

mlagents-learn --help

Step 6: Addressing Further Errors

You might encounter another error related to symbol not found in flat namespace '_CFDataGetBytes'. This is due to the CoreFoundation framework not being available on Apple Silicon Macs by default.

Traceback (most recent call last):
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/bin/mlagents-learn", line 33, in <module>
    sys.exit(load_entry_point('mlagents==0.30.0', 'console_scripts', 'mlagents-learn')())
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/bin/mlagents-learn", line 25, in importlib_load_entry_point
    return next(matches).load()
  File "/Users/alpha/miniforge3/lib/python3.10/importlib/metadata/__init__.py", line 171, in load
    module = import_module(match.group('module'))
  File "/Users/alpha/miniforge3/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents/trainers/learn.py", line 2, in <module>
    from mlagents import torch_utils
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents/torch_utils/__init__.py", line 1, in <module>
    from mlagents.torch_utils.torch import torch as torch  # noqa
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents/torch_utils/torch.py", line 6, in <module>
    from mlagents.trainers.settings import TorchSettings
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents/trainers/settings.py", line 25, in <module>
    from mlagents.trainers.cli_utils import StoreConfigFile, DetectDefault, parser
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents/trainers/cli_utils.py", line 5, in <module>
    from mlagents_envs.environment import UnityEnvironment
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents_envs/environment.py", line 49, in <module>
    from .rpc_communicator import RpcCommunicator
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/mlagents_envs/rpc_communicator.py", line 1, in <module>
    import grpc
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/grpc/__init__.py", line 22, in <module>
    from grpc import _compression
  File "/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/grpc/_compression.py", line 20, in <module>
    from grpc._cython import cygrpc
ImportError: dlopen(/Users/alpha/Files/dev/reinforcement-learning-unity/rl-env/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-darwin.so, 0x0002): symbol not found in flat namespace '_CFDataGetBytes'

To solve this, rebuild grpcio from source using the following commands:

pip uninstall grpcio
export GRPC_PYTHON_LDFLAGS=" -framework CoreFoundation"
pip install grpcio --no-binary :all:

Wrapping Up

Congratulations, you've successfully navigated through the installation process! Don't let these hiccups discourage you; troubleshooting is an integral part of a developer's journey.🚀

Mastering Prisma: A Comprehensive Guide for Beginners

Ankit Sahu — Sat, 29 Jul 2023 17:04:38 +0000

🚀 Initializing the Project 🚀

To kick off our journey with Prisma, let's start by creating a new project and installing the necessary dependencies. Open your terminal and run the following commands:

yarn init -y
yarn add --dev typescript ts-node @types/node nodemon

Next, let's initialize TypeScript in our project and add the Prisma CLI:

yarn tsc --init
yarn add --dev prisma

Now, we need to set up Prisma to work with PostgreSQL:

yarn prisma init --datasource-provider postgresql

📊 Creating a Basic Model 📊

Once Prisma is set up, let's create a simple model in the _schema.prisma file. In this example, we'll create a User model with an id and a name:

model User {
  id   Int    @id @default(autoincrement())
  name String
}

After defining the model, it's time to create the corresponding database tables using Prisma Migrate:

yarn prisma migrate dev --name init

📝 Sending Queries to the Database 📝

Now, let's explore how to interact with the database using Prisma Client. Create a file called script.ts and add the following code:

import { PrismaClient } from '@prisma/client'

const prisma = new PrismaClient()

async function main() {
  // ... you will write your Prisma Client queries here
}

main()
  .catch(e => {
    console.log(e.message)
  })
  .finally(async () => {
    await prisma.$disconnect()
  })

In the script.ts file, you can use Prisma Client to send queries to the database. For example, to add a new user, you can use the following code:

const user = await prisma.user.create({ data: { name: "Steve Rogers" } })

And to retrieve all users from the database:

const users = await prisma.user.findMany()
console.log(users)

💡 Tip: Don't forget to install the Prisma Client and generate the Prisma Client code before running the script:

yarn add @prisma/client
yarn prisma generate

🎉 Adding Nodemon for Automatic Refresh 🎉

To make development more efficient, let's set up Nodemon to automatically refresh the script when changes are made. Add the following script to your package.json:

"scripts": {
  "devStart": "nodemon script.ts"
}

Now, you can run the following command to start your development server:

yarn devStart

✨ Advanced Schema with Relations and Enums ✨

Now, let's dive into more advanced concepts in Prisma and create a schema with relations and an enum. You can use this as a reference for building more complex data models.

// SCHEMA
model User {
  id               String          @id @default(uuid())
  name             String
  age              Int
  email            String          @unique
  isAdmin          Role            @default(BASIC)
  authoredPosts    Post[]          @relation("AuthoredPosts") // One to Many
  bookmarkedPosts  Post[]          @relation("BookmarkedPosts")
  userPreference   UserPreference? @relation(fields: [userPreferenceId], references: [id])
  userPreferenceId String?         @unique

  @@unique([email, name])
  @@index([email])
}

// One to One Relation
model UserPreference {
  id           String  @id @default(uuid())
  user         User?
  emailUpdates Boolean @default(false)
}

model Post {
  id             String     @id @default(uuid())
  title          String
  rating         Float
  createdAt      DateTime   @default(now())
  updatedAt      DateTime   @updatedAt
  author         User       @relation("AuthoredPosts", fields: [authorId], references: [id])
  authorId       String
  bookmarkedBy   User?      @relation("BookmarkedPosts", fields: [bookmarkedById], references: [id])
  bookmarkedById String?
  categories     Category[]
}

// Many to Many Relation
model Category {
  id    String @id @default(uuid())
  posts Post[]
}

enum Role {
  BASIC
  ADMIN
}

🛠️ CRUD Operations with Prisma Client 🛠️

To perform CRUD (Create, Read, Update, Delete) operations using Prisma Client, let's dive into some code examples.

Creating a User:

async function create() {
  const user = await prisma.user.create({
    data: {
      name: "Lizard Zuck",
      email: "zuckiscuck@gmail.com",
      userPreference: {
        create: {
          emailUpdates: true,
        },
      },
    },
    select: {
      name: true,
      userPreference: { select: { id: true } },
    },
  })
  return user
}

Reading a User by Email:

const user = await prisma.user.findUnique({
  where: {
    email: "ankit1042002@gmail.com",
  },
})
console.log(user)

Filtering Users with Complex Conditions:

const user = await prisma.user.findMany({
  where: {
    name: { not: "Ankit" },
    age: { lt: 20 }, // lt -> less than
    email: { contains: "@gmail.com", startsWith: "an" },
  },
})

Updating User Data:

const updatedUser = await prisma.user.update({
  where: {
    email_name: {
      email: "ankit.ks@yahoo.com",
      name: "Ankit",
    },
  },
  data: {
    email: "ankit.ks@yahoo.com",
  },
})
console.log(updatedUser)

Deleting a User:

const deletedUser = await prisma.user.delete({
  where: {
    email_name: {
      email: "ankit.ks@yahoo.com",
      name: "Ankit",
    },
  },
})
console.log(deletedUser)

🔗 Connecting and Disconnecting Relations 🔗

To connect and disconnect relations, such as connecting a User to their UserPreference, you can use the following examples:

Connecting UserPreference:

const preference = await prisma.userPreference.create({
  data: {
    emailUpdates: true,
  },
})

const user = await prisma.user.update({
  where: {
    email_name: {
      email: "ankit.ks@yahoo.com",
      name: "Ankit",
    },
  },
  data: {
    userPreference: {
      connect: {
        id: preference.id,
      },
    },
  },
})

Disconnecting UserPreference:

const user = await prisma.user.update({
  where

: {
    email_name: {
      name: "Ankit",
      email: "ankit.ks@yahoo.com",
    },
  },
  data: {
    userPreference: {
      disconnect: true,
    },
  },
})

🔍 Fetching Posts with Relations 🔍

To fetch posts related to users, you can use Prisma Client to perform queries like this:

async function main() {
  const posts = await prisma.user.findMany({
    where: {
      authoredPosts: {
        every: {
          title: {
            startsWith: "T",
          },
        },
      },
    },
  })
  console.log(posts)
}

📄 Conclusion 📄

Congratulations! You've now learned the essentials of using Prisma.
Now, my coding Jedi, may the Prisma force be with you. Remember, even Darth Bugs will fear your skills!.

A Step-by-Step Guide: Using Ethers v6 library connect Metamask 🦊 to Your React JS Website

Ankit Sahu — Sat, 29 Jul 2023 04:58:58 +0000

Metamask is a well-known browser extension that allows users to interact with the Ethereum blockchain effortlessly. In this step-by-step guide, we'll explore how to connect Metamask to your React JS website using the ethers.js library.

By the end of this tutorial, you'll be able to
✅ check if Metamask is installed
✅ connect to users' wallets
✅ add custom networks

🚀 Step 1: Install the Ethers Library 📦
Let's install the ethers library, which will be our primary tool for interacting with the Ethereum blockchain. Open your terminal and run the following command:

npm install ethers

🔍 Step 2: Checking Metamask Installation 🦊
The first step in connecting Metamask to your React JS website is to check whether the user has Metamask installed. In case it's not installed, we'll prompt the user to install Metamask for a smooth experience.

const checkMetamaskInstallation = () => {
    if (window.ethereum == undefined) {
        alert("Metamask wallet is not installed");
        return;
    }
};

💼 Step 3: Initiating Wallet Connection 💳
Once we've ensured that Metamask is present, the next step is to establish a connection between the user's wallet and our DApp. With the connection established, we'll have the user's account address available for further interactions.

const [account, setAccount] = useState(null);
const [provider, setProvider] = useState(null);

const initiateWalletConnection = async () => {
    try {
        const provider = new ethers.BrowserProvider(window.ethereum);
        const accounts = await provider.send("eth_requestAccounts", []);
        const account = accounts[0];
        setProvider(provider);
        setAccount(account);
    } catch (error) {
        console.log(error);
    }
};

⚙️ Step 4: Adding a Custom Network (Optional) 🌐

In some cases, your DApp might be deployed on a specific network, such as the Polygon Mumbai Testnet. To ensure users can interact with your DApp seamlessly you need to add custom networks to the user's Metamask. I'll illustrate this with the example of adding the Polygon Mumbai Testnet.

const addPolygonMumbaiTestnet = async () => {
    const provider = new ethers.BrowserProvider(window.ethereum);
    await provider.send("wallet_addEthereumChain", [
        {
            chainId: "0x13881",
            chainName: "Mumbai Testnet",
            nativeCurrency: {
                name: "MATIC",
                symbol: "MATIC",
                decimals: 18,
            },
            rpcUrls: ["https://rpc-mumbai.maticvigil.com"],
            blockExplorerUrls: ["https://mumbai.polygonscan.com/"],
        },
    ]);
};

🌐 Step 5: Switching to the Custom Network 🔄
After adding the custom network (if needed), it's essential to switch the user's Metamask to the desired network for proper DApp functioning. If the user is already on the desired network, the function does nothing. Otherwise, it switches to the Polygon Mumbai Testnet.

const switchToMumbaiTestnet = async () => {
    try {
        const provider = new ethers.BrowserProvider(window.ethereum);
        await provider.send("wallet_switchEthereumChain", [
            { chainId: "0x13881" },
        ]);
    } catch (error) {
        if (error.code === 4902) {
            await addPolygonMumbaiTestnet();
        } else {
            console.log(error);
        }
    }
};

🎉 Conclusion: 🌟
By following this comprehensive guide, you've learned how to integrate Metamask with your React JS website using the powerful ethers.js library. You now have the ability to check if Metamask is installed, connect to users' wallets, and even add custom networks for an enhanced user experience. With this knowledge, you can create feature-rich DApps that leverage the full potential of blockchain technology. 🚀💎

Create and deploy your own token using hardhat in just 2 mins

Ankit Sahu — Thu, 29 Jun 2023 14:58:49 +0000

In this tutorial, I will guide you through the process of creating and deploying your own ERC20 token using the Hardhat development environment.

Prerequisites:

Before we get started, make sure you have the following set up on your machine:

Node.js and npm (Node Package Manager) installed
Basic knowledge of Solidity programming language
Familiarity with the Ethereum ecosystem and ERC20 tokens

Step 1: Setting Up the Project

Create a new directory for your project and navigate to it in your terminal.
Initialize a new Node.js project by running the command: npm init -y.
Install Hardhat as a development dependency by running: npm install --save-dev hardhat.
Open the project in your preferred code editor.

Step 2: Writing the ERC20 Token Contract

Install the OpenZeppelin Contracts library, which provides pre-audited and secure contract implementations: npm install @openzeppelin/contracts.
Create a new file named Zen.sol in the contracts directory.
Add the following code to the Zen.sol file:

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

import "@openzeppelin/contracts/token/ERC20/ERC20.sol";

contract Zen is ERC20 {
    uint256 constant initialSupply = 1000000 * (10 ** 18);

    constructor() ERC20("Zen", "ZEN") {
        _mint(msg.sender, initialSupply);
    }

    function decimals() public pure override returns (uint8) {
        return 18;
    }
}

Step 3: Writing the Deployment Script

Create a new directory named scripts in the project root.
Inside the scripts directory, create a new file named deploy.js.
Add the following code to the deploy.js file:

const hre = require("hardhat");
const fs = require("fs");

async function main() {
    const [deployer] = await hre.ethers.getSigners();
    console.log("Deployer address:", deployer.address);
    const zen = await hre.ethers.deployContract("Zen");
    await zen.waitForDeployment();
    const contractAddress = zen.target;
    console.log("Contract Address:", contractAddress);
    const zenArtifact = require("../artifacts/contracts/Zen.sol/Zen.json");
    const zenAbi = zenArtifact.abi;
    const contractData = {
        address: contractAddress,
        abi: zenAbi,
    };
    fs.writeFileSync("./abi/zen.json", JSON.stringify(contractData, null, 2));
}

main()
    .then(() => process.exit(0))
    .catch((error) => {
        console.error(error);
        process.exit(1);
    });

Step 4: Configuring Hardhat

Open the hardhat.config.js file in the project root directory.
Update the file with the following configuration:

require("@nomicfoundation/hardhat-toolbox");
require("dotenv").config();
const { POLYGON_MUMBAI_RPC_PROVIDER, PRIVATE_KEY, POLYGONSCAN_API_KEY } = process.env;

module.exports = {
    defaultNetwork: "hardhat",
    networks: {
        hardhat: {
            chainId: 1337,
        },
        mumbai: {
            url: POLYGON

_MUMBAI_RPC_PROVIDER,
            accounts: [`0x${PRIVATE_KEY}`],
        },
    },
    solidity: {
        version: "0.8.9",
        settings: {
            optimizer: {
                enabled: true,
                runs: 200,
            },
        },
    },
    etherscan: {
        apiKey: POLYGONSCAN_API_KEY,
    },
};

Step 5: Deploying the ERC20 Token

Run the deployment script by executing the command: npx hardhat run scripts/deploy.js --network <network>. Replace <network> with the desired network, such as mumbai for the Polygon Mumbai testnet.
The script will deploy the Zen contract and output the deployer's address and the deployed contract address.
The contract's ABI will be saved to the ./abi/zen.json file.

Conclusion:

Congratulations! You have successfully created and deployed your own ERC20 token using Hardhat. You can now use the contract address and ABI to interact with the token on the Ethereum network. ERC20 tokens are the building blocks for decentralized applications, enabling various use cases such as digital currencies, loyalty points, and more. Experiment with your token and explore the endless possibilities of decentralized finance.

Feel free to customize and extend the functionality of your ERC20 token contract according to your specific requirements. Happy coding!

Effortlessly Share Files Across Devices on Your Wi-Fi Network with Python

Ankit Sahu — Mon, 15 May 2023 18:02:46 +0000

Are you tired of using USB drives or sending files over email to share files between your devices on the same Wi-Fi network? Well, there's an easier way! In this post, I'll show you how to share files wirelessly between devices on the same Wi-Fi network using Python.

Python comes with a built-in module called http.server that allows you to serve files over HTTP. By running a simple command in your terminal, you can turn your computer into a file server that other devices on the same Wi-Fi network can connect to and download files from.

Here are the steps to share files using Python:

Step 1: Open your terminal or command prompt.

Step 2: Navigate to the folder that contains the file you want to share.

Step 3: Run the following command:

python -m http.server

This will start a simple HTTP server on your computer that serves files from the current directory.

Step 4: Open a web browser on another device that is connected to the same Wi-Fi network.

Step 5: In the address bar, enter the IP address of the computer that is running the file server, followed by the port number that the server is listening on.

Use ipconfig getifaddr command to find ip address

Example

http://192.168.1.100:8000

Replace 192.168.1.100 with the IP address of your computer, and 8000 with the port number that the server is listening on (which is usually 8000 by default).

Step 6: Press Enter, and you should see a list of files and folders that are in the directory that you ran the http.server command in.

Step 7: Click on the file that you want to download, and it will be downloaded to the device that you are using.

That's it! You can now share files wirelessly between devices on the same Wi-Fi network using Python.

One thing to keep in mind is that the file server that http.server creates is not secure, and anyone on the same Wi-Fi network can access it. So, make sure to only share files that you don't mind other people seeing.

Switching Between Dark and Light in Nextjs13.4 App

Ankit Sahu — Tue, 09 May 2023 13:12:30 +0000

Create nextjs app

Add darkMode: "class" to tailwind.config.css

Install next-themes

pnpm install next-themes

Create Provider.tsx file inside app directory

"use client"

import React from 'react'
import {ThemeProvider} from 'next-themes'

function Provider({
    children,
    }: {
        children: React.ReactNode
    }) {
    return (
        <ThemeProvider>
            {children}
        </ThemeProvider>
    )
}

export default Provider

Add following to layout.tsx in app directory

import Provider from './Provider'
import './globals.css'
import { Inter } from 'next/font/google'

const inter = Inter({ subsets: ['latin'] })

export const metadata = {
  title: 'Create Next App',
  description: 'Generated by create next app',
}

export default function RootLayout({
  children,
}: {
  children: React.ReactNode
}) {
  return (
    <html lang="en">
      <body className={inter.className}>
        <Provider>
          {children}
        </Provider>
      </body>
    </html>
  )
}

That's it now you just have use useTheme hook to switch between light and dark mode

Create ThemeToggle.tsx file

"use client"
import { useTheme } from 'next-themes'
import React, { useEffect, useState } from 'react'


function ToggleTheme() {
    const [mounted, setMounted] = useState(false)
    const { theme, setTheme } = useTheme()

    useEffect(() => {
        setMounted(true)
    }, [])

    if (!mounted) {
        return null
    }

    return (
        <select value={theme} onChange={e => setTheme(e.target.value)}>
            <option value="system">System</option>
            <option value="dark">Dark</option>
            <option value="light">Light</option>
        </select>
    )
}

export default ToggleTheme

Installing Snort on Kali Linux

Ankit Sahu — Mon, 03 Oct 2022 07:41:53 +0000

Snort is no longer available in Kali repositories

Here are the steps to install snort on Kali

Backup kali's sources.list

mv /etc/apt/sources.list /etc/apt/sources.list.bak

Remove updates

find /var/lib/apt/lists -type f -exec rm {} \;

Change sources.list content

sudo nano /etc/apt/sources.list

Paste content given below

deb http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse<br>
deb-src http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse<br>

deb http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse<br>
deb-src http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse<br>

deb http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse<br>
deb-src http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse<br>

deb http://archive.ubuntu.com/ubuntu/ focal-backports main restricted universe multiverse<br>
deb-src http://archive.ubuntu.com/ubuntu/ focal-backports main restricted universe multiverse<br>

deb http://archive.canonical.com/ubuntu focal partner<br>
deb-src http://archive.canonical.com/ubuntu focal partner<br>

If you are using kali as a virtual machine then paste this instead
As core Ubuntu repositories do not have the ARM repositories in them

deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports focal main restricted universe multiverse<br>
deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports focal-updates main restricted universe multiverse<br>
deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports focal-security main restricted universe multiverse<br>

deb [arch=i386,amd64] http://us.archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse<br>
deb [arch=i386,amd64] http://us.archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse<br>
deb [arch=i386,amd64] http://security.ubuntu.com/ubuntu focal-security main restricted universe multiverse<br>

Add the specified public keys

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 3B4FE6ACC0B21F32
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 871920D1991BC93C

Update

sudo apt update

Now install snort

sudo apt install snort

Install MongoDB 4.4 on Fedora

Ankit Sahu — Tue, 04 May 2021 20:39:20 +0000

Add the following in yum repository configuration file

$ sudo vi /etc/yum.repos.d/mongodb.repo

[Mongodb]
name=MongoDB Repository
baseurl=https://repo.mongodb.org/yum/redhat/8Server/mongodb-org/4.4/x86_64/
gpgcheck=1
enabled=1
gpgkey=https://www.mongodb.org/static/pgp/server-4.4.asc

Install MongoDB

$ sudo dnf install mongodb-org

Start MongoDB Service

$ sudo systemctl enable mongod.service
$ sudo systemctl start mongod.service

Blank Terminal in VS Code (Ubuntu)

Ankit Sahu — Sun, 21 Mar 2021 09:20:56 +0000

Go to settings
Search for Inherit Env and enable it.

Restart VScode and now you will not find the blank terminal screen ;)

Facing error with OpenCV in Ubuntu?

Ankit Sahu — Fri, 19 Mar 2021 18:27:55 +0000

cv2.error: OpenCV(3.4.2) /tmp/build/80754af9/opencv-suite_1535558553474/work/modules/highgui/src/window.cpp:632: error: (-2:Unspecified error) Rebuild the library with Windows, GTK+ 2.x or Carbon support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'.

Is this what you got as error ?

To solve this problem

Install cmake

sudo snap install cmake --classic

Still facing the problem ?
Try installing

sudo apt install libgtk2.0-dev
sudo apt install pkg-config
sudo apt install libopencv-*

Error
QObject::moveToThread: Current thread (0x5618076ecfe0) is not the object's thread (0x561807941d80). Cannot move to target thread (0x5618076ecfe0)

If you are using anaconda then try installing pyqt

conda install pyqt

I hope it helped you :)