A day with .Net

My day to day experince in .net

Communication between Angular 4 components through RxJs and Observables

Posted by vivekcek on September 27, 2017

Hi Guys this is a quick post about how we can implement communication between Angular 4 components via RxJs and Observable. This example can be done with other component communication mechanisms like @Input() and Services.

But observable based communication will be useful in large Angular 4 applications.
I am not going to explain the concept in detail. Please google it.

Back to our example.

I have two nested components, AppComponent and HelloCoponent. AppCoponent is the parent/root.
AppComponent display a list of dynamically created items. There is a button inside HelloComponent, When clicking on it new items are added and these new items are reflected in AppComponent.

Have a look at the structure.

Have a look at the code.

AppComponent

import { Component,OnInit} from '@angular/core';
import {MessageService} from './message.service'
import {Subscription} from 'rxjs/Subscription'
@Component({
  selector: 'my-app',
  templateUrl: './app.component.html',
  styleUrls: [ './app.component.css' ]
})
export class AppComponent implements OnInit  {
  
  names:string[];

  constructor(private msgServce:MessageService){

  }

  ngOnInit():void{
    this.msgServce.msgPublisher$.subscribe(data=>{this.names=data.names;
    //this.changeDetectorRef.detectChanges();
    
    });
  }
}

AppComponent.html

<hello ></hello>
<ul>
       <li *ngFor="let name of names">
          {{name}}
       </li>
</ul>

HelloCoponent

import { Component, Input } from '@angular/core';
import {MessageService} from './message.service'
@Component({
  selector: 'hello',
  template: `<button (click)="onAdd()">Add</button>`,
  styles: [`h1 { font-family: Lato; }`]
})
export class HelloComponent  {
  constructor(private msgService:MessageService){

  }

  onAdd(){
    this.msgService.addName("Test");
  }
}

MessageService

import {Injectable} from '@angular/core'
import {Subject} from 'rxjs/Subject'

@Injectable()
export class MessageService{
  constructor(){}
  names:string[]=[];
  private messageSource=new Subject<any>();
  msgPublisher$=this.messageSource.asObservable();

  addName(name:string){
    this.names.push(name)
    this.messageSource.next({names:this.names})
  }
}
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Posted in Angular2 | Tagged: , | Leave a Comment »

Predicting linear regression with Tensorflow and Azure Machine Learning Studio (Comparison ,Gradient descent)

Posted by vivekcek on September 20, 2017

In this post I am trying to evaluate the prediction done by Tensorflow and Azure Machine Learning Studio.
I am using a dataset obtained from Courseera machine learning tutorial. I will provide the dataset at the end of this post.
Here is the predicted value I got from Tensorflow and Azure Machine Learning for the input “8.5172”

Azure

Tensorflow

I used linear regression with Gradient descent optimizer, and below are the values used for epoch and learning rate in Tensorflow and azure ML.
Epoch=1000
Learning rate=0.01

Azure Model and Algorithm settings are given below

Tensorflow code is given below.

import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf

# Parameters
display_step = 50
learning_rate = 0.01
training_epochs = 1000

data = pd.read_csv('ex1data1.txt', names=['population', 'profit'])

X_data = data[['population']]
Y_data = data[['profit']]

n_samples = X_data.shape[0]  # Number of rows

# tf Graph Input
X = tf.placeholder('float', shape=X_data.shape)
Y = tf.placeholder('float', shape=Y_data.shape)

# Set model weights
W = tf.Variable(tf.zeros([1, 1]), name='weight')
b = tf.Variable(tf.zeros(1), name='bias')

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
# cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
cost = tf.reduce_mean(tf.square(pred-Y)) / 2.0

# Gradient descent
# may try other optimizers like AdadeltaOptimizer, AdagradOptimizer, AdamOptimizer, FtrlOptimizer or RMSPropOptimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    cost_value, w_value, b_value = (0.0, 0.0, 0.0)
    for epoch in range(training_epochs):
        # Fit all training data
        _, cost_value, w_value, b_value = sess.run((optimizer, cost, W, b), feed_dict={X: X_data, Y: Y_data})

        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch+1), 'cost=', '{:.9f}'.format(cost_value), \
                'W=', w_value, 'b=', b_value)

    print ('Optimization Finished!')
    print ('Training cost=', cost_value, 'W=', w_value, 'b=', b_value, '\n')
    print('Evaluation')
    print(w_value*8.5172+b_value)
    # Graphic display
    plt.plot(X_data, Y_data, 'ro', label='Original data')
    plt.plot(X_data, w_value * X_data + b_value, label='Fitted line')
    plt.legend()
    plt.show()

Please use below dataset.

6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705

Posted in Azure, Machine Learning, Tensorflow | Tagged: , | Leave a Comment »

Linear Regression With TensorFlow – Part1

Posted by vivekcek on September 18, 2017

Hi Guys in this blog post i am trying to explain, how we can implement simple linear regression with tensorflow. Simple linear regression means regression with single input and single output.

In future posts i will explain about.

1. Linear regression with multiple features.
2. Polynomial regression.
3. Regularized/Normalized linear regression.
4. Linear regression with external data.

Today anyway i want keep the problem simple, so i am using some inline data for analysis.

I hope you have some good knowledge about Machine Learning. If not please take a training from course-era. Course-era has a good training in Machine Learning by Andrew Ng.

Do we really need tensorflow to do linear regression? We can implement it in Octave, MATLAB, Python, Scikit-learn etc…

Understanding the Math’s and statics behind linear regression is more important than the tool we are going to use.

So how we will approach this problem?

First we need to ensure the data available with us is linearly dependent. For that we need to plot it. You can use the below code to plot your data.

import matplotlib.pyplot as plt
import numpy 

train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

From the image it is clear that the data is linearly dependent and we can use simple linear regression with it.

Next we are going to define our hypothesis, Cost function and Optimizer. Hope the reader is aware of what you mean by cost, how to minimize the cost etc..

For linear regression the hypothesis we are going to use is the equation of a straight line.

hypothesis (prediction)=WX+b(Where W is the slope and b is the y intercept and X is our input).
In tensorflow we say them as Weight (W) and bias (b).

Next what is cost, cost is actually the difference from the actual to predicted. We need to minimize this cost to find a best W and b.

The cost function for linear regression is given below.

To minimize the cost we are going to use gradient descent algorithm.

The full code is given below.

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random


learning_rate = 0.01
training_epochs = 1000
display_step = 50


train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]


X = tf.placeholder("float")
Y = tf.placeholder("float")


W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")


hypothesis = tf.add(tf.multiply(X, W), b)


cost = tf.reduce_mean(tf.square(hypothesis - Y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)


init = tf.global_variables_initializer()


with tf.Session() as sess:


    sess.run(init)
    for epoch in range(training_epochs):
        sess.run(optimizer, feed_dict={X: train_X, Y: train_Y})


        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

    # Testing example, as requested (Issue #2)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        cost,
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

Posted in Machine Learning, Tensorflow | Leave a Comment »

C# Versions 8 to 6 Fetaures

Posted by vivekcek on September 14, 2017

In this post you can find the new features available in C# versions 8, 7.1, 7, 6.

Posted in c#.net | Tagged: , , , , | Leave a Comment »

Visualise Computational Graphs with Tensorboard and Tensorflow

Posted by vivekcek on August 6, 2017

In this post i will show , how you can visualise computational graphs created by tensorflow bu using tensorboard.

I am using visual studio for tensorflow development in windows. Refer my previous blog to setup tensor flow in windows Installing tensorflow in windows

Hope the reader has some basic understanding of tensorflow. Tensorflow execute every operation by building computational graphs. To visualize such graph we use tensorboard.

Tensorboard is a visualisation tool that will be installed as a part of tensorflow installation.

Write below code in visual studio. In this code we declare four constants, then we multiply ‘a’ and b’ after that we divide ‘c’ and ‘d’ then result of both were added to produce the final output.

import tensorflow as tf

a=tf.constant(6,name="a")
b=tf.constant(3,name="b")
c=tf.constant(10,name="c")
d=tf.constant(5,name="d")

mul=tf.multiply(a,b,name="mul")
div=tf.div(c,d,name="div")

addn=tf.add_n([mul,div],name="addn")

sess=tf.Session()
output =sess.run(addn)
print(output)
writer=tf.summary.FileWriter('./visual',sess.graph)
writer.close()
sess.close()

These line actually help us to create graphs for above computation. Here “visual” is a folder created in your running directory.

writer=tf.summary.FileWriter('./visual',sess.graph)
writer.close()

Now go to your running directory and execute below command.

tensorboard --logdir="visual"

Now browse to the link and see the graph.

Posted in Tensorflow | Tagged: , , , , | Leave a Comment »

UiPath Automation Video Tutorial – Data Entry In Windows Application

Posted by vivekcek on August 4, 2017

This is my first attempt to create some YouTube video tutorials. In this video i will demonstrate how you can automate a windows application via UiPath.

UiPath is one of the leading RPA provider and you don’t need much technical expertise to learn it.
Also it is easier to learn than test automation.

Posted in UiPath(Robotic Process Automation) | Tagged: , , | Leave a Comment »

Developing an Ethereum BlockChain based Web Application for Voting

Posted by vivekcek on July 2, 2017

Hi Guys are you new to BlockChain? And want to develop your first Web app with Ethereum?
Then you are in the right place. Before starting the app, Make sure you have the Ethereum development environment is ready in your system. Later I will write a blog post on setting up Ethereum.

This is not a beginner tutorial. Hope the reader deployed and tested some HelloWorld Contracts in Ethereum

So what is my development environment?
1. Windows 8
2. Visual studio code With Solidity
3. NPM
4. Truffle
5. TestRPC
6. MetaMask(Optional)

So now follow the steps.

1.Open PowerShell.
2.Create a directory with mkdir.
3.Issue the command.

Truffle init webpack

4.Now open the VS Code with below command.

Code .

5.Now we need to delete some files from Contracts folder, which is created by truffle.

6.Now create a new file under Contracts folder named “Voting.sol”.

7.Now add below code in Voting.sol and hit Save.

pragma solidity ^0.4.2;

contract Voting {
    mapping (bytes32=>uint8) public votesReceived;
    bytes32[] public candidateList;

    function Voting(bytes32[] candidateNames){
        candidateList=candidateNames;
    }

    function totalVotesFor(bytes32 candidate) returns (uint8) {
        if (validCandidate(candidate) == false) throw;
        return votesReceived[candidate];
    }

    function voteForCandidate(bytes32 candidate) 
    {
        if (validCandidate(candidate) == false) throw;
        votesReceived[candidate] += 1;
    }

    function validCandidate(bytes32 candidate) returns (bool) 
    {
        for(uint i = 0; i < candidateList.length; i++) {
        if (candidateList[i] == candidate) {
            return true;
        }
        }
        return false;
    }
}

8.Now you need to update the contents of two files in Migrations folder with below code

var Voting = artifacts.require("./Voting.sol");
module.exports = function(deployer) {
  deployer.deploy(Voting, ['Vivek', 'Jay', 'Ram']);
};

9.Open a new instance of PowerShell in new window and enter the below command.

testrpc

10.Now in the first PowerShell issue the below commands

truffle compile
truffle migrate

11.Now enter the command and test with below codes

Truffle console
Voting.deployed().then(function(contractInstance) {contractInstance.voteForCandidate('Vivek').then(function(v) {console.log(v)})})
Voting.deployed().then(function(contractInstance) {contractInstance.totalVotesFor.call('Vivek').then(function(v) {console.log(v)})})

12.Now we can setup our web application.
13.Go and edit app.js and index.html with below code.

Add this in app.js

// Import the page's CSS. Webpack will know what to do with it.
import "../stylesheets/app.css";

// Import libraries we need.
import { default as Web3} from 'web3';
import { default as contract } from 'truffle-contract'

// Import our contract artifacts and turn them into usable abstractions.
import voting_artifacts from '../../build/contracts/Voting.json'


var Voting = contract(voting_artifacts);
let candidates = {"Vivek": "candidate-1", "Jay": "candidate-2", "Ram": "candidate-3"}

window.App = {
  start: function() {
    var self = this;
    
    Voting.setProvider(web3.currentProvider);

    let candidateNames = Object.keys(candidates);
    for (var i = 0; i < candidateNames.length; i++) {
      let name = candidateNames[i];
      Voting.deployed().then(function(contractInstance) {
        contractInstance.totalVotesFor.call(name).then(function(v) {
          $("#" + candidates[name]).html(v.toString());
        });
      })
    }
   
  },

  voteForCandidate : function(candidate) {
  let candidateName = $("#candidate").val();
    try {
      $("#msg").html("Vote has been submitted. The vote count will increment as soon as the vote is recorded on the blockchain. Please wait.")
      $("#candidate").val("");

      /* Voting.deployed() returns an instance of the contract. Every call
      * in Truffle returns a promise which is why we have used then()
      * everywhere we have a transaction call
      */
      Voting.deployed().then(function(contractInstance) {
        contractInstance.voteForCandidate(candidateName, {gas: 140000, from: web3.eth.accounts[0]}).then(function() {
          let div_id = candidates[candidateName];
          return contractInstance.totalVotesFor.call(candidateName).then(function(v) {
            $("#" + div_id).html(v.toString());
            $("#msg").html("");
          });
        });
      });
    } catch (err) {
      console.log(err);
    }
    }
   
};

window.addEventListener('load', function() {
  // Checking if Web3 has been injected by the browser (Mist/MetaMask)
  if (typeof web3 !== 'undefined') {
    console.warn("Using web3 detected from external source. If you find that your accounts don't appear or you have 0 MetaCoin, ensure you've configured that source properly. If using MetaMask, see the following link. Feel free to delete this warning. 🙂 http://truffleframework.com/tutorials/truffle-and-metamask")
    // Use Mist/MetaMask's provider
    window.web3 = new Web3(web3.currentProvider);
  } else {
    console.warn("No web3 detected. Falling back to http://localhost:8545. You should remove this fallback when you deploy live, as it's inherently insecure. Consider switching to Metamask for development. More info here: http://truffleframework.com/tutorials/truffle-and-metamask");
    // fallback - use your fallback strategy (local node / hosted node + in-dapp id mgmt / fail)
    window.web3 = new Web3(new Web3.providers.HttpProvider("http://localhost:8545"));
  }

  App.start();
});

Add this in index.html

<!DOCTYPE html>
<html>
<head>
  <title>MetaCoin - Truffle Webpack Demo w/ Frontend</title>
  <link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
  <link href='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css' rel='stylesheet' type='text/css'>
  <script src="https://code.jquery.com/jquery-3.1.1.slim.min.js"></script>
  <script src="./app.js"></script>
</head>
<body class="container">
  <h1>A Simple Hello World Voting Application</h1>
  <div id="address"></div>
  <div class="table-responsive">
    <table class="table table-bordered">
      <thead>
        <tr>
          <th>Candidate</th>
          <th>Votes</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>Vivek</td>
          <td id="candidate-1"></td>
        </tr>
        <tr>
          <td>Jay</td>
          <td id="candidate-2"></td>
        </tr>
        <tr>
          <td>Ram</td>
          <td id="candidate-3"></td>
        </tr>
      </tbody>
    </table>
    <div id="msg"></div>
  </div>
  <input type="text" id="candidate" />
  <a href="#" onclick="App.voteForCandidate()" class="btn btn-primary">Vote</a>
</body>
</html>

14.Now issue the command

npm run dev

15.Now open Chrome in incognito.
16.And hit the url http://localhost:8081/
17.Happy coding with blockchain

Posted in BlockChain | Tagged: , , , , , , , | Leave a Comment »

Insert values to a table with MERGE and keeping the order of insertion SQL Server

Posted by vivekcek on June 28, 2017

In some cases you may want to insert some values into the production database during a new release.
What we normally do is sharing some data insert script to the DBA.
What will happen when this DBA people run your script multiple times? Duplicates!!! oops!! application down!!

So to avoid this we need to add condition for duplicate checking in our query.
This can be achieved by MERGE statement.

Try this example.

1. Create a table

CREATE TABLE UserInfo(Id INT,Name VARCHAR(50), Email VARCHAR(100));

2. Insert one record.

INSERT INTO UserInfo VALUES(1,'Vivek','vivek@vivek.com')

3. Now prepare the data need to insert. Here we are using the SELECT and UNION ALL.
UNION ALL will preserve the order of insertion (Try with UNION only then you can see the difference)

SELECT 1 AS Id,'Vivek' AS Name, 'vivek@vivek.com' AS Email
UNION ALL
SELECT 2 AS Id,'Ramu' AS Name, 'ramu@ramu.com' AS Email
UNION ALL
SELECT 3 AS Id,'Jay' AS Name, 'Jay@ramu.com' AS Email

4. Now write the MERGE statement as below and execute. You can see only 2 records got inserted and they are in order.

MERGE INTO UserInfo AS UserInfoTarget
USING(
SELECT 1 AS Id,'Vivek' AS Name, 'vivek@vivek.com' AS Email
UNION ALL
SELECT 2 AS Id,'Ramu' AS Name, 'ramu@ramu.com' AS Email
UNION ALL
SELECT 3 AS Id,'Jay' AS Name, 'Jay@ramu.com' AS Email
)AS Source
ON (UserInfoTarget.Id=Source.Id AND UserInfoTarget.Name=Source.Name AND UserInfoTarget.Email=Source.Email)
WHEN NOT MATCHED THEN
INSERT VALUES(Source.Id,Source.Name,Source.Email);

Full code.

Posted in SQL | Tagged: , , , | Leave a Comment »

Installing Tensorflow in Windows and Visual Studio – Deep learning.

Posted by vivekcek on March 25, 2017

Recently i was working with designing a chat-bot with Microsoft bot framework.
And i was using a combination of Retrieval based model and Intent/Entity based model.

During the research i decided to include some deep learning into the bot with a Generative Model.
Because generative Models will be the future of chat bot’s.

So i decided to play with Google’s tensorflow for deep learning implementation.
As i am a .NET guy, i like to do all my experiments with Visual Studio.
Then i googled and found that we can integrate tensorflow with Visual Studio.

So how can we do that? First we need to have below software requirements.

1. A 64 bit Windows.
2. Visual studio 2015.

Now download 64 bit Python 3.5, You can have it from below link.
https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe

Install python, Please ensure that you selected the “Add Python 3.5 to PATH”

Now open the Command Prompt in Administrator Mode.
Then check the Python Version, by using below commands and ensure that you have 3.5 , 64 bit version.

python --version

Now we need to update pip, Issue the below command.

python -m pip install --upgrade pip

Now install the CPU mode tensorflow by using below command.

pip install tensorflow

Now try this code in your python prompt.

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

Now to do python in visual studio, we need to install Python Tools For Visual Studio.
Have it from this link https://github.com/Microsoft/PTVS

After successful installation create a python application in Visual studio.

Add below code in your file and run.

import tensorflow as tf
hello = tf.constant('Hello, Tensorflow on Windows!')
sess = tf.Session()
print(sess.run(hello))

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Dependency Injection in Microsoft Bot Framework

Posted by vivekcek on January 15, 2017

In this post i will show you, how you can implement dependency injection in Microsoft Bot application.
Basically Microsoft bot application is a Web API service. So you can implement dependency injection, the same way you done it in Web API.

Here i am using Ninject to implement dependency injection.

1. Add below Nuget Packages

“Ninject.Web.WebApi”

1

“Ninject.Web.Common.WebHost”

2

2. Go to App_Start folder and open NinjectWebCommon.cs

3. Update the CreateKernel() method by adding below line.

RegisterServices(kernel);
GlobalConfiguration.Configuration.DependencyResolver = new NinjectDependencyResolver(kernel);
return kernel;

4. You may need to add below namespaces in NinjectWebCommon.cs

using System.Web.Http;
using Ninject.Web.WebApi;

5. Register your services in NinjectWebCommon.cs

private static void RegisterServices(IKernel kernel)
{
  kernel.Bind<IMyInterface>().To<MyClass>();
}    

6. Use this class inside your MessageController.

[BotAuthentication]
public class MessagesController : ApiController
{
        
private readonly IMyInterface instance;
public MessagesController(IMyInterface _instance)
{
  instance = _instance;
}

Posted in Bot Framework | Tagged: , , | Leave a Comment »