Showing posts with label Medhat. Show all posts
Showing posts with label Medhat. Show all posts

Monday, October 12, 2026

Seed & Extend Identity Users & Roles in ASP.NET with Scaffold tool

In this tutorial, we will learn how to add more data fields to the standard ASP.NET users & roles schema using the scaffold tool. We will also learn how to seed users and roles.

Prerequisites

To proceed with this tutorial, you need the following:

  • VS Code
  • .NET: The current version of .NET is 10.0
  • the dotnet-ef tool
  • Tthe Microsoft.dotnet-scaffold tool

Getting Started

Create a new razor pages application with the following terminal window command:

dotnet new razor --auth individual -o XtendUsersRoles
cd XtendUsersRoles
mkdir Models

This creates an ASP.NET app that uses SQLite database for authentication.

Open the application folder in VS Code.

Suppose we want to capture more data about the user, in addition to email and password. Keeping it simple, lwe will extend user data with FirstName & LastName.

An easy way to do this is to create a new class that extends IdentityUser and adds FirstName and LastName properties. In the Models folder, add a new class named XUser to it with the following class code: 

public class XUser : IdentityUser {
  public XUser() : base() { }

  public string? FirstName { get; set; }
  public string? LastName { get; set; }
}

We may also wish to extend the standard roles table with a CreatedDate property.

Just as we did with users, we will create another class for roles that inherits from IdentityRole. In the Models folder, add another class named XRole and add to it the following code:

public class XRole: IdentityRole {

  public XRole() : base() { }

  public XRole(string roleName) : base(roleName) { }

  public DateTime CreatedDate { get; set; }
}

Add the following to Pages/_ViewImports.cshtml:

@using XtendUsersRoles.Models

Edit Data/ApplicationDbContext.cs file and make ApplicationDbContext  inherit from IdentityDbContext<XUser, XRole, string>(options). The ApplicationDbContext class code will look like this:

public class ApplicationDbContext(
    DbContextOptions<ApplicationDbContext> options)
    : IdentityDbContext<XUser, XRole, string>(options)
{

}

Add these passwords to the appsettings.Development.json file:

"Passwords": {
  "Admin": "P@$$w0rd",
  "Member": "P@$$w0rd"
}

We will seed the database with two roles and two users. Add Data/IdentitySeeder.cs with the following code:

public static class IdentitySeeder {
  public static async Task SeedAsync(
      UserManager<XUser> userManager,
      RoleManager<XRole> roleManager,
      IConfiguration configuration) {

      ArgumentNullException.ThrowIfNull(userManager);
      ArgumentNullException.ThrowIfNull(roleManager);
      ArgumentNullException.ThrowIfNull(configuration);

      await EnsureRoleAsync(roleManager, "Admin");
      await EnsureRoleAsync(roleManager, "Member");

      await EnsureUserAsync(
          userManager, configuration["Passwords:Admin"]!, "aa@aa.aa", "Adam", "Atkins", "Admin");

      await EnsureUserAsync(

          userManager, configuration["Passwords:Member"]!, "mm@mm.mm", "Mike", "Moore", "Member");
  }

  private static async Task EnsureRoleAsync(RoleManager<XRole> roleManager, string roleName) {
      if (!await roleManager.RoleExistsAsync(roleName)) {
          var result = await roleManager.CreateAsync(new XRole(roleName) {
              CreatedDate = DateTime.UtcNow
          });

          EnsureSucceeded(result, $"creating role '{roleName}'");
      }
  }

  private static async Task EnsureUserAsync(
      UserManager<XUser> userManager,
      string password,
      string email,
      string firstName,
      string lastName,
      string roleName) {

      var user = await userManager.FindByEmailAsync(email);

      if (user is null) {
          user = new XUser {
              UserName = email,
              Email = email,
              EmailConfirmed = true,
              FirstName = firstName,
              LastName = lastName
          };

          var result = await userManager.CreateAsync(user, password);
          EnsureSucceeded(result, $"creating user '{email}'");
      }

      if (!await userManager.IsInRoleAsync(user, roleName)) {
          var result = await userManager.AddToRoleAsync(user, roleName);
          EnsureSucceeded(result, $"adding user '{email}' to role '{roleName}'");
      }

  }

  private static void EnsureSucceeded(IdentityResult result, string operation) {
      if (!result.Succeeded) {
          var errors = string.Join("; ", result.Errors.Select(error => $"{error.Code}: {error.Description}"));

          throw new InvalidOperationException($"Identity seeding failed while {operation}: {errors}");
      }
  }
}

In the Program.cs, replace the builder.Services.AddDefaultIdentity. . .  statement with this:

builder.Services.AddIdentity<XUser, XRole>(
options => {
    options.Stores.MaxLengthForKeys = 128;
})
.AddEntityFrameworkStores<ApplicationDbContext>()
.AddDefaultUI()
.AddDefaultTokenProviders()
.AddRoles<XRole>();

We need to call the identity seed logic from somewhere. In Program.cs, Add the following code to the “if (app.Environment.IsDevelopment())” block, right before “app.UseMigrationsEndPoint()”:

await using var scope = app.Services.CreateAsyncScope();
var dbContext = scope.ServiceProvider.GetRequiredService<ApplicationDbContext>();
await dbContext.Database.MigrateAsync();

await IdentitySeeder.SeedAsync(
    scope.ServiceProvider.GetRequiredService<UserManager<XUser>>(),
    scope.ServiceProvider.GetRequiredService<RoleManager<XRole>>(),
    app.Configuration);

Edit Pages/Shared/_LoginPartial.cshtml and change:

@inject SignInManager<IdentityUser> SignInManager
@inject UserManager<IdentityUser> UserManager

TO

@inject SignInManager<XUser> SignInManager
@inject UserManager<XUser> UserManager

Let us start with a clean database and migration. Therefore, delete app.db and the Data/Migrations folder. 

If you do not already have the .NET entity framework tool, execute the following command from within a terminal window:

dotnet tool install -g dotnet-ef

Then, execute the following entity framework migration commands from within a terminal window in the root folder of the application:

dotnet ef migrations add M1 -o Data/Migrations
dotnet ef database update

To seed the database with sample roles and users, start the application with:

dotnet watch

To prove that user and role data are successfully seeded, login with one of the below credentials that were previously seeded:

EmailPassword
aa@aa.aa P@$$w0rd
mm@mm.mm P@$$w0rd

The next task we need to accomplish is to modify the registration page so that the application can capture extended data such as FirstName & LastName. ASP.NET provides ASP.NET Core Identity as a Razor Class Library. This means that the registration UI is baked into the assemblies and is surfaced with the .AddDefaultUI() option with the services.AddIdentity() command in Program.cs.

If you do not already have the .NET scaffolding tool, execute the following command from within a terminal window:

dotnet tool install -g Microsoft.dotnet-scaffold

To expose the identity pages (like LOgin, Register, Logout, etc) and change them, we will use the Scaffold tool. Run the following command from within a terminal window at the root of your project:

dotnet scaffold

Follow these steps to generate the Identity razor pages:


The result of the scaffolding process is that we now have a new folder /Areas/Identity/Pages/Account with many pages that pertains to Identity:

There are, however, some things that the scaffold tool did incorrectly:

  1. It added an ApplicationDbContext connection string in appsettings.json file. Go ahead and delete it.
  2. The scaffold tool also added this line of code into Program.cs, that also needs to be deleted:

builder.Services.AddDefaultIdentity<ApplicationUser>(options => options.SignIn.RequireConfirmedAccount = true).AddEntityFrameworkStores<ApplicationDbContext>();

Edit the code-behind file Areas/Identity/Pages/Account/Register.cshtml.cs. Add the following properties to the InputModel class: 

[Required]
[DataType(DataType.Text)]
[StringLength(50, ErrorMessage = "The {0} must be at least {2} and at max {1} characters long.", MinimumLength = 2)]
[Display(Name = "First Name")]
public string? FirstName { get; set; }

[Required]
[DataType(DataType.Text)]
[StringLength(50, ErrorMessage = "The {0} must be at least {2} and at max {1} characters long.", MinimumLength = 2)]
[Display(Name = "Last Name")]
public string? LastName { get; set; }

In the same file, delete the CreateUser() method.

In the OnPostAsync() method at around line 128, replace:

var user = CreateUser();

with: 

var user = new XUser {
  UserName = Input.Email,
  Email = Input.Email,
  FirstName = Input.FirstName,
  LastName = Input.LastName
};

In the same Register.cshtml.cs file, replace all instances of ApplicationUser with XUser. Do this also in files Login.cshtml.cs and Logout.cshtml.cs.

Next, let us update the UI. Edit razor page Areas\Identity\Pages\Account\Register.cshtml. Add the following markup to the form right before the email/username block: 

<div class="form-floating mb-3">
    <input asp-for="Input.FirstName" class="form-control" aria-required="true" placeholder="first name" />
    <label asp-for="Input.FirstName">First Name</label>
    <span asp-validation-for="Input.FirstName" class="text-danger"></span>
</div>

<div class="form-floating mb-3">
    <input asp-for="Input.LastName" class="form-control" aria-required="true" placeholder="last name" />
    <label asp-for="Input.LastName">Last Name</label>
    <span asp-validation-for="Input.LastName" class="text-danger"></span>
</div>

Run the web application and click on the Register button on the top-right side.


After you click on Register, all user data will be saved in the database. We can capture first name and last name as expected. 

We have succeeded in updating the registration page so that additional user data can be captured. Mission accomplished.

Monday, February 16, 2026

Scaffolding Blazor pages with microsoft.dotnet-scaffold

In this tutorial, I will show you how to build a server-side Blazor application that connects directly to a SQLite database using Entity Framework Core. We will scaffold the CRUD pages with the microsoft.dotnet-scaffold tool. There is also a quick introduction into the QuickGrid component.

Source Code: https://github.com/medhatelmasry/BlazorStudents

Companion Video: https://youtu.be/4hbE-GZ-WZA

Pre-requisites

  1. .NET Framework 10+
  2. VS Code (or any other .NET editor)
  3. dotnet-ef tool
  4. microsoft.dotnet-scaffold tool

Getting Started

In a terminal window, go to your working directory. Enter the following commands to create a Server-Side Blazor application inside a directory called BlazorStudents:

dotnet new blazor -int server --auth individual -o BlazorStudents
cd BlazorStudents

Run the application by entering the following command:

dotnet watch

The following page will load into your default browser:

The default page after creating and running a server-side blazor template.

Open the BlazorStudents folder in Visual Studio Code (or any other .NET editor).

We will work with a very simple student model. Therefore, add a Student class file in a folder named Models with the following content: 

public class Student {
    public int StudentId { get; set; }

    [Required(ErrorMessage = "You must enter first name.")]
    public string? FirstName { get; set; }

    [Required(ErrorMessage = "You must enter last name.")]
    public string? LastName { get; set; }

    [Required(ErrorMessage = "You must enter school.")]
    public string? School { get; set; }
    
    [Required(ErrorMessage = "You must enter gender.")]
    public string? Gender { get; set; }
    
    [Required(ErrorMessage = "You must enter date of birth.")]
    public DateTime? DateOfBirth { get; set; }
}

From within a terminal window at the root of your BlazorStudents project,  run the following command to add some required packages:

dotnet add package CsvHelper

The CsvHelper package will help us read data from a CSV.

Developers prefer having sample data when building data driven applications. Therefore, we will create some sample data to ensure that our application behaves as expected. Copy the CSV data at https://gist.github.com/medhatelmasry/e8d4edc2772a538419adda45e8f82685 and save it in a text file named students.csv in the wwwroot folder.

Edit Data/ApplicationDbContext.cs and add to the class the following property and methods:

public DbSet<Student> Students => Set<Student>();

protected override void OnModelCreating(ModelBuilder modelBuilder) {
  base.OnModelCreating(modelBuilder);
  modelBuilder.Entity<Student>().HasData(GetStudents());
}

private static IEnumerable<Student> GetStudents() {
  string[] p = { Directory.GetCurrentDirectory(), "wwwroot", "students.csv" };
  var csvFilePath = Path.Combine(p);

  var config = new CsvConfiguration(CultureInfo.InvariantCulture) {
    PrepareHeaderForMatch = args => args.Header.ToLower(),
  };

  var data = new List<Student>().AsEnumerable();
  using (var reader = new StreamReader(csvFilePath)) {
    using (var csvReader = new CsvReader(reader, config)) {
      data = csvReader.GetRecords<Student>().ToList();
    }
  }

  return data;
}

Notice the above code is adding contents of the wwwroot/students.csv file as seed data into the database.

We are now ready to apply Entity Framework migrations, create the database and seed data. If you have not done so already, you will need to globally install the Entity Framework CLI tool. This tool is installed globally on your computer by running the following command in a terminal window:

dotnet tool install --global dotnet-ef

To have a clean start with Entity Framework migrations, delete the Data/Migrations folder and the Data/app.db files.

From within a terminal window inside the BlazorStudents root directory, run the following command to create migrations:

dotnet ef migrations add Stu -o Data/Migrations

This results in the creation of a migration file ending with the name ....Stu.cs in the Data/Migrations folder. 

The next step is to create the SQLite Data/app.db database file. This is done by adding the following code to Program.cs, right before app.Run():

using (var scope = app.Services.CreateScope()) {
    var services = scope.ServiceProvider;

    var context = services.GetRequiredService<ApplicationDbContext>();    
    context.Database.Migrate();
}

Run the application. This will cause students data to be seeded in the database.

Scaffolding Blazor Components

If you have not done so already, install the dotnet scaffold tool with this terminal window command:

dotnet tool install -g microsoft.dotnet-scaffold

Let us scaffold the CRUD pages for students. Run the scaffold tool from the root directory of your application with this command:

dotnet scaffold

Follow these steps....

After running the "dotnet scaffold" tool, when asked to pick a scaffolding category, select "blazor".

When asked to pick a scaffolding command, choose "Razor Components with EntityFrameworkCore (dotnet-scaffold)".

When asked for a .NET prject file, choose the only one available: BlazorStudents.csproj.

When asked for model name, choose Student (Student).

When asked for database context class, enter ApplicationDbContext.

When asked for database provider, choose: sqlite-efcore (sqlite-efcore).

When asked for page type, choose: CRUD (CRUD).

When asked to include prerelease packages, answer no.

The scaffolding process adds the following pages to your application:

There is a new folder named StudentPages (under Components/Pages) with all the razor components for CRUD operations.

Edit Components/Layout/NavMenu.razor to add this menu item:

<div class="nav-item px-3">
    <NavLink class="nav-link" href="students">
        <span class="bi bi-lock-nav-menu" aria-hidden="true"></span> Students
    </NavLink>
</div>

Find the following code in Program.cs and delete it because the scaffold tool registered ApplicatioDbContext using DbContextFactory:

builder.Services.AddDbContext<ApplicationDbContext>(options => options.UseSqlite(connectionString));

Run the application with:

dotnet watch

The app will launch in your default browser.

After the web app launches in your default browser, click on students in the left navigation. A list of students will display in the main panel

You should have the full CRUD experience once you click on Students in the left navigation.

QuickGrid

If you open StudentPages/Index.razor in your editor, you will notice that the QuickGrid Blazor component is being used to display students data. This is the code that uses QuickGrid:

<QuickGrid Class="table" Items="context.Students">
  <PropertyColumn Property="student => student.FirstName" />
  <PropertyColumn Property="student => student.LastName" />
  <PropertyColumn Property="student => student.School" />
  <PropertyColumn Property="student => student.DateOfBirth" />

  <TemplateColumn Context="student">
    <a href="@($"students/edit?studentid={student.StudentId}")">Edit</a> |
    <a href="@($"students/details?studentid={student.StudentId}")">Details</a> |
    <a href="@($"students/delete?studentid={student.StudentId}")">Delete</a>
  </TemplateColumn>
</QuickGrid>

Visit the QuickGrid for Blazor site for more information on this freely available component.

Pagination

Let's extend the QuickGrid component to add paging to StudentPages/Index.razor. Add this directive right under the first line (@page "/students"):

@rendermode InteractiveServer

The above directive tells Blazor that a component should be rendered on the server and made fully interactive using the Blazor Server model.

Add this instance variable to the @code { .... } block

private PaginationState pagination = new PaginationState { ItemsPerPage = 10 };

Next, Add the following attribute to the opening <QuickGrid .... > tag:

Pagination="@pagination"

Finally, place this Paginator component right after the closing </QuickGrid> tag:

<Paginator State="@pagination" />

Refreah the Students List page. You will see pagination in action.

Sorting

Making the columns sortable involves just adding an attribute to the <PropertyColumn...> opening tags for FirstName, LastName, School, and DateOfBirth as follows:

<PropertyColumn Property="student => student.FirstName" Sortable="true" />
<PropertyColumn Property="student => student.LastName" Sortable="true" />
<PropertyColumn Property="student => student.School" Sortable="true" />
<PropertyColumn Property="student => student.DateOfBirth" Sortable="true" />

You can now click on the column titles to sort the columns as needed:

Filtering

Add this filter input field right above the <Quickgrid ...> opening tag:

<p>There are @filtered.Count() students.</p>

<div class="search-box">
    <input class="form-control me-sm-2 " style="width: 95%" type="search" autofocus @bind="itemsFilter"
           @bind:event="oninput" placeholder="Search Filter ..." />
</div>

Add these C# properties inside the @code { . . .  } block:

string? itemsFilter;

private List<Student>? studentList {
  get {
    var data = context.Students!.ToList();
    if (!data.Any()) {
      return null;
    } else {

      return data;
    }
  }
}

private IQueryable<Student> filtered {
  get {
    if (studentList == null || !studentList.Any()) {
      return Enumerable.Empty<Student>().AsQueryable();
    }

    if (string.IsNullOrEmpty(itemsFilter)) {
      return studentList!.AsQueryable();
    } else {
      var filteredList = studentList!.AsQueryable()
      .Where(
      b => b.FirstName!.Contains(itemsFilter, StringComparison.CurrentCultureIgnoreCase)
      || b.LastName!.Contains(itemsFilter, StringComparison.CurrentCultureIgnoreCase)
      || b.School!.Contains(itemsFilter, StringComparison.CurrentCultureIgnoreCase)
      || b.Gender!.Contains(itemsFilter, StringComparison.CurrentCultureIgnoreCase)
      || b.DateOfBirth.ToString()!.Contains(itemsFilter, StringComparison.CurrentCultureIgnoreCase)
      );
      return filteredList;
    }
  }
}

In the opening <QuickGrid ... > tag, change the value of the Items attribure from "context.Students" to simply:

@filtered

You can now filter any text in any of the columns as shown below:

If you enter 1998 in the filter, all those born in 1998 will display in the main panel.

Component CSS

With Blazor components, it is easy create CSS that targets individual components. Simply create a file with the same name as the component and add to it .css. 

For example:

Create a file named StudentPages/Index.razor.css in the same folder as the component itself with this styling:

p.create-new {
    background-color: orange;
}

In StudentPages/Index.razor component, add the following styling to the <p> tag that contains the “Create New” link as follows:

<p class="create-new">
    <a href="students/create">Create New</a>
</p>

You will notice that styling is successfully applied to the StudentPages/Index.razor component.

Image showing that the styling in StudentPages/Index.razor.css has affected the Index.razor component by setting the background color of the "Create New" link to orange.

The .NET scaffold tool can be used for more than creating pages for Blazor app. Among other things, it can be used for scaffolding Aspire, API, MVC Controllerts, and Itentity.

Sunday, October 19, 2025

Small Language Models with AI Toolkit Extension in VS Code

In this article, we will see how we can work with small language models (SLM) from the AI Toolkit extension in VS Code. Though the toolkit can do other things, our focus is to consume an ONNX SLM hosted on Visual Studio Code from a C# application. We will first look at an example that is based on OpenAI packages. We will later use a similar example based on the Sematic Kernal approach.

Companion Video: https://youtu.be/V_eWAM2fxJg

Prerequisites

You will need:

  • The latest version of VS Code
  • .NET version 9.0 or higher

What are small language models (SLMs)?

Small Language Models (SLMs) are compact versions of large language models (LLMs), designed to deliver strong performance in natural language tasks while using significantly fewer computational resources.

What is the AI Toolkit Extension in VS Code?

The AI Toolkit Extension for Visual Studio Code is a powerful, all-in-one environment for building, testing, and deploying generative AI applications—especially useful for developers working with small language models (SLMs).

Getting Started

Install the following Visual Studio Code extension:


Click on the three dots (...) in the left navigation of VS Code, and choose "AI Toolkit".

Click on "Model Catalog".

Scroll down down the list until you find “Local Models” >> ONNX >> Minstral 7B – (CPU – Small, Standard) >> + Add Model.

Once the model is fully downloaded, it will appear under Models >> ONNX.

Right-click on the model and select “Copy Model Name”.

I copied the following name for the "Minstral 7B" model: 

mistral-7b-v02-int4-cpu

Using OpenAI packages

Create a C# console application named AIToolkitOpenAI and add to it required packages with the following terminal window commands:

dotnet new console -n AIToolkitOpenAI
cd AIToolkitOpenAI
dotnet add package OpenAI

Start VS Code with:

code .

Click on the "AI Toolkit" tab in VS Code and make sure that the "Minstral 7B" model is running.

Replace content of Program.cs with this code:

using OpenAI;
using OpenAI.Chat;
using System.ClientModel;
using System.Text;

var model = "mistral-7b-v02-int4-cpu";
var baseUrl = "http://localhost:5272/v1/"; // root URL for local OpenAI-like server
var apikey = "unused";

OpenAIClientOptions options = new OpenAIClientOptions();
options.Endpoint = new Uri(baseUrl);
ApiKeyCredential credential = new ApiKeyCredential(apikey);
ChatClient client = new OpenAIClient(credential, options).GetChatClient(model);

// Build the prompt
StringBuilder prompt = new StringBuilder();
prompt.AppendLine("You will analyze the sentiment of the following product reviews.");
prompt.AppendLine("Each line is its own review. Output the sentiment of each review in");
prompt.AppendLine("a bulleted list and then provide a general sentiment of all reviews.");
prompt.AppendLine();
prompt.AppendLine("I bought this product and it's amazing. I love it!");
prompt.AppendLine("This product is terrible. I hate it.");
prompt.AppendLine("I'm not sure about this product. It's okay.");
prompt.AppendLine("I found this product based on the other reviews. It worked");

// send the prompt to the model and wait for the text completion
var response = await client.CompleteChatAsync(prompt.ToString());
// display the response
Console.WriteLine(response.Value.Content[0].Text);

Run the application with:

dotnet run

The application does sentiment analysis on what customers think of the product.

This is a sample of the output:

* I bought this product and it's amazing. I love it!: Positive sentiment
* This product is terrible. I hate it.: Negative sentiment
* I'm not sure about this product. It's okay.: Neutral sentiment
* I found this product based on the other reviews. It worked for me.: Positive sentiment

General sentiment: The reviews contain both positive and negative sentiments. Some customers expressed their love for the product, while others expressed their dislike. Neutral sentiment was also expressed by one customer. Overall, the reviews suggest that the product has the potential to elicit strong feelings from customers, both positive and negative.

Sematic Kernel packages

Create a C# console application named AIToolkitSK and add to it required packages with the following terminal window commands:

dotnet new console -n AIToolkitSK
cd AIToolkitSK
dotnet add package Microsoft.SemanticKernel

Start VS Code with:

code .

Click on the "AI Toolkit" tab in VS Code and make sure that the "Minstral 7B" model is running.

Replace content of Program.cs with this code:

using System.Text;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;

var model = "mistral-7b-v02-int4-cpu";
var baseUrl = "http://localhost:5272/v1/";
var apikey = "unused";

// Create a chat completion service
var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion(modelId: model, apiKey: apikey, endpoint: new Uri(baseUrl))
    .Build();
var chat = kernel.GetRequiredService<IChatCompletionService>();
var history = new ChatHistory();
history.AddSystemMessage("You are a useful chatbot. Always reply in a funny way with short answers.");
var settings = new OpenAIPromptExecutionSettings
{
    MaxTokens = 500,
    Temperature = 1,
};

while (true)
{
    Console.Write("\nUser: ");
    var userInput = Console.ReadLine();
    if (string.IsNullOrWhiteSpace(userInput)) break;

    history.AddUserMessage(userInput);

    var responseBuilder = new StringBuilder();
    Console.Write("\nAI: ");
    await foreach (var message in chat.GetStreamingChatMessageContentsAsync(userInput, settings, kernel))
    {
        responseBuilder.Append(message);
        Console.Write(message);
    }
}

This is a simple chat completion app.

Run the application with:

dotnet run

My prompt was:

Red or white wine with beef steak?

The response was:

AI:  Both red and white wines can pair well with beef steak, but a red wine is generally the more traditional choice. Red wines, such as Cabernet Sauvignon, Merlot, or Pinot Noir, have flavors that complement the rich and savory flavors of beef. However, if you prefer a lighter taste, a white wine such as Pinot Noir or Chardonnay can also work well with beef steak. Ultimately, it comes down to personal preference.

Conclusion

We have seen how to use SLMs hosted by VS Code through the AI Toolkit extension. We were able to communicate with the model from these two C# applications: (1) a app the uses OpenAI packages, and (2) an app that uses Sematic Kernel.

Sunday, February 2, 2025

Using PHP with AI models hosted on GitHub

Overview

In this article I will show you how you can experiment with AI models hosted on GitHub in a simple PHP web app. GitHub AI Models are intended for learning, experimentation and proof-of-concept activities. The feature is subject to various limits (including requests per minute, requests per day, tokens per request, and concurrent requests) and is not designed for production use cases.

Prerequisites

To proceed, you will need the following:

  • PHP - You need to have PHP version 8.3 (or higher) installed on your computer. You can download the latest version from https://www.php.net/downloads.php.
  • Composer – If you do not have Composer yet, download and install it for your operating system from https://getcomposer.org/download/.

Getting Started

There are many AI models from a variety of vendors that you can choose from. The starting point is to visit https://github.com/marketplace/models. At the time of writing, these are a subset of the models available:


For this article, I will use the "DeepSeek-R1" beside the red arrow above. If you click on that model, you will be taken to the model's landing page:

Click on the green "Get API key" button.


The first thing we need to do is get a 'personal access token' by clicking on the “Get developer key” button.

Choose 'Generate new token', which happens to be in beta at the time of writing.


Give your token a name, set the expiration, and optionally describe the purpose of the token. Thereafter, click on the green 'Generate token' button at the bottom of the page.


Copy the newly generated token and place it is a safe place because you cannot view this token again once you leave the above page.

Let's do some PHP coding

In a working directory, create a sub-directory named PHP-GitHub-AI inside a terminal window with the following command:

mkdir PHP-GitHub-AI

Change into the newly created directory named PHP-GitHub-AI with:

cd PHP-GitHub-AI

In the PHP-GitHub-AI folder, create a file named index.php and add to it the following code:

<?php

// Set your Azure API key and endpoint
$apiKey = 'PUT-YOUR-PERSONAL-ACCESS-TOKEN-FROM-GITHUB-HERE';
$endpoint = 'https://models.inference.ai.azure.com';

// Define the API endpoint
$url = $endpoint . '/chat/completions';

// Set up the data for the API request
$data = [
    'messages' => [
        [
            'role' => 'system',
            'content' => 'you are an expert in astronomy'
        ],
        [
            'role' => 'user',
            'content' => 'which is the furthest planet to earth?'
        ]
    ],
    'model' => 'DeepSeek-R1',    // gpt-4o   DeepSeek-R1
    'temperature' => 1,
    'max_tokens' => 4096,
    'top_p' => 1
];
// Initialize cURL
$ch = curl_init($url);

// Set cURL options
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    'Content-Type: application/json',
    'Authorization: ' . $apiKey,
]);

curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));

// Execute the API request
$response = curl_exec($ch);

// Check for errors
if ($response === false) {
    echo 'Error: ' . curl_error($ch);
} else {

    // Decode the response
    $result = json_decode($response, true);

    // Print the entire response for debugging
    /*
    echo '<pre>';
    print_r($result);
    echo '</pre>';
    */
    // Check if the 'choices' key exists in the response
    if (isset($result['choices'][0]['message']['content'])) {
        echo '<h3>Generated Text by ' . $result['model'] .':</h3>';
        // Print the generated text
        echo "<p>" . $result['choices'][0]['message']['content'] . "</p>";
    } else {
        if (isset($result['error'])) {
            echo 'Error: ' . $result['error']['message'];
        } else {
            echo 'Error: Unable to retrieve generated text.';
        }
    }
}

// Close cURL

curl_close($ch);
?>

In the above code:

  • Set the value of $apiKey to be the personal access token from GitHub
  • The system prompt is: 'you are an expert in astronomy'.
  • The user prompt is: 'which is the furthest planet to earth?'
  • We will be using the ‘DeepSeek-R1’ model

You can start the PHP web server in the PHP-GitHub-AI folder with this terminal window command:

php -S localhost:8888

Point your browser to http://localhost:8888. The output would look like this:


The output is in markdown format. We will need a library that converts from markdown to HTML. To that end, stop the web server and install the erusev/parsedown package into your application with this terminal window command:

composer require erusev/parsedown

Back in the code, make the following changes:
  • Add this code to the first line of your PHP code:
// composer require erusev/parsedown
require_once 'vendor/autoload.php';

  • Replace the following statement:

echo "<p>" . $result['choices'][0]['message']['content'] . "</p>";

WITH

error_reporting(E_ALL ^ E_DEPRECATED);
$Parsedown = new Parsedown();
$text =  $Parsedown->text($result['choices'][0]['message']['content']);
echo "<p>$text</p>";

Restart the web server with “php -S localhost:8888”. The page now shows a much better looking output:

You can change the AI model from DeepSeek-R1 to any other model (like gpt-4o) and will get similar results.

Monday, January 20, 2025

Phi-3 Small Language Model (SLM) in a PHP app with Ollama and LLPhant framework

Overview

In this tutorial, we will see how easy it is to use the Phi-3 small language model in a PHP application. The best part is that it is free and runs entirely on your local device. Ollama is used to serve the Phi-3 small language model and LLPhant is the PHP framework for communicating with the AI model. 

Prerequisites

To proceed, you will need the following:

What is small language model (SLM)?

A small language model (SLM) is a machine learning model typically based on a large language model (LLM) but of greatly reduced size. An SLM retains much of the functionality of the LLM from which it is built but with far less complexity and computing resource demand.

What is Ollama?

Ollama is an application you can download onto your computer or server to run open-source generative AI small-language-models (SLMs) such as Meta's Llama 3 and Microsoft's Phi-3. You can see the many models available at https://www.ollama.com/library.

What is LLPhant

LLPhant is an open-source PHP Generative AI Framework at https://github.com/LLPhant/LLPhant. 

Getting Started

Download the Ollama installer from https://www.ollama.com/download.

Once you have installed Ollama, run these commands from a terminal window:

ollama pull phi3:latest
ollama list
ollama show phi3:latest

In a suitable working directory, create a folder named PhpAI with the following terminal window command:

mkdir PhpAI

Change into the newly created folder with:

cd PhpAI

Using Composer, install the theodo-group/llphant package by running this command:

composer require theodo-group/llphant

Let's get coding

Create a file named index.php with the following content:

<?php

require_once 'vendor/autoload.php';

use LLPhant\OllamaConfig;
use LLPhant\Chat\OllamaChat;  
 
$config = new OllamaConfig();
$config->model = 'phi3'; 
 
$chat = new OllamaChat($config); 
 
$chat->setSystemMessage('You are a helpful assistant who knows about world geography.'); 
 
$response = $chat->generateText('what is the capital of france?');  
 
echo $response;
?>

Running the app

To run the app, start the PHP web server to listen on port number 8888 with the following command in the PhpAI folder.

php -S localhost:8888

You can view the output by pointing your browser to the following URL:

http://localhost:8888/

This is what I experienced:

Conclusion

We can package our applications with a local SLM. This makes our applications cheaper, faster, connection-free, and self-contained.

Monday, January 13, 2025

Adding a UI to your WebAPI in ASP.NET 9.0

You may have noticed that when you create a WebAPI project in .NET 9.0, the default swagger UI is not there anymore by default. In this article, I will show you how to restore it back as the default UI. In addition, we will install an alternative UI to swagger named Scalar. The approach used applies to both minimal and controller-based WebAPI.

Companion Video: https://youtu.be/vsy-pIxKpYU
Source Code: https://github.com/medhatelmasry/WebApiDemo

Getting Started

Let us first create a minimal WebAPI project with the following terminal command:

dotnet new webapi -o WebApiDemo
cd WebApiDemo

Open the project in VS Code and note the following statements in Program.cs:

  • Around line 5 is this statement, which adds the OpenApi service:

builder.Services.AddOpenApi();

  • Also, around line 12 is this statement:

app.MapOpenApi();

The above statements produce the following JSON endpoint file describing your API when you run your app:

/openapi/v1.json

This displays a page like the following in your browser:

Restoring Swagger UI

Note that swagger UI is nowhere to be found. It is quite straight forward to add a Swagger UI to your .NET 9.0 WebAPI project. Start by adding the following package to your project:

dotnet add package Swashbuckle.AspNetCore

You can now add the following statement right under app.MapOpenApi():

app.UseSwaggerUI(options =>  {
    options.SwaggerEndpoint("/openapi/v1.json", "My WebAPI");
});

Now, run the project and go to endpoint /swagger in your browser. You should see this UI:

NOTE: If you want the Swagger UI to be accessed at the / (root) endpoint, then you can add this option:

options.RoutePrefix = "";

Scalar: alternative to Swagger

There is a much better alternative to swagger named Scalar. It offers you an enhanced UI and some additional features. Let us explore Scalar. 

To use Scalar in an ASP.NET 9.0 WebAPI application, you must add the following package:

dotnet add package Scalar.AspNetCore

Comment out the following code in Program.cs:

// app.UseSwaggerUI(options => 
// {
//     options.SwaggerEndpoint("/openapi/v1.json", "My WebAPI");
// });

Replace the above commented-out code with this:

app.MapScalarApiReference();

That's all you need to do. Let’s see how the Scalar UI looks like. Run the project and point your browser to this endpoint:

/scalar/v1

Explore this UI. One interesting feature is that you can view client code in a variety of languages. Here is what it looks like if you choose C#:


You can further customize the UI by enabling and disabling various options. For example, replace the statement app.MapScalarApiReference() with:

app.MapScalarApiReference(options => {
    options
        .WithTitle("My WebAPI")
        .WithTheme(ScalarTheme.Moon)
        .WithDefaultHttpClient(ScalarTarget.CSharp, ScalarClient.HttpClient);
});

This results in a less dark theme and a default client code of C#.

Making Scalar UI the default page

To make the Scalar UI the default page when launching your WebAPI page with “dotnet watch”, edit the /Properties/launchSettings.json file and make the following changes:

1. In both http and https blocks, add this item:

"launchUrl": "scalar/v1"

2. Also, in both http and https blocks, change the value of launchBrowser to true.

Now when you restart your webAPI web app with “dotnet watch”, the Scalar UI is automatically loaded in your default browser.

Happy Coding!