Creating a fully functioning API project for the Littlelemon Restaurant so that client application developers can consume the APIs to develop web and mobile applications. People with different roles will be able to browse, add and edit menu items, place orders, browse orders, assign delivery crew to orders and finally deliver the orders.

• Using Djoser to automatically create endpoints

  • POST: auth/users/: An api endpoint for user registration

  • GET: auth/users/me: Display the current user

  • POST: auth/token/login: Generate user’s access-token {username and password}

• api/menu-items Role: Customer and Delivery Crew:

  • GET: List all menu items

  • POST,PUT,PATCH,DELETE: Denies access and returns 403-Unauthorized HTTP status code

  Role: Manager

  • GET: List all menu items

  • POST: Creates a new menu item

• api/menu-items/{menuItem}: Role: Customer and Delivery Crew

  • GET: List a single menu item

  • POST,PUT,PATCH,DELETE: Denies access and returns 403-Unauthorized HTTP status code

  Role: Manager

  • GET: List a single menu item.

  • PUT,PATCH: Updates single menu item.

  • DELETE: Deletes menu item

• api/groups/manager/users:

  Role: Manager

  • GET: Returns all managers

  • POST: Assigns the username in the payload to the manager group

  • api/groups/manager/users/int:id:

    • DELETE: Removes this particular user from the manager group and returns 200 – Success if everything is okay. If user not found, return 404 – Not Found

• api/groups/delivery-crew/users:

  Role: Manager

  • GET: Returns all delivery crew members

  • POST: Assigns the username in the payload to the delivery group

  • api/groups/delivery-crew/users/int:id:

    • DELETE: Removes this particular user from the delivery group and returns 200 – Success if everything is okay. If user not found, return 404 – Not Found

• api/cart/menu-items:

  Role: Customer

  • GET: Returns current items int the cart for the current user

  • POST: Adds the menu item to the cart.

  • DELETES: Deletes all menu-items created by the current user token

• api/orders: Role: Customer

  • GET: Returns all orders with order items created by the user

  • POST: Creates a new order item for the current user. Gets current cart items from the cart endpoint and adds those items to the order items table. Then deletes all items from the cart for the user.

  Role: Manager

  • GET: Returns all orders with order items by all users

  Role: Delivery Crew

  • GET: Returns all orders with order items assigned to the delivery crew

• api/orders/{orderId}:

  Role: Customer

  • GET: Returns all items for this order id. If the order ID doesn’t belong to the current user, it displays an appropriate HTTP error status code.

  Role: Manager

  • PUT,PATCH: Updates the order. A manager can use this endpoint to set a delivery crew to this order, and also update the order status to 0 or 1. If a delivery crew is assigned to this order and the status = 0, it means the order is out for delivery. If a delivery crew is assigned to this order and the status = 1, it means the order has been delivered.

  • DELETE: Deletes this order

  Role: Delivery Crew

  • PATCH: A delivery crew can use this endpoint to update the order status to 0 or 1. The delivery crew will not be able to update anything else in this order.

Pagination, filtering and proper sorting capabilities were applied to the api/menu-items and api/orders endpoints.

API throttling was also applied for authenticated users and anonymous or unauthenticated users.

Generalized regression neural network (GRNN) is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991.GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamical systems. GRNN represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to a radial basis neuron.[1]

GRNN is a feed forward ANN model consisting of four layers: input layer, pattern layer, summation layer and output layer. Unlike backpropagation ANNs, iterative training is not required. Each layer in the structure consists of different numbers of neurons and the layers are connected to the next layer in turn. [2]

• In the first layer, the input layer, the number of neurons is equal to the number of properties of the data.[3]

• In the pattern layer, the number of neurons is equal to the number of data in the training set. In the neurons in this layer, the distances between the training data and the test data are calculated and the results are passed through the radial based function (activation function) with the σ value and the weight values are obtained.[3]

• The summation layer has two subparts one is Numerator part and another one is Denominator part. Numerator part contains summation of the multiplication of training output data and activation function output (weight values). Denominator is the summation of all weight values. This layer feeds both the Numerator & Denominator to the next output layer.[3]

• The output layer contains one neuron which calculate the output by dividing the numerator part of the Summation layer by the denominator part.[3]

                                               The general structure of GRNN [3]

Training procedure is to find out the optimum value of σ. Best practice is that find the position where the MSE (Mean Squared Error) is minimum. First divide the whole training sample into two parts. Training sample and test sample. Apply GRNN on the test data based on training data and find out the MSE for different σ. Now find the minimum MSE and corresponding value of σ. [3]

• The main advantage of GRNN is to speed up the training process which helps the network to be trained faster.

• The network is able to learning from the training data by “1-pass” training in a fraction of the time it takes to train standard feed forward networks.

• The spread, Sigma (σ), is the only free parameter in the network, which often can be identified by the V-fold or Split-Sample cross validation.

• Unlike standard feed forward networks, GRNN estimation is always able to converge to a global solution and won’t be trapped by a local minimum. [3]

• Its size can be huge, which would make it computationally expensive. [4]

Retrieved from [3]

[1] https://www.wikizeroo.org/index.php?q=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnL3dpa2kvR2VuZXJhbF9yZWdyZXNzaW9uX25ldXJhbF9uZXR3b3Jr

[2] https://www.journalagent.com/pajes/pdfs/PAJES_24_5_857_863.pdf

[3] https://easyneuralnetwork.blogspot.com/2013/07/grnn-generalized-regression-neural.html

Generalized regression neural network (GRNN) is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991.GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamical systems. GRNN represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to a radial basis neuron.[1]

GRNN is a feed forward ANN model consisting of four layers: input layer, pattern layer, summation layer and output layer. Unlike backpropagation ANNs, iterative training is not required. Each layer in the structure consists of different numbers of neurons and the layers are connected to the next layer in turn. [2]

• In the first layer, the input layer, the number of neurons is equal to the number of properties of the data.[3]

• In the pattern layer, the number of neurons is equal to the number of data in the training set. In the neurons in this layer, the distances between the training data and the test data are calculated and the results are passed through the radial based function (activation function) with the σ value and the weight values are obtained.[3]

• The summation layer has two subparts one is Numerator part and another one is Denominator part. Numerator part contains summation of the multiplication of training output data and activation function output (weight values). Denominator is the summation of all weight values. This layer feeds both the Numerator & Denominator to the next output layer.[3]

• The output layer contains one neuron which calculate the output by dividing the numerator part of the Summation layer by the denominator part.[3]

                                               The general structure of GRNN [3]

Training procedure is to find out the optimum value of σ. Best practice is that find the position where the MSE (Mean Squared Error) is minimum. First divide the whole training sample into two parts. Training sample and test sample. Apply GRNN on the test data based on training data and find out the MSE for different σ. Now find the minimum MSE and corresponding value of σ. [3]

• The main advantage of GRNN is to speed up the training process which helps the network to be trained faster.

• The network is able to learning from the training data by “1-pass” training in a fraction of the time it takes to train standard feed forward networks.

• The spread, Sigma (σ), is the only free parameter in the network, which often can be identified by the V-fold or Split-Sample cross validation.

• Unlike standard feed forward networks, GRNN estimation is always able to converge to a global solution and won’t be trapped by a local minimum. [3]

• Its size can be huge, which would make it computationally expensive. [4]

Retrieved from [3]

[1] https://www.wikizeroo.org/index.php?q=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnL3dpa2kvR2VuZXJhbF9yZWdyZXNzaW9uX25ldXJhbF9uZXR3b3Jr

[2] https://www.journalagent.com/pajes/pdfs/PAJES_24_5_857_863.pdf

[3] https://easyneuralnetwork.blogspot.com/2013/07/grnn-generalized-regression-neural.html

Webpack work template

Webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.

Author: Vedees | Youtube guide (ru)

• separated configs for dev and prod
• typescript / javascript full support
• sass / css full support
• full babel & postcss setup
• 0 dependencies
• the best optimization for your production
• easy webpack and babel customization

Everybody knows that developing runs on coffee! Thanks for your support!

# Download repository:
git clone https://github.com/vedees/webpack-template webpack-template

# Go to the app:
cd webpack-template

# Install dependencies:
# npm install
# or:
yarn

# Server with hot reload at http://localhost:8084/
# npm run start
# or:
yarn start

# Output will be at dist/ folder
# npm run build
# or:
yarn build

• public/*.html – HTML files
• src/app – core app
• src/shared – shared files
• src/shared/img – images folder (! for html calls use correct path: static/img/some.jpg)
• src/shared/misc – misc files (i.g. favicon, sitemap, etc.)
• src/index.ts – main app entity

Configs:

• /babel-defines.js – config for babel
• /webpack/webpack-pages.js – config for html pages
• /webpack/webpack-defines.js – config for entire webpack

Main entry point:

• src/app/index.ts – core entry point

Core webpack config from /webpack/webpack-defines.js:

const PATHS = {
  // path to the src dir
  src: path.join(__dirname, '../src'),
  // path to the output dir
  dist: path.join(__dirname, '../dist'),
  // path to the public files (html files)
  public: path.join(__dirname, '../public'),

  // path to output sub dir (js, css, fonts, etc.)
  assets: 'assets/',
  // path to output sub dir (img, icons, etc.)
  static: 'static/'
}

Pages config from /webpack/webpack-pages.js:

const pages = [
  {
    // page title
    title: 'Home page',
    // template name `public/index.html`
    template: 'index.html',
    // output filename `dist/index.html`
    filename: 'index.html',

    // other options can be here
  },
  {
    title: 'About page',
    template: 'about.html',
    filename: 'about.html',
  }
]

You can pass a hash of configuration options to html-webpack-plugin.

Allowed values are as follows: https://github.com/jantimon/html-webpack-plugin#options

In case if you don’t want to use Pages config:

1. Create another html file in ./public
2. Go to ./webpack/webpack.common.js
3. Add new page to the config:

    // index page:
    new HtmlWebpackPlugin({
      title: 'Home page',
      favicon: defines.src + '/shared/misc/favicon.ico',
      template: defines.public + '/index.html', // public/index.html page
      filename: 'index.html' // output file
    }),
    // about page:
    new HtmlWebpackPlugin({
      title: 'About page',
      favicon: defines.src + '/shared/misc/favicon.ico',
      template: defines.public + '/about.html', // public/about.html page
      filename: 'about.html' // output file
    }),

Install it:

yarn add bootstrap react react-dom

Import libs to src/app/index.ts:

// React example
import React from 'react'

// Bootstrap example (with custom js imports)
import Bootstrap from 'bootstrap/dist/js/bootstrap.min.js'
import 'bootstrap/dist/js/bootstrap.min.js'

Import libs to src/app/index.scss:

// sass lib import example:
@import '../../node_modules/spinners/stylesheets/spinners';
// css lib import example:
@import '../../node_modules/flickity/dist/flickity.css';

Here’s an example with React + i18n Provider.

Install react:

yarn add react react-dom

Create div with id app in public/index.html:

<div id="app"></div>

Init the app in src/app/index.ts:

import React from 'react'
import { createRoot } from 'react-dom/client'

// app styles
import './index.scss'

// local providers:
import { I18nProvider } from './providers/I18nProvider'

const container = document.getElementById('app') as HTMLElement
const root = createRoot(container)

root.render(
  <React.StrictMode>
    <I18nProvider>...</I18nProvider>
  </React.StrictMode>
)

File src/app/providers/I18nProvider.tsx:

import React, { FC, PropsWithChildren } from 'react'

export const I18nProvider: FC<PropsWithChildren> = ({ children }) => {
  // ...

  return <I18n locale={detectedLocale}>{children}</I18n>
}

Install vue:

yarn add vue

Init the app in src/app/index.ts:

// vue example (react one is above):
const app = new Vue({
  el: '#app'
})

Create div with id app in public/index.html:

<div id="app"></div>

Create your component in src/app/components/.

HTML Usage (in *.html files):

Init component in src/app/index.ts:

Vue.component('example-component', require('./components/Example.vue').default)

In any html files:

<example-component />

VUE Usage (in *.vue files):

Import component:

import ExampleComponent from '@/components/Example.vue'

Init component (template):

<Example />

Register component (script):

components: {
  Example: ExampleComponent
}

Connect fonts to public/index.html:

<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=Open+Sans:wght@400;500&display=swap" rel="stylesheet" />

Change the font in src/app/styles/body.scss:

html {
  font-family: 'Open Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, 'Apple Color Emoji', Arial, sans-serif, 'Segoe UI Emoji', 'Segoe UI Symbol' !important;
}

In case if you don’t want to use Google Fonts:

• Download fonts
• Add fonts to the (i.g. /src/shared/fonts/OpenSans/...).

Then add @font-face in some .scss file (i.g. /src/app/styles/font.scss):

// Open Sans example:
@font-face {
  font-family: 'Open Sans';
  font-style: normal;
  font-weight: 400;
  font-stretch: 100%;
  font-display: swap;
  src: url('/static/fonts/OpenSans/Open-Sans.woff2') format('woff2');
  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
}

The last step is to copy these fonts into the /dist folder every time you build the project.

Add another config for CopyWebpackPlugin to /webpack/webpack.common.js:

new CopyWebpackPlugin({
  // ...

  // `shared/fonts` to `dist/static/fonts`
  {
    from: `${defines.src}/shared/fonts`,
    to: `${defines.dist}/${defines.static}/fonts`
  },
})

Change the font in src/app/styles/body.scss:

html {
  font-family: 'Open Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, 'Apple Color Emoji', Arial, sans-serif, 'Segoe UI Emoji', 'Segoe UI Symbol' !important;
}

MIT

Copyright (c) 2018-present, Evgenii Vedegis

Dependencies

• A memory caching library which offers timed evictions for stored objects.

• I’ve also created my own version of Microsoft’s memory cache to show how you could roll your own.

• This solution includes a console application for testing the DLL.

 // Instantiate
 var cache = new CacheHelper<string>();

 // Add an item
 cache.AddOrUpdate("key1", "value1", TimeSpan.FromSeconds(3));
 // Add an item to update
 cache.AddOrUpdate("key3", "value3", DateTime.Now.AddMinutes(1));
 // Update an item
 cache.AddOrUpdate("key3", "updated value3", DateTime.Now.AddMinutes(2));

 // Check expiry
 var dt = cache.GetExpiration("key3");
 Console.WriteLine($"key3 will expire at {dt.Value.ToLongTimeString()}");

 // Check if exists
 Console.WriteLine($"The current cache does {(cache.Contains("key1") ? "" : "not")} contain key1");

 // Refresh the expiration by fetching
 var temp = cache.Get("key1");

 // Delete an item
 cache.Remove("key1");

 // Check if exists after removal
 Console.WriteLine($"The current cache does {(cache.Contains("key1") ? "" : "not")} contain key1");

 // Fetching all keys
 var keys = cache.GetAllKeys();
 foreach (var key in keys) { /* do something */ }

 // Fetching all objects
 var objs = cache.GetCacheAsEnumerable();
 foreach (var obj in objs) { /* do something */ }

 // Clean-up
 cache.Dispose();

• Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish and distribute copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
• The software is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the author or copyright holder be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
• Copyright © 2025. All rights reserved.

• This application was compiled and tested using VisualStudio 2022 on Windows 10/11 versions 22H2, 21H2, 21H1, and 23H2.

eslint-config-biome

Disables ESLint rules that have an equivalent and recommended Biome rule, allowing the simultaneous use of Biome and ESLint.

npm install -D eslint-config-biome # or your preferred package manager ;)

• eslint.config.js: Import eslint-config-biome and have it as the last item in the configuration array

  import biome from "eslint-config-biome";
  
  export default [
    // other configs,
    biome,
  ];

• Or .eslintrc.* (eslint <= v8): Add the following as the last item in the "overrides" array. Create it if necessary.

  {
    "overrides": [
      // other overrides,
      {
        files: ["*.ts", "*.js", "*.tsx", "*.jsx"],
        extends: ["biome"],
      }
    ],
  }

• In VSCode, to apply Biome and ESLint on save, you should have these in your project’s .vscode/settings.json:

  {
    "editor.codeActionsOnSave": {
      "source.fixAll.eslint": "explicit",
      "source.organizeImports.biome": "explicit",
      "quickfix.biome": "explicit"
    },
    "editor.defaultFormatter": "biomejs.biome"
  }

• For package.json scripts and CI, I recommend running biome before eslint for faster failure detection.

  "scripts": {
    "lint": "biome check --unsafe --fix && eslint --fix .",
    "lint:check": "biome check --unsafe && eslint .",
  }

• This package includes eslint-config-prettier, so formatting rules are also disabled as Biome is equivalent to Prettier. Attribution.

My personal Team Fortress 2 configuration. I wrote this config between 2012 – 2015, when I played the game competitively at the European TF2 League. The config is primarily intended for competitive play and I’ve used in 6v6 & 9v9 competitive tournaments. The majority of features are also useful for casual play. Do not expect any significant performance gains from this config, although it contains some optimizations. You can include your own graphical configurations and extend it.

🚧 This config doesn’t support the new x64 bit TF2 client. It was developed, tested and used with the old x32 bit one more than 10 years ago.

• Null-movement
• Callouts (🚧 WIP)
• Crouch Jump (CTap)
  • 💡 You’ll need explicitly execute this script in your soldier.cfg file. In addition to that you’ll have to rebind MOUSE2 in every other class config
• Auto record tournament demos
• Disabled tutorials
• Custom viewmodel FOV switcher
  • Hold MOUSE4 and move the scroll up or down in order to increase/decrease viewmodel FOV
• Loadout switcher
  • Hold ALT+6-9 to switch through the 4 default loadout slots + a hidden 5th one
• Custom server admin mode
  • Up to as many servers as you wish. list of configs and maps changable on the moment with a simple command such as: blands instead of 2x rcon changelevel cp_badlands / ecp6 instead of !rcon exec etf2l_6v6_5cp; open it for how to use it and more features
• Micspam loopback script
• Chat & Voice toggles
• In-game memory compactor
  • Frees up memory
• 4 different network presets
  • Chris’ Bad
  • Chris’ Good
  • m0re
  • Mine (Default)
• Netgraph presets
• Built in respawn timer
  • Useful in competitive to keep track of medic respawns etc, it shows everytime you hit TAB
• Instant FIX button.
  • Restarts sound, hud, heartbeat and records a temp demo
• Wait Tester (F11)
• Sourcemod plugin lister (listplugins)
• Offline Jumping (🚧 WIP)
  • Provide 3 teleports (save/tp)
  • Health Regen
• Numpad class switcher

1. Download the master ZIP file or clone the repository.
2. Copy the contents of the artifact in the following directory: \steamapps\common\Team Fortress 2\tf\custom\my_custom_files\cfg\
3. 💡 Optional: Make the files & directories READ-ONLY to prevent any changes in the settings of this config.
4. Paste surfaceproperties.txt in \custom\my_custom_files\scripts.

❗ If you have any other configs created with clugu or some other config generator it is recommended that you remove all *.cfg files and reset all cvars via steam://runsafe/440

-novid -high -console -w XXXX -h XXXX -dxlevel XX

💡 If you want the most of your machine use -dxlevel 81 otherwise use -dxlevel 95.

💭Where XXXX insert a resolution 1x/2x/3x lower than your native one, it can help performance.

A simple CLI for combining json and yaml files

Available via Homebrew/Linuxbrew

brew install avencera/tap/yamine

OR

Install from a github release:

curl -LSfs https://avencera.github.io/yamine/install.sh | sh -s -- --git avencera/yamine

OR

Install using cargo:

cargo install yamine or cargo binstall yamine

OR

Download a release directly from github: github.com/avencera/yamine/releases

yamine --help

Combine JSON/YAML files into a single file

Usage: yamine [OPTIONS] [FILES_OR_FOLDERS]...

Arguments:
  [FILES_OR_FOLDERS]...  File(s) or folder you want to run in

Options:
  -i, --stdin            Read from STDIN
  -d, --depth <DEPTH>    Number of folder depth to recurse into [default: 1]
  -o, --output <OUTPUT>  Output file name [default: combined.yaml]
      --dry-run          Default mode
  -w, --write            Write new output file
  -s, --stdout           Outputs combined file contents to STDOUT
  -f, --format <FORMAT>  The format for the output file, defaults to yaml [default: yaml] [possible values: yaml, json-array, json-k8s, json]
  -h, --help             Print help
  -V, --version          Print version

• Combine all yaml and json files in the current folder and creates combined.yaml file
  • yamine -w .
• Combine all yaml and json files in the current folder and creates a combined.json file in json-k8s format:
  • yamine --write --format json-k8s --output combined.json .
• Output the combined file to STDOUT in json format:
  • yamine --stdout -f json .
• Convert YAML from stdin and output as JSON to stdout
  • pbpaste | yamine --stdin --stdout -f json

• yaml – a multi document yaml file separated by --- (a kubernetes multi resource document)

  ---
  apiVersion: traefik.containo.us/v1alpha1
  kind: IngressRoute
  ---
  apiVersion: v1
  kind: Namespace
  metadata:
    name: example
  ---
  kind: ServiceAccount
  apiVersion: v1
  ---
  apiVersion: v1
  kind: Service
  ---
  apiVersion: apps/v1
  kind: Deployment

• json – a json file with each combined file being an element in the array

  [
      {
        "apiVersion": "traefik.containo.us/v1alpha1",
        "kind": "IngressRoute"
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "Namespace",
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "ServiceAccount",
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "Service",
        ...
      },
  ]

• json-k8s – a kubernetes multi resource json document ex:

  {
    "kind": "List",
    "apiVersion": "v1",
    "items": [
      {
        "apiVersion": "traefik.containo.us/v1alpha1",
        "kind": "IngressRoute"
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "Namespace",
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "ServiceAccount",
        ...
      },
      {
        "apiVersion": "v1",
        "kind": "Service",
        ...
      },
    ]
  }

A WPF Message Box implementing material design

The message box has the following custom features:

✅ Material Theme design

✅ Custom styles for border window, message foreground and background, title foreground and background, border, etc

✅ Button to copy message box details to clipboard

✅ Scrollable message box content

✅ Right to left (RTL) support

✅ Message content is .NET UIElement which can host any content

▶️ Download from Nuget ☁⤵

▶️ Install from Package manager Console

$ Install-Package MaterialMessageBox

Or, if using dotnet

$ dotnet add package MaterialMessageBox

Creating a simple message box

MaterialMessageBox.Show("Your cool message here", "The awesome message title");

Show a message box with RTL support

MaterialMessageBox.Show("Your cool message here", "The awesome message title", true);

Showing an error message

MaterialMessageBox.ShowError(@"This is an error message");

Showing an error message

MaterialMessageBox.ShowError(@"This is an error message");

Capturing Message Box Results

var result = MaterialMessageBox.ShowWithCancel($"This is a simple message with a cancel button. You can listen to the return value", "Message Box Title");

Styling a message box

CustomMaterialMessageBox msg = new CustomMaterialMessageBox
{
    TxtMessage = { Text = "Do you like white wine?", Foreground = Brushes.White },
    TxtTitle = { Text = "This is too cool", Foreground = Brushes.White },
    BtnOk = { Content = "Yes" },
    BtnCancel = { Content = "Noooo" },
    MainContentControl = { Background = Brushes.MediumVioletRed },
    TitleBackgroundPanel = { Background = Brushes.BlueViolet },

    BorderBrush = Brushes.BlueViolet
};

msg.Show();
MessageBoxResult results = msg.Result;

This library is built on top of the following toolkits:

• Material Design In XAML Toolkit – Comprehensive and easy to use Material Design theme and control library for the Windows desktop.
• Material Design Colors – ResourceDictionary instances containing standard Google Material Design swatches, for inclusion in a XAML application.

If you’ve improved Material Message Box and think that other people would enjoy it, submit a pull request. Anyone and everyone is welcome to contribute.

• You could always contact me through Email for any feature or issue. ⭐

• You need Visual Studio 2015 Community/Enterprise Edition (upwards) to build and test the solution.

The MIT License (MIT)

Copyright (c) 2021, Bespoke Fusion

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

❤️