I am happy to share with you that I Just received the email confirmation saying I am becoming a Microsoft Certified Trainer (MCT). I am very happy to be part world biggest learning MCT community.

Suthahar Become a Microsoft Certified Trainer 2020

“Microsoft Certified Trainers (MCTs) are the premier technical and instructional experts in Microsoft technologies. Join this exclusive group of worldwide Microsoft technical training professionals and reap the benefits of MCT training certification and membership” I have received emails with MCT program benefits . If you are interested in becoming an MCT, please review the MCT Program Guide and the MCT Program Agreement to understand the terms of membership.

Suthahar Become a Microsoft Certified Trainer 2020

Power Virtual Agents are an easily created powerful bot and no-code graphical interface without the need for data scientists or developers. It eliminates the gap between the subject matter experts and the development teams building the bots and it minimizes the IT effort required to deploy and maintain a custom conversational solution. In my previous article you learned how to set up and quick overview about power virtual agents.

In this article you will learn more about how you can customize the bot conversation messages. User conversation message called as “Topic” in Power virtual agents. A topic has trigger phrases - these are phrases, keywords, or questions that a user is likely to type that is related to a specific issue - and conversation nodes - these are what you use to define how a bot should respond and what it should do and you can maintain up to 1000 topics in a Bot.


User and System Topic

Once again remind you, before reading this article, You can follow my previous article to learn more about setup and create the first Power Virtual agent Bot application.

When you have successfully create bot applications, you are able to see a number of topics will be created automatically and also you can see how the bot conversation works in practice by testing it in the Test bot pane. For example, a user might type “I want a Business Laptop" into your bot. The AI will recommend the Business Laptop and request you to confirm adding to the cart. Bot will suggest you provide “Yes” or “No”.



There are two topics the category system will generate. You can refer and learn the topic, how you need to implement it for your business use case.

User Topics Category you will find Four pre-populated User Topics that are titled as lessons. These lesson topics can be used to help understand, simple to complex ways of using nodes to create bot conversations.

System Topics is reusable topics like greeting, Say goodbye like that. These are pre-populated topics that you are likely to need during a bot conversation.

Create New Topic

You can create your own topic and maintain up to 1000 topics in a Bot.

Step 1: Go to the Topics tab on the side navigation pane to open the topics page.

Step 2: on the Topic page, Select the New Topic option on the top navigation pane.



Step 3: Provide a topic name, description, and one or more trigger phrases for the topic by clicking on Add button.


Go to Authoring Canvas

Click on Save Topic options on the right corner on the Topics page. Once click on Save topic, you will get the error and asking you to set Authoring Canvas



Power Virtual Agents open the topic in the authoring canvas and displays the topic's trigger phrases. The authoring canvas is where you define the conversation path between a customer and the bot, message Edit text box having styling feature, add and change the text font style and hyperlink


Build and Save Topic

Once you have done the changes, Click on Topic Checker. Topic checker will show you if there are any errors or warnings in the topic. If there's no error, Click on Save options.


Test Bot Application

You're now ready to test your bot. You will test your bot using the built-in Power virtual agent bot Emulator. You can provide user input, which we created a topic say “Can you suggest a Microsoft AI Learning Blog” and Bot will reply to the triggered message.


Summary


You have successfully created topics and run the application in the power virtual agent. I hope you have enjoyed learning about the Power virtual agent bot. If you have any questions/feedback/issues, please write them in the comment box.

Power Virtual Agents is an easily created powerful bot and no-code graphical interface without the need for data scientists or developers. It eliminates the gap between the subject matter experts and the development teams building the bots and it minimizes the IT effort required to deploy and maintain a custom conversational solution.

This article will demonstrate how easy it is to setup and build a Bot using Power Virtual Agents. The Bot will call a Power Automate Flow to return data from CDS and display it to the user. Power Virtual Agents is part of Microsoft AI and it allows Bots to be created using a Flow like designer without any code

Power Virtual Agents: Build a Microsoft Bot Application

Setup Power Virtual Agents Power Virtual Agents is a software-as-a-service (SaaS). It allows you to easily sign up, create your bot, and embed it into your website and integrate to the different channels with just a few clicks. There’s no infrastructure to maintain or complex systems to deploy.

1. Navigate to Power Virtual Agents and select Start Free and Sign in/ Signup using your work school account.

Power Virtual Agents: Build a Microsoft Bot Application

Create New Bot Application You can create a new bot by selecting the icon on the title bar to open the Bots pane and then selecting New bot, let you select the default environment for now.

Power Virtual Agents: Build a Microsoft Bot Application

Customize your Greeting The Power virtual agent dashboard with sequence steps is the author topic , publish bot and Monitor the performance. You can click on customize your greeting and will see the details below

Power Virtual Agents: Build a Microsoft Bot Application

Select the options “Customize your greeting” and you can see the default user trigger options and message template. Initial trigger phase will be, which user provides the trigger message and based on that bot will reply the message.

Power Virtual Agents: Build a Microsoft Bot Application

Test Your Bot Application You're now ready to test your bot. You will test your bot using the built-in Power virtual agent bot Emulator. Start give the user Input and also refer below image, how to Trigger phase and message sync with user replay


Power Virtual Agents: Build a Microsoft Bot ApplicationPower Virtual Agents: Build a Microsoft Bot Application
Summary 

You have successfully done the setup and built a bot application in the power virtual agent. I hope you have enjoyed learning about the Power virtual agent bot. If you have any questions/feedback/issues, please write them in the comment box.
The Azure AD is the identity provider, responsible for verifying the identity of users and applications and providing security tokens upon successful authentication of those users and applications.in this article I have explained about create Azure AD authentication and integrate into bot application using AuthBot library.



The Bot show very simple dialog with openUrl button and this button launches the web browser for validate user credential and AD will response the message with authentication code, you can copy same code and reply back to the bot, bot will validation and response the welcome message.

You can follow below given steps one by one and you will get to see an interesting demo at end of article.

Azure AD App registration:

I will show the steps given below for the azure application creation, user creation and permission configuration. While implementing bot application, We need Client ID, tenant, return URL, so here I will show how to get all the configuration information from the steps given below.

Step 1: 

Login to Microsoft Azure portal and choose Azure Active Directory from the sidebar.

Step 2:

 If you have not created AZURE Active directory, try to create new AD creation for tenant url or Select or add tenant url from Domain names sections

Building Bot Application with Azure AD Login Authentication using AuthBot

Step 3: 

Select Application Registration and Provide the details given below, name for the application , application type must be Web app/API, enter your application redirect URL and click on Create.

Building Bot Application with Azure AD Login Authentication using AuthBot

Step 4: 

We need to give the permission to access the application from Bot, so grand the permission. Select newly created Application > select Required Permission > Click Grand permission.

Step 5: 

create new user from users and groups sections (optional)

Step 6:

 Create client secret key from Application. Select Application > Select keys > add new / copy client secret key .

Building Bot Application with Azure AD Login Authentication using AuthBot

Step 4: 

You can copy tenant, client ID and Client Secret and you can follow below steps for create and implement AD authentication in Bot

Create New Bot Application:

Let's create a new bot application using Visual Studio 2017. Open Visual Studio > Select File > Create New Project (Ctrl + Shift +N) > Select Bot application.

Building Bot Application with Azure AD Login Authentication using AuthBot


The Bot application template gets created with all the components and all required NuGet references installed in the solutions.

Building Bot Application with Azure AD Login Authentication using AuthBot

Install AuthBot Nuget Package:

The AuthBot provide Azure Active Directory authentication library for implement Azure AD login in Bot .

Right click on Solution, select Manage NuGet Package for Solution > Search “ AuthBot” > select Project and install the package.

Building Bot Application with Azure AD Login Authentication using AuthBot

You can follow given below steps for integrate AD authentication 

Step 1:

 Select Web.config file and add Mode,resourceID,Endpointurl ,Tenant,clientID,clientSecret and redirect url appsettings property and replace Azure AD details as per below
<appSettings>
<add key="BotId" value="YourBotId" />
<add key="MicrosoftAppId" value="" />
<add key="MicrosoftAppPassword" value="" />
<add key="ActiveDirectory.Mode" value="v1" />
<add key="ActiveDirectory.ResourceId" value="https://graph.windows.net/" />
<add key="ActiveDirectory.EndpointUrl" value="https://login.microsoftonline.com" />
<add key="ActiveDirectory.Tenant" value="dxdemos.net" />
<add key="ActiveDirectory.ClientId" value="2d3b5788-05a5-486d-b2a4-2772a4511396" />
<add key="ActiveDirectory.ClientSecret" value="wU3oFBJ1gjWcB8Lo/fMaaCwg7ygg8Y9zBJlUq+0yBN0=" />
<add key="ActiveDirectory.RedirectUrl" value="http://localhost:3979/api/OAuthCallback" />
</appSettings>

Step 2: 

Select Global.asax.cs file and call all the bot app setting property and assign to AuthBot model class, like below
using System.Configuration;
using System.Web.Http;
namespace DevAuthBot
{
public class WebApiApplication : System.Web.HttpApplication
{
protected void Application_Start()
{
GlobalConfiguration.Configure(WebApiConfig.Register);
AuthBot.Models.AuthSettings.Mode = ConfigurationManager.AppSettings["ActiveDirectory.Mode"];
AuthBot.Models.AuthSettings.EndpointUrl = ConfigurationManager.AppSettings["ActiveDirectory.EndpointUrl"];
AuthBot.Models.AuthSettings.Tenant = ConfigurationManager.AppSettings["ActiveDirectory.Tenant"];
AuthBot.Models.AuthSettings.RedirectUrl = ConfigurationManager.AppSettings["ActiveDirectory.RedirectUrl"];
AuthBot.Models.AuthSettings.ClientId = ConfigurationManager.AppSettings["ActiveDirectory.ClientId"];
AuthBot.Models.AuthSettings.ClientSecret = ConfigurationManager.AppSettings["ActiveDirectory.ClientSecret"];
}
}
}

Step 3: 

You can create a new AzureADDialog class to show the default login and logout UI Design dialog. Rightclick on Project, select Add New Item, create a class that is marked with the [Serializable] attribute (so the dialog can be serialized to state), and implement the IDialog interface.
using AuthBot;
using AuthBot.Dialogs;
using Microsoft.Bot.Builder.Dialogs;
using Microsoft.Bot.Connector;
using System;
using System.Configuration;
using System.Threading;
using System.Threading.Tasks;
namespace DevAuthBot.Dialogs
{
[Serializable]
public class AzureADDialog : IDialog<string>
{

Step 4 :

IDialog interface has only StartAsync() method. StartAsync() is called when the dialog becomes active. The method passes the IDialogContext object, used to manage the conversation.
public async Task StartAsync(IDialogContext context)
{

context.Wait(MessageReceivedAsync);

}

Step 5: 

Create a MessageReceivedAsync method and write the following code for the login and logout default dialog and create a ResumeAfterAuth for after the user login, bot will reply the user name and email id details.
///
/// Login and Logout
///
/// context">
/// item">
///
public virtual async Task MessageReceivedAsync(IDialogContext context, IAwaitable<IMessageActivity> item)
{
var message = await item;
//endpoint v1
if (string.IsNullOrEmpty(await context.GetAccessToken(ConfigurationManager.AppSettings["ActiveDirectory.ResourceId"])))
{
//Navigate to website for Login
await context.Forward(new AzureAuthDialog(ConfigurationManager.AppSettings["ActiveDirectory.ResourceId"]), this.ResumeAfterAuth, message, CancellationToken.None);
else
{
//Logout
await context.Logout();
context.Wait(MessageReceivedAsync);
}
}
///
/// ResumeAfterAuth
///
/// context">
/// result">
///
private async Task ResumeAfterAuth(IDialogContext context, IAwaitable<string> result)
{
//AD resposnse message
var message = await result;
await context.PostAsync(message);
context.Wait(MessageReceivedAsync);
}

After the user enters the first message, our bot will reply and ask to login to the AD. Then, it waits for Authentication code and bot will reply the user details as a response like below.

Building Bot Application with Azure AD Login Authentication using AuthBot

Run Bot Application

The emulator is a desktop application that lets us test and debug our bot on localhost. Now, you can click on "Run the application" in Visual studio and execute in the browser


Building Bot Application with Azure AD Login Authentication using AuthBot
  • Test Application on Bot Emulator
  • You can follow the below steps to test your bot application.
  • Open Bot Emulator.
  • Copy the above localhost url and paste it in emulator e.g. - http://localHost:3979
  • You can append the /api/messages in the above url; e.g. - http://localHost:3979/api/messages.
  • You won't need to specify Microsoft App ID and Microsoft App Password for localhost testing, so click on "Connect".
Building Bot Application with Azure AD Login Authentication using AuthBot

Related Article:

I have explained about Bot framework Installation, deployment and implementation in the below article

Summary

In this article, you learned how to create a Bot Azure AD login authentication and Logout using AuthBot. If you have any questions/feedback/ issues, please write in the comment box.



Microsoft Cognitive services is set of cloud based intelligence APIs for building richer and smarter application development. Cognitive API will use for Search meta data from Photos and video and emotions, sentiment analysis and authenticating speakers via voice verification.

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

The Computer Vision API will help developers to identify the objects with access to advanced algorithms for processing images and returning image meta data information. In this article, you will learn about Computer Vision API and how to implement Compute Vision API into Bot application.


You can follow below steps for implement object detection in Bot Application 
 

Computer Vision API Key Creation

Computer Vision ApI returns information about visual content found in an image. You can follow below steps for create Vision API key.

Navigate to https://azure.microsoft.com/en-us/try/cognitive-services/

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

Click on “Get API Key “or Login with Azure login.

Login with Microsoft Account and Get API key

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

Copy API key and store securely, we will use this API key into our application

Step 2: Create New Bot Application

Let's create a new bot application using Visual Studio 2017. Open Visual Studio > Select File > Create New Project (Ctrl + Shift +N) > Select Bot application.

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

The Bot application template gets created with all the components and all required NuGet references installed in the solutions.

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

In this solution, we are going edit Messagecontroller and add Service class.

Install Microsoft.ProjectOxford.Vision Nuget Package

The Microsoft project oxford vision nuget package will help for access cognitive service so Install “Microsoft.ProjectOxford.Vision” Library from the solution

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

Create Vision Service: Create new helper class to the project called VisionService that wraps around the functionality from the VisionServiceClient from Cognitive Services and only returns what we currently need.

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading.Tasks;
using System.Web;
using Microsoft.ProjectOxford.Vision;
using Microsoft.ProjectOxford.Vision.Contract;
namespace BotObjectDetection.Service
{
public class VisionService : ICaptionService
{
/// <summary>
/// Microsoft Computer Vision API key.
/// </summary>
private static readonly string ApiKey = "<API Key>";
/// <summary>
/// The set of visual features we want from the Vision API.
/// </summary>

private static readonly VisualFeature[] VisualFeatures = { VisualFeature.Description };

public async Task<string> GetCaptionAsync(string url)
{
var client = new VisionServiceClient(ApiKey);
var result = await client.AnalyzeImageAsync(url, VisualFeatures);
return ProcessAnalysisResult(result);
}
public async Task<string> GetCaptionAsync(Stream stream)
{
var client = new VisionServiceClient(ApiKey);
var result = await client.AnalyzeImageAsync(stream, VisualFeatures);
return ProcessAnalysisResult(result);
}
/// <summary>
/// Processes the analysis result.
/// </summary>
/// <param name="result">The result.</param>
/// <returns>The caption if found, error message otherwise.</returns>

private static string ProcessAnalysisResult(AnalysisResult result)
{
string message = result?.Description?.Captions.FirstOrDefault()?.Text;
return string.IsNullOrEmpty(message) ?
"Couldn't find a caption for this one" :
"I think it's " + message;
}
}
}


In the above helper class, replace vision API key and call the Analyze image client method for identify image meta data

Messages Controller

MessagesController is created by default and it is the main entry point of the application. it will call our helper service class which will handle the interaction with the Microsoft APIs. You can update “Post” method like below

using System;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
using System.Web.Http;
using BotObjectDetection.Service;
using Microsoft.Bot.Builder.Dialogs;
using Microsoft.Bot.Connector;

namespace BotObjectDetection
{
[BotAuthentication]
public class MessagesController : ApiController
{
private readonly ICaptionService captionService = new VisionService();
/// <summary>
/// POST: api/Messages
/// Receive a message from a user and reply to it
/// </summary>

public async Task<HttpResponseMessage> Post([FromBody]Activity activity)
{
if (activity.Type == ActivityTypes.Message)
{
//await Conversation.SendAsync(activity, () => new Dialogs.RootDialog());
var connector = new ConnectorClient(new Uri(activity.ServiceUrl));
string message;
try
{
message = await this.GetCaptionAsync(activity, connector);
}
catch (Exception)
{
message = "I am object Detection Bot , You can Upload or share Image Url ";
}
Activity reply = activity.CreateReply(message);
await connector.Conversations.ReplyToActivityAsync(reply);
}
else
{
HandleSystemMessage(activity);
}
var response = Request.CreateResponse(HttpStatusCode.OK);
return response;
}

private Activity HandleSystemMessage(Activity message)
{
if (message.Type == ActivityTypes.DeleteUserData)
{
// Implement user deletion here
// If we handle user deletion, return a real message
}
else if (message.Type == ActivityTypes.ConversationUpdate)
{
// Handle conversation state changes, like members being added and removed
// Use Activity.MembersAdded and Activity.MembersRemoved and Activity.Action for info
// Not available in all channels
}
else if (message.Type == ActivityTypes.ContactRelationUpdate)
{
// Handle add/remove from contact lists
// Activity.From + Activity.Action represent what happened
}
else if (message.Type == ActivityTypes.Typing)
{
// Handle knowing tha the user is typing
}
else if (message.Type == ActivityTypes.Ping)
{
}
return null;
}
private async Task<string> GetCaptionAsync(Activity activity, ConnectorClient connector)
{
var imageAttachment = activity.Attachments?.FirstOrDefault(a => a.ContentType.Contains("image"));
if (imageAttachment != null)
{
using (var stream = await GetImageStream(connector, imageAttachment))
{
return await this.captionService.GetCaptionAsync(stream);
}
}
string url;
if (TryParseAnchorTag(activity.Text, out url))
{
return await this.captionService.GetCaptionAsync(url);
}
if (Uri.IsWellFormedUriString(activity.Text, UriKind.Absolute))
{
return await this.captionService.GetCaptionAsync(activity.Text);
}
// If we reach here then the activity is neither an image attachment nor an image URL.
throw new ArgumentException("The activity doesn't contain a valid image attachment or an image URL.");
}

private static async Task<Stream> GetImageStream(ConnectorClient connector, Attachment imageAttachment)
{
using (var httpClient = new HttpClient())
{
var uri = new Uri(imageAttachment.ContentUrl);
return await httpClient.GetStreamAsync(uri);
}
}

private static bool TryParseAnchorTag(string text, out string url)
{
var regex = new Regex("^<a href=\"(?<href>[^\"]*)\">[^<]*</a>$", RegexOptions.IgnoreCase);
url = regex.Matches(text).OfType<Match>().Select(m => m.Groups["href"].Value).FirstOrDefault();
return url != null;
}
}
}


Run Bot Application 

The emulator is a desktop application that lets us test and debug our bot on localhost. Now, you can click on "Run the application" in Visual studio and execute in the browser

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

Test Application on Bot Emulator


You can follow the below steps to test your bot application.

Open Bot Emulator.

Copy the above localhost url and paste it in emulator e.g. - http://localHost:3979

You can append the /api/messages in the above url; e.g. - http://localHost:3979/api/messages.

You won't need to specify Microsoft App ID and Microsoft App Password for localhost testing, so click on "Connect".

Cognitive Computer Vision API: Intelligent Image Object Detection Bot

Related Article: I have explained about Bot framework Installation, deployment and implementation in the below article

Summary


In this article, you learned how to create an Intelligent Image Object Detection Bot using Microsoft Cognitive Computer Vision API. If you have any questions/feedback/ issues, please write in the comment box.
The Bot Application runs in  an application, like Skype, web chat, Facebook, Message, etc. Users can interact with bots by sending them messages, commands, and inline requests. You control your bots using HTTPS requests to the bot API.

The better visibility, the higher will be the chance of customers making a purchase. Unlike other bots which are available on business web pages which provide visibility only to the customers visiting their site, a Facebook bot is very easy to find. It is visible to around 1.3 billion users of Facebook. A Facebook bot interacts very well with customers. It can receive and send messages, images and provide clickable buttons like Buy Now. In this article, I am going to show how we can connect Facebook Messenger's channel and integrate Bot Application to the Messenger app.

In this Article, I am going to show how we can connect Facebook messengers’ channel and integrate Bot Application to the messengers App.


Create FAQ Bot Application

You can refer my previews article to create and build a Xamarin FAQ Bot using the Azure Bot Service and deploy it into Azure. I am not using any coding for developing the Bot Application, you can follow the steps provided in the article to create and deploy FAQ Bot.


Setup Facebook Page

We can implement Bot Application to the Facebook page. You can create Facebook page or select existing page and navigate to “About page” for fining and copy the Page ID.


Login to Facebook APP

Create a new Facebook App (https://bit.ly/1D0BHpg) on the Page and generate an App ID , App Secret and Page Access Token for integrating Bot to the page messenger. You can click on “Skip and Create APP Id” from following screen.




Create New App ID



Provide display Name and Contact Email for integrating Bot application and Click on Create.


After Create button, it will navigate to the App dashboard screen. The side navigation menu having Settings > Basic and copy the APPID and APP Secret. Provide the Privacy URL, Terms of Service URL, App icon and Select Category.




I have shown the following screen to Select Setting > Advanced and Set the "Allow API Access to App Settings slider to "Yes" and click on Save Changes.

Setup Message

Select Dashboard and Click on Setup button from messenger group


Create Page Access Token

Select Setting from the messenger side navigation menu to generate token, you can select the Page and generate access token and copy the page access token.

Setup Webhooks

Click Set up Webhooks to forward messaging events from Facebook Messenger to the bot.



Provide following callback URL and Verify token to the webhooks setup page and select the message, message_postbacks, messaging_optins and message_deliveries subscription fields. The following Steps 2, will show how to generate Callback URL and verify token from Azure portal.


You can click on “Verify and Save “and select the Facebook page to Subscribe the webhook to the Facebook page.

Connect Facebook Channel

Step 1: Login to Azure portal > Select the “All Resources” > Select Channels > Select Facebook Messengers, let we start configure “Facebook Messengers “Channel and follow below steps, the end of this article you can able to deploy Bot into the Facebook messenger



Step 2:The Azure Facebook configuration channel will generate following Callback URL and verify token, You can copy those information and Past to the Facebook webhook setup screen. (Return to the Facebook messenger setup screen).






Step 3: You can paste the Facebook App ID, Facebook App Secret, Page ID,and Page Access Token values copied from Facebook Messenger previously. You can use the same bot on multiple facebook pages by adding additional page ids and access tokens.


Submit to Facebook Review

Select App preview and Click and Submit for review after submit will take some time for the facebook team testing to the messenger bot and you can mark your app live available to the public , then you can test messenger from the Facebook page .


Facebook Team reviews and Testing

You can verify Facebook team testing progress, navigate to your Facebook page and Click on Inbox and verify, if anything problem to the messenger bot, the Team will update the bug list from App review screen.

Xamarin FAQ Messengers testing

You can select your Facebook page and test your bot application. I have trained 7000+ more questions to the Facebook messenger bot from Xamarin Q & A Page, If you want to look the xamarin FAQ demo. Navigate to Xamarin Q & A Facebook page and ask your xamairn related question.





Summary

In this article, you have learned how to integrate bot application right into your Facebook Page via Azure Microsoft AI. If you have any questions/feedback/issues, please write in the comment box.


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