The Bot Application runs inside an application in different channel like Cortana, Skype, web chat, Facebook, Message, etc. Users can interact with bots by sending them messages, commands, and inline requests. Microsoft has announced Line App new channel added into the Azure portal.

LINE is a new communication app which allows you to make FREE voice calls and send FREE messages whenever and wherever you are. Line is available in different device iPhone, Android, Windows Phone, BlackBerry, and Nokia and even your PC and LINE is a popular messaging app with hundreds of millions of users in India, Japan, Taiwan, Thailand, Indonesia, and other countries. You can follow the below steps for Create FAQ Bot and enable your bot in the Line new channel.



Create FAQ Bot Application

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

Microsoft Bot Application deploy to Line App using Azure Bot


Line Developer Portal 

We can implement a Bot Application to the Line apps. You can create a Line developer account or select an existing provider name. Create a new Line Developer App on the Page and generate an Channel Secret and Channel access token for integrating the Bot to the Line ap. You can start login or create account in the following screen.
Microsoft Bot Application deploy to Line App using Azure Bot

Create Provider

After login, start creating a provider name for your bot or if you already have the provider, select the provider name and do the setup. The provider name is individual owner or company, it’s not app name. Next Click on confirm, if you want change in the future, edit options always enable. Microsoft Bot Application deploy to Line App using Azure Bot

Line Messaging API channel 

Create a new Line App on the developer portal and generate a Channel Secret and Channel access token integrating the Bot to the page messenger. You can click on “Message API Create Channel” from the following screen.
Microsoft Bot Application deploy to Line App using Azure Bot


Create New Channel 

The following creates new channel screen provide valid your app icon.
Microsoft Bot Application deploy to Line App using Azure Bot
You can Fill out the required fields and confirm your channel settings. You can find the two developer options is available.

Developer trial - A trial plan which lets you create a bot that can send push messages and have up to 50 friends.

Free - A plan which lets you create a bot with an unlimited number of friends.The Push message cannot be sent with this plan.

Microsoft Bot Application deploy to Line App using Azure Bot

Once you've confirmed your channel settings, you'll be navigating to a following screen, which list all the apps.

Microsoft Bot Application deploy to Line App using Azure BotClick on the channel you created to access your channel settings, and scroll down to find the Basic information > Channel secret.

You can Save following somewhere for a moment for update in to Azure portal.

Channel Secret – Copy the secret code, if not available, click on Issue

Channel access Token - Scroll further to Messaging settings. There, you will see a Channel access token field, with a click on issue button forget your access token. 

Webhook URL - Webhook URL copy from Azure Portal and Update in the Line app developer portal

Microsoft Bot Application deploy to Line App using Azure Bot

Connect New LINE Channel 

Login to Azure portal > Select the “All Resources” > Select your Bot Application > Select Channels property > Find and Select New Line icon. Let us start to configure the “Line “Channel and follow the below steps, at the end of this article you will be able to deploy the Bot into the Line apps.

Microsoft Bot Application deploy to Line App using Azure Bot


The Azure Line configuration channel will generate the following Webhook URL and past/update the channel secret and access token and click Save button. You can copy following custom webhook URL and Return to the Line Developer portal and update the webhook URL

Microsoft Bot Application deploy to Line App using Azure Bot

Test Your Bot 

Once you have completed all the above steps, your bot will be successfully configured to communicate with users on LINE and is ready to test.

LINE developer console - Navigate to the settings page and you will see, a QR code of your bot.

Mobile LINE app - go to the right most navigation tab with three dots [...] and tap on the QR code icon.

Microsoft Bot Application deploy to Line App using Azure Bot

Live Demo 

Xamarin Developer Interview questions and answers Bot is ready to use in Line app. Xamarin FAQ Bot will be ready with 7000+ more Xamarin QnA’s. Now you can start open your Line App and > click on three dot line > Scan the following QR Code to add Xamarin QA bot as a friend. 

Microsoft Bot Application deploy to Line App using Azure Bot

Summary 

In this article, you have learned how to integrate a bot application into Line App via Azure Microsoft AI. In case your bot is not responding to any of your messages at all, navigate to your bot in Azure portal, and choose Test in Web Chat. If you have any questions/ feedback/ issues, please write in the comment box.

Introduction :

Bot Framework enables you to build bots that support different types of interactions with users. You can design conversations in your bot to be free. Your bot can also have more guided interactions where it provides the users with choices or actions. The conversation can use simple text strings or more complex rich cards that contain text, images, and action buttons. And, you can add natural language interactions, which let your users interact with your bots in a natural and expressive way.

We are going to create and build Xamarin FAQ Bot using Azure Bot Service and deploy into Webchat .in this article, we are not going to write any coding for implement Xamarin FAQ Bot but be ready with what are the question and answer to train the Bot. I have already created 7000+ more Xamarin Q A as word document, we will use same document for upload and train knowledge base.


Create a QnA Service:

Step 1: 

Navigate to https://qnamaker.ai/ and Sign in using Microsoft Account.

Step 2: 

Click on “Create a knowledge base” from main menu

Step 3:

You can skip “Create a QnA service” step, let we publish QnA service after create knowledge base.


Step 4: 

Provide QnA Knowledge base basic information




Step 5:

 You can extract question and answer pairs from an online FAQ, manuals Entry and upload files with .tsv,.pdf,.doc,.docx,.xls format. If you are trying to enter manual entry skip this step.
Add chit-chat to your bot, to make your bot more conversational and engaging, with low effort. You can easily add chit-chat data sets for 3 pre-defined personalities when creating your KB, and change them at any timer. Chit-chat is supported for 3 predefined personalities

  • The Professional
  • The Friend
  • The Comic

Step 5:

 Click on “Create your KB”


Step 6: 

Wait for a few seconds for load all the knowledge base Q & A to the Online Editor.

Step 7: 

QnA service has loaded our FAQs Editor with two-column knowledge base, without any extra tweaking needed from you. Now you can edit and modify old Q&A and also Select Add new QnA pair to input other greetings and responses.




Step 8: 

The main menu having different option, Edit, Publish, Save, train, test and Settings. While click on Edit the above knowledge base edit screen will open, you can search and filter the question and Edit. After Edit always click on “Save and train” menu option for Save.


Step 9: 

Click on “Publish”. once you publish the knowledge base endpoint available for use in your bot or App

Step 10: 


The Knowledge base will generate following, you can make note. Need to update below details in Azure Hosting.
  • Knowledge base Key
  • Host Address
  • EndPointKey

Create and Publish QnA Bot in Azure:

Step 1: 

Navigate and Login to https://portal.azure.com/.

Step 2: 

Select + Create a resource > Select “AI Machine Learning” > Click on “Web App Bot”





Step 3: 

Let us start create Web App Bot, Provide the Bot Name, resource, location and also follow the step 4, Step 5 for select Bot template and prizing and click on Create.

Step 4: 

You can use the v3 templates, select SDK version of SDK v3 and SDK language of C# or Node.js. Select the Question and Answer template for the Bot template field, then save the template settings by selecting Select.



Step 5: 

You can choose a pricing tier for Bot Search service

 

Step 6: 

Review your settings, then select Create. This creates and deploys the bot service with XamarinQA to Azure .


Step 7: 

Open Xamarin BotQA App Service from All Resource > Application Settings and edit the QnAKnowledgebaseId, QnAAuthKey, and the QnAEndpointHostName fields to contain the values of your QnA Maker knowledge base. Like below


Test and Implement Web Chat App:

In the Azure portal, click on Test in Web Chat to test the bot and Click on Channel > Deploy bot Application to Web Chat and Implement into your Website or Application. 



Summary

In this article, you learned Create, train, and publish your QnA Maker knowledge base. I have created 7000+ Xamarin Q A knowledge base and deployed into my blog (www.devenvexe.com) and Xamarin Q A Facebook Page, you can try out for demo and If you have any questions/feedback/ issues, please write in the comment box.



Introduction:

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.



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/

Click on “Get API Key “or Login with Azure login.
Login with Microsoft Account and Get API key 


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.



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



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


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


  • 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".

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.

Introduction :

Bot Framework enables you to build bots that support different types of interactions with users. You can design conversations in your bot to be free. Your bot can also have more guided interactions where it provides the users with choices or actions. The conversation can use simple text strings or more complex rich cards that contain text, images, and action buttons. And, you can add natural language interactions, which let your users interact with your bots in a natural and expressive way.

The Bot Builder SDK for .NET is an easy-to-use framework for developing bots using Visual Studio in Windows but for Visual Studio for Mac, it is not available in the official release. I have modified the Bot Framework template to work on Visual Studio for Mac and started using all the Bot Framework features on my Mac machine.

In this article, I am showing how to create, build, and test a Bot application using a Mac machine.


Prerequisites

  • Download and install Visual Studio for Mac
  • Clone and download the Bot Framework Project Template for Mac.
  • Download and install the Bot Framework Emulator for Mac.
  • Configure and Register Project Template

Step 1

Clone and download the Bot Framework template for Mac from the following URL - https://goo.gl/9ivoov

Step 2

Open the *.Csproj file or Visual Studio solution.

Step 3

Select and right-click on “Project” from Visual Studio Mac > “Restore NuGet packages”.

Step 4

Right-click on Project, select the Project Options, select XSP Web server and expand the Run option. Update the port number to 3978 like in the below screen.



Step 5

Build the solution. If it has successfully completed the Build, the project template gets added into the "Custom Folders" from Visual Studio Preference.


Create a Bot Application

Let's start with creating a new bot application in Visual Studio for Mac. Open Visual Studio 2017, create a new project with C#. Select the Bot applications template as below.



Provide the project Name, solution name, and location as below.



The bot application gets created with all the components and all required NuGet references installed.


Update the code

The default application adds a simple code snippet and we have no need to change anything. If you want to test your custom message, you can change it like below.

You can find the messagereceiveAsync method from Dialogs/RootDialog.cs file. In this method, the activity.Text container will return the user text input so that you can reply to a message based on the input text.

private async Task MessageReceivedAsync(IDialogContext context, IAwaitable < object > result) {
var activity = await result as Activity;
// calculate something for us to return
int length = (activity.Text ? ? string.Empty).Length;
// return our reply to the user
//test
if (activity.Text.Contains("technology")) {
await context.PostAsync("Refer C# corner website for tecnology http://www.c-sharpcorner.com/");
} else if (activity.Text.Contains("morning")) {
await context.PostAsync("Hello !! Good Morning , Have a nice Day");
}
//test
else if (activity.Text.Contains("night")) {
await context.PostAsync(" Good night and Sweetest Dreams with Bot Application ");
} else if (activity.Text.Contains("date")) {
await context.PostAsync(DateTime.Now.ToString());
} else {
await context.PostAsync($ "You sent {activity.Text} which was {length} characters");
}
Wait(MessageReceivedAsync);
}

Run Bot Application

Emulator is a desktop application that lets you test and debug your bot on localhost or remotely. Now, you can click on "Run the application" in any browser.


Install Bot Emulator

If you have not installed the Bot Emulator on Mac, you need to download and install the emulator for testing the bot application. You can download the Bot Emulator from - https://goo.gl/kZkoJT


Follow the below steps to test your bot application on Mac.

  • Open Bot Emulator.
  • Click "New Bot Configuration".
  • Copy the above localhost URL and paste it into the emulator. For example - http://127.0.0.1:3978
  • You can append the /api/message in the above URL; e.g. - http://127.0.0.1:3978/api/messages.
  • You won't need to specify the MSA ID and MSA password for localhost testing. So, click on "Save and Connect".


You can send a message to the bot application. The bot will reply as per your guide/code.


Summary

In this article, you learned how to create a bot application using Visual Studio for Mac. If you have any question, feedback, or issues, please write in the comment box.

Introduction:

Microsoft Cognitive Services awesome APIs and services for developers to create more intelligent applications. You can add more interesting feature like people emotion and video detection, facial, speech and vision recognition and speech and language understanding into our all the application. The following sample image showing for emotion and face detection using cognitive service.



In this article, you will get understand how to Create a Cognitive Services APIs account in the Azure Portal.

Prerequisites:

Create a free trial Azure subscription from Azure portal.
If you are looking paid version, Click here for detail



Create a Cognitive Services Account in Azure:
You can follow below steps for Create a Cognitive Services APIs account in the Azure Portal.

Step 1: 

Sign in to the Azure portal.

Step 2: 

Click + NEW and Select AI + Cognitive Services


Step 3:

You can see the entire list of Cognitive Services APIs. Click on the API of your choice to proceed.


Step 4: 

Select on required API and read about the API and Click on Create


Step 5: 

after click on create button, provide the following information for create cognitive service and click on create.



Name: Name of the account, Microsoft recommend a descriptive name. for example, <common name><APIName>Account.

Subscription: Select the available Azure subscriptions.

Location: Select the service locations.

Pricing tier: you can choose your pricing tier. F0 is free service and S0 paid service. based on your usage you can choose the pricing tier



Select the Resource group > confirm the Microsoft notice and Click on create for create the account

Step 6: 

wait for few second and you will get notification after complete. If Cognitive Services account is successfully deployed, click the notification to view the account information.

You can see and copy the Endpoint URL in the Overview section.



You can also copy keys in the Keys section to start making API calls in our Xamarin or other applications.


Summary:

In this article, you learned about how to Create a Cognitive Services APIs account in the Azure Portal.

We can use these keys and end point URL to the next article with app to communicate intelligent feature in Xamarin application.

If you have any questions/ feedback/ issues, please write in the comment box.

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