nishanc

using Microsoft.Extensions.FileProviders;

var builder = WebApplication.CreateBuilder(args);

// Add services to the container.

builder.Services.AddControllersWithViews();

var app = builder.Build();

nishanc

<Project Sdk="Microsoft.NET.Sdk.Web">

  <PropertyGroup>
    <TargetFramework>net7.0</TargetFramework>
    <ImplicitUsings>enable</ImplicitUsings>
    <Nullable>enable</Nullable>
    <TypeScriptCompileBlocked>true</TypeScriptCompileBlocked>
	  <TypeScriptToolsVersion>Latest</TypeScriptToolsVersion>
    <IsPackable>false</IsPackable>
    <SpaRoot>ClientApp\</SpaRoot>

nishanc

# WARNING: This script will delete everything in Downloads, Pictures, Videos, Documents\PowerShell folders for the specified user and the D: drive.
# Use it at your own risk.

# Specify the user's profile folder
$userProfile = [System.Environment]::GetFolderPath([System.Environment+SpecialFolder]::UserProfile)

# Specify the folders to delete for the user
$userFoldersToDelete = @("Downloads", "Pictures", "Videos", "Documents\PowerShell")

# Specify the drive to delete

nishanc

    [MemoryDiagnoser]
    public class BenchmarkDemo2
    {
        private static readonly string _dateString = "01 05 1991";
        
        [Benchmark(Baseline = true)]
        public DateTime Original()
        {
            var day = _dateString.Substring(0, 2);
            var month = _dateString.Substring(3, 2);

nishanc

using CustomerChurnMLDemo;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.AutoML;

class Program
{
    static void Main(string[] args)
    {
        string trainDataFilePath = @"D:\CustomerChurnMLDemo\CustomerChurnMLDemo\Data\customer_churn_dataset-training-master.csv";

nishanc

// Import the necessary ML.NET namespace
using Microsoft.ML;
using CustomerChurnMLDemo;

// Create an MLContext instance, which serves as the entry point to ML.NET functionality
var mlContext = new MLContext();

// Load the data from a file using the CustomerChurn class's LoadIDataViewFromFile method
// It loads the data for retraining a machine learning model for customer churn prediction
var data = CustomerChurn.LoadIDataViewFromFile(mlContext, CustomerChurn.RetrainFilePath, CustomerChurn.RetrainSeparatorChar, CustomerChurn.RetrainHasHeader);

nishanc

{
  "age": 22,
  "gender": "Female",
  "tenure": 25,
  "usage_Frequency": 14,
  "support_Calls": 4,
  "payment_Delay": 27,
  "subscription_Type": "Basic",
  "contract_Length": "Monthly",
  "total_Spend": 598,

nishanc

app.UseSwaggerUI(c =>
{
    c.SwaggerEndpoint("/swagger/v1/swagger.json", "My API V1");
    c.RoutePrefix = string.Empty; // <--------Add this line.
});

nishanc

[ApiController]
[Route("api/[controller]")]
public class TodosController : Controller
{
  [HttpGet("/")]
  // [ProducesResponseType(typeof(Todo), StatusCodes.Status200OK)]
  [ProducesResponseType<Todo>(StatusCodes.Status200OK)]
  public Todo Get() => new Todo(1, "Write a sample", DateTime.Now, false);
}

nishanc

var connection = new signalR.HubConnectionBuilder()
  .withUrl("/chatHub")
  .withServerTimeoutInMilliseconds(60000)
  .withKeepAliveIntervalInMilliseconds(30000)
  .build();