Tuesday, August 8, 2023

Creating Unit Tests and Maintainable Code with Moq and NUnit in 5 Minutes



What is a Unit Test?

If you have been coding for more than 5 minutes that you have heard of the term Unit Test. You have also heard that you need to have unit tests in your code. But why do I need this test when you can attach the code to a debugger and step through all the code and verify it works. It seems like a lot of unnecessary steps and time that could be spent improving the project. While in small projects this may be true, when working with large enterprise projects with thousands of lines of code the need for unit testing becomes obvious.

As a project grows so does our dependencies on the code. For example, an email service may be referenced throughout the project. Meaning if you update the email service all the references need to be verified that they are in a working state after the change. This can add hours if stepping through each line is necessary. If an update to the code that depends on the email service changes, then all the lines of code with this dependency need to be tested as well. As you can see the chain of testing can get quite extensive.

Unit tests will ensure our code is tested and that any dependencies are still working after an update. Unit testing also allows for testing of several user scenarios that may not come up during initial development. These edge cases can be identified and tested against. This also helps with code updates since these edge cases may not be remembered. For example, you might mail all orders USPS except to those in Texas must be mailed FedEx.  However, FedEx does not deliver to PO Boxes so a unit test for testing PO Boxes is needed to ensure updates to the mail service does not break our shipments to Texas as the next person may not know this inherit knowledge about FedEx which can cause a bug down the road.

In order to cover all our test cases the method of Test Driven Development or TDD for short can be used. TDD is when test cases are created before any coding of the feature is developed. This allows for the developer to think of test scenarios and try to think of edge cases before any logic is written as the logic can skew the developer into not thinking an edge case exists. For example, a simple function called "AddNumbers" which adds two numbers together is very simple. I write the code and add a unit test that inputs 2 numbers and the test returns success. Code works but is open for bugs, what happens if I pass a letter, a null value, an extremely large number, etc. Because I didn't sit down and think of scenarios before writing my function my tests passed but I didn't cover the entire out user cases available.  There are extensions that help with this such as Fine Code Coverage that help with identifying tests that are missed, but this will only go so far if you are not thinking of the use case to handle in the first place.

Unit Testing Terms

There are many terms that refer to testing. These terms are known as "Test Doubles" or referring to what is going to be standing in place of our dependencies like an stunt double would for an actor. 

Some of the most common are Dummy, Fake, Stub, and Mock. These terms are very similar to each other and the descriptions below are how I view them. They may not fit the exact description, but the name of the test double isn't as important as that it is used.

Dummy

Data and objects just used to keep a complier from yelling at you. It has no outcome on the test itself

Fake

A fake is when an object implementation is replaced with a simpler execution and usually replaces dependencies on outside sources. For example, a SQL database call would require a SQL database, the fake would simulate this call and return objects form say a json file or in memory database.

Stub

A stub is a object that is designed to create responses that are predictable so that our test knows what to expect. When unit testing a stub is used when the object's functionality is not necessary a response is needed to complete the test. In construction, stub out is when you leave a pipe out of the dry wall and hide all the plumbing behind. All you care about is the exposed pipe. Same is true in testing, we just need a response, we do not care how the response was created.

Mock

Mocks are a step up from a stub, a stub will always return. Mocks can contain some logic to validate what is being returned. This helps when you need basic logic and null checking. These come in handy for testing edge cases and other variable cases than just the clean path a stub would provide.



Setting up a Unit Test

Planning for unit testing needs to happen at the beginning of a project. To introduce unit testing to a mature project is a large undertaking. Taking a test driven approach allows for understanding the requirements better, planning for edge cases and allows for good design practices.

Using Interfaces in C# is a must when it comes to unit testing setup and design. The interface allows for creating different dependencies for our tests than our project. This allows for stubbing and mocking our dependencies to test our code. When ever there is a dependency on another class this is an indication of a new stub. Using Inversion of Control (dependency injection) will allow for the dependencies to be swapped out for testing and not require the code to be dependent on external items.

Arrange, Act, Assert

Unit tests consist of 3 parts per test: Arrange, Act, Assert. Arrange is prepping the data for the function test. This can be initializing Mocks or Stubs, prepping data models, or initializing variables. Act is next is is executing the function that needs to be tested. Finally Assert, assert is checking that the function executed successfully and returned the correct data. Digging into assert more opens the question, how do you test void and Task functions? NUnit has specific ways for testing these, but a professor once told me to avoid void functions whenever possible. This becomes apparent here in unit testing why you should. Void means empty, nothing, vacant a function that you execute and have no idea what happens, when you think of it in these terms a function should return at the very least bool. Yes I worked or no I didn't giving a concrete answer to assert than just it ran. Another reason is error handling, exceptions are expensive instead of throwing an error, return false and handle the error outside the function for a more flexible application.

Creating a Unit Test in 5 minutes

Create the Web Project

All Blazor projects come with an example of a weather app that fetches random weather forecasts. For our 5 minute project we will repurpose this code to be testable.To begin create a new blazor server application


dotnet new blazorserver -o FiveMinuteProject

Modify the Web Project to be Testable

Currently the weather is just a random set of integers btween -20 and 55. To make our WeatherForecastService testable lets incapsulate the weather call to a repository. First, create a new class called WeatherRepository.cs  under the data folder. Add a method called GetForecast, accepts an int range. Copy Enumerable Logic from the weather service.


static Random random = new Random(5);

/// 
/// Returns an array of WeatherForecast objects
/// representing the weather for the next Range days.
/// 
public WeatherForecast[] GetForecast(int Range)
{
	var rng = new Random();
	return Enumerable.Range(1, Range).Select(index => new WeatherForecast
	{
		Date = DateTime.Now.AddDays(index),
		TemperatureC = random.Next(-20, 55),
		Summary = Summaries[random.Next(Summaries.Length)]
	})
	.ToArray();
}

Next, create a folder under data called interfaces, then add an interface call IWeatherRepository. Add interface to weather repository with a function reference to the repository method just created.


public interface IWeatherRepository
{
	WeatherForecast[] GetForecast(int Range);
}

After the interface is created, go back and the repository class and inherit the interface. 


public class WeatherRepository : IWeatherRepository
{
	private static readonly string[] Summaries = new[]
	{
		"Freezing", "Bracing", "Chilly", "Cool", "Mild", "Warm", "Balmy", "Hot", "Sweltering", "Scorching"
	};
	
	....


Go back to WeatherForecastService, and add a reference to the newly created interface. We will use dependency injection to inject the repository into the service so our dependency is loosely coupled. To do this add a readonly field to the service and a constructor that takes the interface as a parameter.


IWeatherRepository _weatherRepository;

public WeatherForecastService(IWeatherRepository weatherRepository)
{
	_weatherRepository = weatherRepository;
}

To call our repository, an update must be made to the GetForcast method. The method takes a DateTime so it must be checked that it is valid. Then the DateTime is converted to the range, or the number of days that are being fetching. Finally, call the repository.


public Task GetForecastAsync(DateTime startDate)
{
	int range = 5;
	DateTime now = DateTime.Now;

	if(startDate > now)
		throw new ArgumentException("startDate cannot be in the future", nameof(startDate));

	range = now.Subtract(startDate).Days;
	
	return Task.FromResult(_weatherRepository.GetForecast(range));
}

Last open the fetchdata.razor. At the top add a dropdown with 3, 7, 10 as options. default to 7. Then add an on change call blazor function that will be linked to the dropdown. Finally, update GetForecast function to convert days to DateTime and fetch the forecast. 


@page "/fetchdata"

Weather forecast

@using FiveMinuteProject.Data
@inject WeatherForecastService ForecastService

<h1>Weather forecast</h1>

<p>This component demonstrates fetching data from a service.</p>

<select bind="selectedTimeFrame">
    @foreach (var timeFrame in timeFrames)
    {
        <option value="@timeFrame">@timeFrame</option>
    }
<select>
<button onclick="OnSelectedTimeFrameChanged">Get Forecast</button>
@if (forecasts == null)
{
    <p><em>Loading...</em></p>
}
else
{
     
	<table class="table">
		<thead>
			<tr>
    			<th>Date</th>
    			<th>Temp. (C)</th>
                <th>Temp. (F)</th>
                <th>Summary</th>
            </tr>
        </thead>
        <tbody>
        @foreach (var forecast in forecasts)
        { 
        	<tr>
            	<td>@forecast.Date.ToShortDateString()</td>
                <td>@forecast.TemperatureC</td>
                <td>@forecast.TemperatureF</td>
                <td>@forecast.Summary</td>
            </tr>
         }
         </tbody>
    </table>
}

@code {
    private int[] timeFrames = new int[] { 3, 7, 10 };
    private int selectedTimeFrame;

    private WeatherForecast[]? forecasts;

    protected override async Task OnInitializedAsync()
    {
        selectedTimeFrame = 7;
        forecasts = await GetForecast();
    }

    private async Task OnSelectedTimeFrameChanged()
    {
        forecasts = await GetForecast();
    }

    private async Task GetForecast()
    {
        Console.WriteLine(selectedTimeFrame.ToString());
        return await ForecastService.GetForecastAsync(DateTime.Now.AddDays((selectedTimeFrame) * -1));
    }
}


Why are we converting from numbers in the dropdown to date, then back to numbers? That seems redundant. Answer is easy, we need something to test in our unit test!

Why are we using interfaces here? An interface creates a contract to the dependency for service. What this allows us to do is extract the dependency and create fake data to test our Weather Service. A weather repository is probably reliant on a 3rd party service such as weather.gov to get it's information. A unit test is meant to test our current classes code logic, and having a dependency on a weather API can make testing tricky. The interface allows us to create Mocks and Stubs so our unit test doesn't need to rely on the API service anymore and allows control over what is returned making the unit test more efficient and accurate.

Setup Unit Test

With the web project is created, the test project needs to be setup. To create a NUnit test project run the following terminal command


dotnet new nunit -o FiveMinuteProject.Tests

Next, the test needs to reference the web project. To do this, run this command in the terminal


dotnet add FiveMinuteProject.Tests/FiveMinuteProject.Tests.csproj reference FiveMinuteProject/FiveMinuteProject.csproj


Finally run the nuget command to install "Moq"

The Unit Test project is setup for testing

Create a Stub

For our example, the WeatherService is going to be tested. The weather service has a dependency on the weather repository. In this case we will stub out a response to test the logic of our weather service. Moq can also be used for mocking your interface, in this example a mock is not needed.

to create a stub, first create a new class and call it WeatherRepositoryStubs. Then we will create a class called build. I like to use the builder design pattern for stubs that way all our stubs will have a build function and is called to create our stub. In this example we will just create a function called BuildStub and forego the builder pattern to keep the example quick.



public class WeatherRepositoryStubs
{
	Mock mockWeatherRepository = new Mock();
	//Create a stub for IWeatherRepository using Moq

	public IWeatherRepository BuildStub()
	{
		mockWeatherRepository.Setup(x => x.GetForecast(It.IsAny()))
			.Returns(new WeatherForecast[]
			{
				new WeatherForecast
				{
					Date = DateTime.Now,
					TemperatureC = 32,
					Summary = "Freezing"
				}
			});
		return mockWeatherRepository.Object;
	}

}

Once the stub is created, the tests can now be setup.

Create the Test

In the test project, a new class is created called WeatherForecastService_Tests.cs. In order to use NUnit our class needs to be set as a test fixture. To do this use the text decorator to add TestFixture. Next the Setup function is created. I like to use the setup function to initiate my service and inject my stubs. This keeps me from repeating the constructor call in each arrange section of the test. some may require the setup of the constructor done in the arrange section so different stubs can be passed to produce errors such as a null reference exception.

Finally we can create tests. Each function will have a decorator [Test] so NUnit knows which functions are tests. I have setup 5 tests to represent different scenarios for the service.

    1. startDate is in the future
    2. startDate is in the past
    3. startDate is today
    4. startDate is DateTime.MinValue
    5. startDate is DateTime.MaxValue



using System;
using NUnit.Framework;
using System.Threading.Tasks;
using FiveMinuteProject;
using FiveMinuteProject.Data;
using FiveMinuteProject.Data.Interfaces;


namespace FiveMinuteProject.Tests;

[TestFixture]
public class WeatherForecastService_Tests
{
    //create test cases for the following: WeatherForecast[] GetForecastAsync(DateTime startDate, int range)
    //1. startDate is in the future
    //2. startDate is in the past
    //3. startDate is today
    //4. startDate is DateTime.MinValue
    //5. startDate is DateTime.MaxValue

    WeatherForecastService weatherForecastService;

    [SetUp]
    public void Setup()
    {
        WeatherRepositoryStubs weatherRepositoryStubs = new WeatherRepositoryStubs();
        IWeatherRepository weatherRepository = weatherRepositoryStubs.BuildStub();
        weatherForecastService = new WeatherForecastService(weatherRepository);
    }

    
    
    [Test]
    public void GetForecastAsync_StartDateIsInTheFuture_ThrowsArgumentException()
    {
        //Arrange
        
        DateTime startDate = DateTime.Now.AddDays(1);
        
        //Act
        //Assert
        Assert.Throws(() => weatherForecastService.GetForecastAsync(startDate));
    }

    [Test]
    public async Task GetForecastAsync_StartDateIsInThePast_ReturnsWeatherForecastArray()
    {
        //Arrange
        DateTime startDate = DateTime.Now.AddDays(-1);
        
        //Act
        var result = await weatherForecastService.GetForecastAsync(startDate);
        
        //Assert
        Assert.IsInstanceOf(result);
    }

    [Test]
    public async Task GetForecastAsync_StartDateIsToday_ReturnsWeatherForecastArray()
    {
        //Arrange
        DateTime startDate = DateTime.Now;
        
        //Act
        var result = await weatherForecastService.GetForecastAsync(startDate);
        
        //Assert
        Assert.IsInstanceOf(result);
    }


    [Test]
    public async Task GetForecastAsync_StartDateIsDateTimeMinValue_ReturnsWeatherForecastArray()
    {
        //Arrange
        DateTime startDate = DateTime.MinValue;
        
        //Act
        var result = await weatherForecastService.GetForecastAsync(startDate);
        
        //Assert
        Assert.IsInstanceOf(result);
    }

    [Test]
    public void GetForecastAsync_StartDateIsDateTimeMaxValue_ReturnsWeatherForecastArray()
    {
        //Arrange
        DateTime startDate = DateTime.MaxValue;
        
        //Act
        //Assert
        Assert.Throws(() => weatherForecastService.GetForecastAsync(startDate));
    }

}

Run the Test

Finally to run our tests, all that needs to be done is call the command "dotnet test". If you are using Visual Studio, the tests will show in the test explorer.

That's it, unit tests using Moq And NUnit created in 5 minutes. You can find the project on my Github repo here: https://github.com/fiveminutecoder/blogs/tree/master/FiveMinuteUnitTests


Wednesday, March 29, 2023

Boost your productivity and creativity with ChatGPT as a C# developer with these 5 tips




ChatGPT is a language model that is optimized for conversational interfaces. It can interact with users in a natural and engaging way, and can also perform tasks such as generating code, optimizing code, and testing code .

 

Here are 5 ways you can use ChatGPT to boost your productivity as a C# developer:

 

Code Optimization

 Optimizing your code can improve its performance, readability, and maintainability. However, it can also be challenging and complex, especially for large and legacy codebases. ChatGPT can help you optimize your code by suggesting improvements and refactorings based on best practices and coding standards. You can ask ChatGPT to review your code and provide feedback on how to make it more efficient, elegant, and consistent. You can also ask ChatGPT to apply the suggested changes automatically or manually.

Chat GPT is a powerful tool that can help you optimize your code with examples for c#. Chat GPT is a chatbot that uses natural language processing and deep learning to understand your coding problems and generate solutions. You can ask Chat GPT to optimize your code for speed, memory, readability, or any other criteria you specify. Chat GPT can also provide you with examples of how to use different features or libraries in c#, such as LINQ, async/await, delegates, etc.

 

For example, suppose you have a code snippet that calculates the sum of all the even numbers in an array:

 


int[] numbers = {1, 2, 3, 4, 5, 6};

int sum = 0;

for (int i = 0; i < numbers.Length; i++)
{
    if (numbers[i] % 2 == 0)
    {
        sum += numbers[i];
    }
}

 

You can ask Chat GPT to optimize this code for speed and readability. Chat GPT might generate something like this:

 


int[] numbers = {1, 2, 3, 4, 5, 6};

int sum = numbers.Where(n => n % 2 == 0).Sum();

 

This code uses LINQ to filter the even numbers and sum them in one line. This is faster and more readable than the original code.

 

As you can see, Chat GPT can optimize your code with examples for c# in various ways. You can use Chat GPT to improve your coding skills and productivity. Chat GPT is your friendly and helpful coding assistant.

Code Generation

Sometimes you may need to write new code from scratch or extend existing code with new features or functionalities. ChatGPT can help you generate code based on your natural language description or specification. You can simply tell ChatGPT what you want to achieve or implement, and it will write the corresponding C# code for you. This can speed up your development process and also help you explore new ideas and possibilities.

 

Another example is if you want to learn how to use async/await in c#. You can ask Chat GPT to show you an example of how to make an asynchronous HTTP request using HttpClient. Chat GPT might generate something like this:

 


using System.Net.Http;
using System.Threading.Tasks;


async Task GetResponseAsync(string url)
{
    HttpClient client = new HttpClient();

    HttpResponseMessage response = await client.GetAsync(url);

    response.EnsureSuccessStatusCode();

    string content = await response.Content.ReadAsStringAsync();

    return content;
}

 

This code uses the async and await keywords to make an asynchronous HTTP request and return the response content as a string. This is more efficient and elegant than using synchronous methods or callbacks

Code Documentation

 Documenting your code is important for making it understandable, reusable, and maintainable. However, it can also be boring and repetitive, especially for complex and lengthy code. ChatGPT can help you document your code by generating comments, summaries, and descriptions based on your code logic and structure. You can ask ChatGPT to document your code at different levels of granularity, such as methods, classes, modules, or projects. You can also ask ChatGPT to update your documentation when you make changes to your code.

 

It can summarize your code by extracting the main logic and functionality of your program and presenting it in natural language. For example, if you have a code snippet like this in C#:


using System;

class Program
{

    static void Main(string[] args)
    {
        int x = 10;
        int y = 20;
        int z = x + y;

        Console.WriteLine("The sum of x and y is " + z);

    }
}

Chat GPT will then summarize the code like this "The program defines three variables: x, y, and z. It assigns the values 10 and 20 to x and y respectively. It calculates the sum of x and y and assigns it to z. It prints the value of z to the console".


Code Debugging

 Debugging your code can be frustrating and time-consuming, especially when you encounter errors or bugs that are hard to find or fix. ChatGPT can help you debug your code by providing suggestions and solutions based on your error messages or test results. You can ask ChatGPT to explain the cause of an error or bug, suggest possible fixes or workarounds, or apply the fixes automatically or manually.


 Unit Test Generation

 Writing unit tests can be tedious and time-consuming, but they are essential for ensuring the quality and reliability of your code. ChatGPT can help you generate unit tests automatically based on your code and specifications. You can simply provide ChatGPT with your code snippet and some test cases, and it will write the corresponding unit test code for you. This can save you a lot of time and effort, and also help you catch bugs and errors early on.


These are just some of the ways you can use ChatGPT to increase productivity for software developers who use C#. ChatGPT is a powerful and versatile tool that can handle a variety of tasks and scenarios related to C# development. You can try ChatGPT yourself at chat.openai.com or learn more about it at openai.com/blog/chatgpt.



**Note, this was written using AI and was a test of Chat GPT to see if it would increase organic traffic**

Monday, March 6, 2023

How I Used ChatGPT to Respond to my Emails in 5 Minutes



What is ChatGPT?

ChatGPT is an advanced AI chatbot created by the folks at https://openai.com that can accurately reproduce human responses without prior training on the subject. The GPT stands for generative pre-trained transformer, which means the model is already trained. This is different than a traditional chat bot, see my example here https://www.fiveminutecoder.com/2020/12/create-faq-bot-using-microsoft-bot.html, that needs prior knowledge on a subject to create accurate response to the subject. ChatGPT can also be tuned for your business similar to a traditional chat bot system by training the system with additional information. 

What makes ChatGPT so impressive is the confident responses made by the bot. You ask it a question and it will respond with an in depth answer. It also allows for follow up questions giving a feeling of a natural conversation with a human. Many people, including developers, are seeing the power of this and questioning if their job is in danger. While the tool is impressive, it does not replace the extensive knowledge gained by troubleshooting an issue for hours. Also, while the chat bot is confident in its answers this does not mean it is right. 

To test out ChatGPT I decided to make an email response app to respond to all the junk mail I get. This will be an Azure function that runs in 5 minute intervals. It will use the Graph API to check my email for new emails then send the subject/body of the email to the ChatGPT API. I found that the subject helps with a better response. Once I get a response I will reply to the email and set it to read. I wanted to see how the API and bot worked. So I asked it how to create an integration to the API while the system got me started it's response were either incomplete or outdated. 

Let's use ChatGPT to setup Chat GPT

Since Chat GPT is known for giving detailed responses to questions including code, lets just ask the chatbot how to setup ChatGPT in C#. 




Great! this looks like it will work. During setup however, this was wrong. It looks like the NuGet Package was updated to support Open API GPT-3 which changed the code. The updated calls can be found on the GitHub site here: https://github.com/OkGoDoIt/OpenAI-API-dotnet.

Before writing the app, I wanted to test out the API using PostMan. I wanted to get a feel that the chatbot could respond so again, I asked the Chat GPT chatbot for how to use Postman.



Again this looked promising. I setup Postman as the instructions showed and got an error. There was no model parameter passed in the JSON file. The model is quite important the model is what chat bot to use.... I guess that is an 0/2 using Chat GPT to code. 



 In my tests I used two different models "Davinci", which is the most sophisticated, but the slowest, and "Curie" which is a faster model. Out of the two, Davinci came across angrier in it's responses so I decided to use Curie for this example. Here are some of the responses I got from Postman using a junk email, basically they are replies with false information, hilarious!

"I look forward to hearing from you.Please share this with your team and I would be happy to provide details on our past projects.Regards,Shailesh Srinivasan"

" If you could send me your skype ID that would be great. Thank you."

 " I will discuss project portfolio, your team's strengths and skill gaps, the job description and requirements, and how you will benefit from working with us. All of our consultants are seasoned professionals who have worked for fortune 500 companies and top-tier consulting firms. We are typically able to leverage our existing resources to find the right talent for you.I look forward to working with you.Regards,TedFor a free consultation please contact me at ted@TECHstaffing.com. I am happy to help you with your project staffing needs. Please visit our website at www.TECHstaffing.com for more information. Ted KolodziejskyPhone: 1-972-200-1791Email: ted@TECHstaff"
 
 " I would love to chat about the following topics:1. What is the best way to build a strong engineering team?2. What are your hiring challenges?3. What is your IT roadmap for the next 3-5 years?4. What",


From this experience, I dont see Chat GPT taking my job anytime soon, but still to complete the exercise, the auto response Azure Function can be found below.

Creating an Email Auto Responder in 5 minute

To begin, an Azure function must be created, the details to create an Azure function can be found in a previous post here, https://www.fiveminutecoder.com/2021/05/create-email-tracking-campaign-using.html. Also, a Graph API application must be created. Again, details about how to do this can be found in a previous post here, https://www.fiveminutecoder.com/2021/03/creating-azure-document-queue-for.html. For the app permissions, application permissions are necessary. Under the Graph API section, find the mail section. The app will need read/write permissions and send as permissions.




Next the following Nuget packages must be installed.

 Azure.Identity, Microsoft.Graph, Microsoft.Graph.Core, OpenAI


At the top of the function I added my using statements for the installed nuget packages.


using System;
using Microsoft.Azure.WebJobs;
using Microsoft.Extensions.Logging;
using System.Threading.Tasks;
using System.Collections.Generic;
using Microsoft.Graph;
using Microsoft.Graph.Models;
using Azure.Identity;

Next I defined my IDs necessary to access all the apps. This includes the Chat GPT API and Graph API.


//openAISecretKey
private string openAIKey = "";
//ID of the mailbox you want to auto reply from
private string userId = "user id of mailbox";
//ID of the tenant used
private string tenantId = "azure tenant"; 
//App id from created azure app
private string clientId = "registered app client id"; 
//Secret created for the app
private string clientSecret = "registered app secret"; 
//hold our graph context here for our calls
private GraphServiceClient graphService;

Inside the Run function, i setup the calls to get the unread emails then loop through the emails and respond to the email.


[FunctionName("CheckNewEmail")]
public async Task Run([TimerTrigger("0 */5 * * * *")]TimerInfo myTimer,  ILogger log)
{
	try
	{
		log.LogInformation($"C# Timer trigger function executed at: {DateTime.Now}");
		graphService = GetGraphAPIClient();
		List newMessages = await GetNewEmails();
		log.LogInformation("found " + newMessages.Count);
		foreach(Message message in newMessages)
		{
			log.LogInformation("replying to " + message.Subject);
			string response = await GetChatGPTResponse(message.Subject, message.Body.Content);
			await SendEmail(message.Id, message.From, response);
			await UpdateToRead(message.Id);
			log.LogInformation("Reply successful");

		}
	}
	catch(Exception ex)
	{
		log.LogError(ex, ex.Message);
	}
}  


To instantiate the graph service I used the new Azure.Identity to create an authentication scope and then return the created service to be used throughout the application.


//Create the graph service client  that will be used to get and respond to emails
private GraphServiceClient GetGraphAPIClient()
{
	
	string[] scopes = new string[] {"https://graph.microsoft.com/.default" };
	// using Azure.Identity;
	var options = new TokenCredentialOptions
	{
		AuthorityHost = AzureAuthorityHosts.AzurePublicCloud
	};

	ClientSecretCredential clientSecretCredential = new ClientSecretCredential(
		tenantId, clientId, clientSecret, options);

	GraphServiceClient graphClient = new GraphServiceClient(clientSecretCredential, scopes);
	return graphClient;
}


Using the newly created client, a call is made to the Graph API to get all the unread emails using the isRead filter.


//Graph API call to get all unread emails
private async Task> GetNewEmails()
{
	MessageCollectionResponse messages = await graphService.Users[userId].Messages.GetAsync((requestConfiguration) =>{
		requestConfiguration.QueryParameters.Filter = "isRead eq false";
	});
	
	return messages.Value;
}

Once all the emails are fetched, the subject and body are combined into one string and then sent to the Chat GPT API.


//The call to the Chat GPT end point
private async Task GetChatGPTResponse(string Subject, string Body)
{
	OpenAI_API.OpenAIAPI openai = new OpenAI_API.OpenAIAPI(openAIKey);

	//Create a request suitable for the Chat GPT API. It will remove an non readable characters that the API cannot read
	OpenAI_API.Completions.CompletionRequest completionRequest = new OpenAI_API.Completions.CompletionRequest(Subject + "." + Body, OpenAI_API.Models.Model.CurieText,150);

	// Send a request to the ChatGPT model
	OpenAI_API.Completions.CompletionResult response = await openai.Completions.CreateCompletionAsync(completionRequest);

	return response.Completions[0].Text;
}


With an AI generated response, I send an email using the Graph API to the original sender.


//Graph API call to send reply to email
private async Task SendEmail(string MessageId, Recipient RecipientEmail, string Response)
{
	Microsoft.Graph.Users.Item.Messages.Item.Reply.ReplyPostRequestBody reply = new Microsoft.Graph.Users.Item.Messages.Item.Reply.ReplyPostRequestBody
	{
		Message = new Message
		{
			ToRecipients = new List
			{
				new Recipient()
				{
					EmailAddress = new EmailAddress()
					{
						Address = RecipientEmail.EmailAddress.Address,
						Name = !String.IsNullOrEmpty(RecipientEmail.EmailAddress.Name) ? RecipientEmail.EmailAddress.Name : RecipientEmail.EmailAddress.Address
					}
				}
			},
		},
		Comment = Response,
		
	};

	await graphService.Users[userId].Messages[MessageId].Reply.PostAsync(reply);
}


Finally, I set the email to read so it is not picked up by the next call.


//Graph API call to update email to read
private async Task UpdateToRead(string MessageId)
{
	
	//only update the properties we want to update
	Message msg = new Message()
	{
		IsRead = true
	};
	await graphService.Users[userId].Messages[MessageId].PatchAsync(msg);
}


 
That's it! A function for responding to emails has been created and let the spammers be enthralled by the witty comebacks of the AI. To view the code, please visit my GitHub page here: https://github.com/fiveminutecoder/blogs/tree/master/ChatGPTEmail

UPDATE!!!!

With the general release of ChatGPT 3.5 the responses have changed significantly. We can give the bot a persona to respond to the emails which greatly changes the usefulness of the application. While I miss the snarky response of Davinci using ChatGPT 3.5 is the way to go.

To test this in Postman, all that needs to be done is update the body to include messages instead of prompt. You will see the messages section is an array. This is to help with persistence in responses. Also notice system and user role. System role allows me to tell the chat bot how to act, while the user role is the content to respond to.


{
    "messages":[
        {"role": "system", "content": "You are the assistant to the Director of IT. He does not want any meetings"},
        {"role": "user", "content": "email body here!!"}
    ],
    "temperature": 0.7,
    "max_tokens": 3250,
    "top_p": 1,
    "frequency_penalty": 0,
    "presence_penalty": 0,
    "model": "gpt-3.5-turbo-0301"
}


For the C# application instead of the completion endpoint, the ChatCompletion endpoint will be used, this is a quick change to handle the new message array.




var result = await api.Chat.CreateChatCompletionAsync(new ChatRequest()
{
	Model = Model.ChatGPTTurbo,
	Temperature = 0.7,
	MaxTokens = 50,
	Messages = new ChatMessage[] {
	new ChatMessage(ChatMessageRole.System, "You are the assistant to the Director of IT. He does not want any meetings")
		new ChatMessage(ChatMessageRole.User, "email body here!!")
	}
});
C#, C sharp, machine learning, ML.NET, dotnet core, dotnet, O365, Office 365, developer, development, Azure, Supervised Learning, Unsupervised Learning, NLP, Natural Language Programming, Microsoft, SharePoint, Teams, custom software development, sharepoint specialist, chat GPT,artificial intelligence, AI

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