Implementing Feedback Feature in SUSI Android App

Recently, on SUSI Server, a new servlet was added which is used to rate SUSI Skills either positive or negative. The server stores the rating of a particular skill in a JSON file. These ratings help in improving answers provided by SUSI. So, the server part is done and it was required to implement this in the SUSI Android App. In this blog, I will cover the topic of implementation of the Rating or Feedback feature in SUSI Android App. This will including all the cases when feedback should be sent, when it should not be sent, when to send positive feedback, when to send negative feedback, etc.

API Information

For rating a SUSI Skill, we have to call on  /cms/rateSkill.json providing 5 parameters which are:

  1. model: The model of SUSI Skill. (String)
  2. group: The Group under the model in which that particular skill resides. (String)
  3. language: The language of skill. (String)
  4. skill: This is skill name. (String)
  5. rating: This can be two strings, either “positive” or “negative”. String)

Basically, in the SUSI Skill Data repo (in which all the skills are stored), models, groups language etc are part of folder structure.

So, if a skill is located here

https://github.com/fossasia/susi_skill_data/blob/master/models/general/Knowledge/en/news.txt

This would mean

model = general

group = Knowledge

language = en

skill = news

rating = positive/negative

Implementation in SUSI Android App

    

So, when the like button on a particular skill is clicked, a positive call is made and when the dislike button is clicked, a negative call is made.

Let’s see example when the thumbs up button or like button is clicked.

There can be three cases possible:

  1. None of Like button or dislike button is clicked already: In this case, initially, both like and dislike button will be transparent/hollow. So, when like button is clicked, the like button will be colored blue and a call will be made with positive feedback.
  2. Like button is already clicked: In this case, like button is already clicked. So, it will already be blue. So, when user clicks again on positive button, it should get back to normal/hollow indicating rating which was sent is cancelled and a a call will be made with negative feedback thus cancelling or neutralizing the earlier, positive feedback.
  3. Dislike button is already clicked: In this case, the dislike button is already blue, indicating a negative call is already made. So, now when the like button is clicked, we need to cancel the earlier negative feedback call and sending another negative feedback call. Thus, sending two negative feedback calls. And after that coloring dislike button as blue.

Look at the code below. It is self explanatory. There are three if-else conditions covering all the above mentioned three cases.

thumbsUp.setOnClickListener(new View.OnClickListener() {
   @Override
   public void onClick(View view) {
       thumbsUp.setImageResource(R.drawable.thumbs_up_solid);
       if(!model.isPositiveRated() && !model.isNegativeRated()) {
           rateSusiSkill(Constant.POSITIVE, model.getSkillLocation(), context);
           setRating(true, true);
       } else if(!model.isPositiveRated() && model.isNegativeRated()) {
           setRating(false, false);
           thumbsDown.setImageResource(R.drawable.thumbs_down_outline);
           rateSusiSkill(Constant.POSITIVE, model.getSkillLocation(), context);
           sleep(500);
           rateSusiSkill(Constant.POSITIVE, model.getSkillLocation(), context);
           setRating(true, true);
       } else if (model.isPositiveRated() && !model.isNegativeRated()) {
           rateSusiSkill(Constant.NEGATIVE, model.getSkillLocation(), context);
           setRating(false, true);
           thumbsUp.setImageResource(R.drawable.thumbs_up_outline);
       }
   }
});

Similarly for when dislike button is clicked, the above three mentioned cases still hold resulting in this code snippet.

thumbsDown.setOnClickListener(new View.OnClickListener() {
   @Override
   public void onClick(View view) {
       thumbsDown.setImageResource(R.drawable.thumbs_down_solid);
       if(!model.isPositiveRated() && !model.isNegativeRated()) {
           rateSusiSkill(Constant.NEGATIVE, model.getSkillLocation(), context);
           setRating(true, false);
       } else if(model.isPositiveRated() && !model.isNegativeRated()) {
           setRating(false, true);
           thumbsUp.setImageResource(R.drawable.thumbs_up_outline);
           rateSusiSkill(Constant.NEGATIVE, model.getSkillLocation(), context);
           sleep(500);
           rateSusiSkill(Constant.NEGATIVE, model.getSkillLocation(), context);
           setRating(true, false);
       } else if (!model.isPositiveRated() && model.isNegativeRated()) {
           rateSusiSkill(Constant.POSITIVE, model.getSkillLocation(), context);
           setRating(false, false);
           thumbsDown.setImageResource(R.drawable.thumbs_down_outline);
       }
   }
});

Summary

So, this blog talked about how the Feedback feature in SUSI Android App is implemented. This included how a network call is made, logic for sending positive/negative feedback, logic to withdraw feedback etc. So, If you are looking forward to contribute to SUSI Android App, this can help you a little. But if not so, this may also help you in understanding and how rating mechanism in social media websites like Facebook, Twitter, Quora, Reddit, etc work.

References

  1. To know about servlets https://en.wikipedia.org/wiki/Java_servlet
  2. To see how to implement one https://www.javatpoint.com/servlet-tutorial
  3. To see how to make network calls in android using Retrofit https://guides.codepath.com/android/Consuming-APIs-with-Retrofit
  4. To see how to implement click listeners on button https://developer.android.com/reference/android/view/View.OnClickListener.html

Implementing the Feedback Functionality in SUSI Web Chat

SUSI AI now has a feedback feature where it collects user’s feedback for every response to learn and improve itself. The first step towards guided learning is building a dataset through a feedback mechanism which can be used to learn from and improvise the skill selection mechanism responsible for answering the user queries.

The flow behind the feedback mechanism is :

  1. For every SUSI response show thumbs up and thumbs down buttons.
  2. For the older messages, the feedback thumbs are disabled and only display the feedback already given. The user cannot change the feedback already given.
  3. For the latest SUSI response the user can change his feedback by clicking on thumbs up if he likes the response, else on thumbs down, until he gives a new query.
  4. When the new query is given by the user, the feedback recorded for the previous response is sent to the server.

Let’s visit SUSI Web Chat and try this out.

We can find the feedback thumbs for the response messages. The user cannot change the feedback he has already given for previous messages. For the latest message the user can toggle feedback until he sends the next query.

How is this implemented?

We first design the UI for feedback thumbs using Material UI SVG Icons. We need a separate component for the feedback UI because we need to store the state of feedback as positive or negative because we are allowing the user to change his feedback for the latest response until a new query is sent. And whenever the user clicks on a thumb, we update the state of the component as positive or negative accordingly.

import ThumbUp from 'material-ui/svg-icons/action/thumb-up';
import ThumbDown from 'material-ui/svg-icons/action/thumb-down';

feedbackButtons = (
  <span className='feedback' style={feedbackStyle}>
    <ThumbUp
      onClick={this.rateSkill.bind(this,'positive')}
      style={feedbackIndicator}
      color={positiveFeedbackColor}/>
    <ThumbDown
      onClick={this.rateSkill.bind(this,'negative')}
      style={feedbackIndicator}
      color={negativeFeedbackColor}/>
  </span>
);

The next step is to store the response in Message Store using saveFeedback Action. This will help us later to send the feedback to the server by querying it from the Message Store. The Action calls the Dispatcher with FEEDBACK_RECEIVED ActionType which is collected in the MessageStore and the feedback is updated in the Message Store.

let feedback = this.state.skill;

if(!(Object.keys(feedback).length === 0 &&    
feedback.constructor === Object)){
  feedback.rating = rating;
  this.props.message.feedback.rating = rating;
  Actions.saveFeedback(feedback);
}

case ActionTypes.FEEDBACK_RECEIVED: {
  _feedback = action.feedback;
  MessageStore.emitChange();
  break;
}

The final step is to send the feedback to the server. The server endpoint to store feedback for a skill requires other parameters apart from feedback to identify the skill. The server response contains an attribute `skills` which gives the path of the skill used to answer that query. From that path we need to parse :

  • Model : Highest level of abstraction for categorising skills
  • Group : Different groups under a model
  • Language : Language of the skill
  • Skill : Name of the skill

For example, for the query `what is the capital of germany` , the skills object is

"skills": ["/susi_skill_data/models/general/smalltalk/en/English-Standalone-aiml2susi.txt"]

So, for this skill,

    • Model : general
    • Group : smalltalk
    • Language : en
    • Skill : English-Standalone-aiml2susi

The server endpoint to store feedback for a particular skill is :

BASE_URL+'/cms/rateSkill.json?model=MODEL&group=GROUP&language=LANGUAGE&skill=SKILL&rating=RATING'

Where Model, Group, Language and Skill are parsed from the skill attribute of server response as discussed above and the Rating is either positive or negative and is collected from the user when he clicks on feedback thumbs.

When a new query is sent, the sendFeedback Action is triggered with the required attributes to make the server call to store feedback on server. The client then makes an Ajax call to the rateSkill endpoint to send the feedback to the server.

let url = BASE_URL+'/cms/rateSkill.json?'+
          'model='+feedback.model+
          '&group='+feedback.group+
          '&language='+feedback.language+
          '&skill='+feedback.skill+
          '&rating='+feedback.rating;

$.ajax({
  url: url,
  dataType: 'jsonp',
  crossDomain: true,
  timeout: 3000,
  async: false,
  success: function (response) {
    console.log(response);
  },
  error: function(errorThrown){
    console.log(errorThrown);
  }
});

This is how the feedback feedback mechanism works in SUSI Web Chat. The entire code can be found at SUSI Web Chat Repository.

Resources

 

How to Implement Feedback System in SUSI iOS

The SUSI iOS app provides responses for various queries but the response is always not accurate. To improve the response, we make use of the feedback system, which is the first step towards implementing Machine Learning on the SUSI Server. The way this works is that for every query, we present the user with an option to upvote or downvote the response and based on that a positive or negative feedback is saved on the server. In this blog, I will explain how this feedback system was implemented in the SUSI iOS app.

Steps to implement:

We start by adding the UI which is two buttons, one with a thumbs up and the other with a thumbs down image.

textBubbleView.addSubview(thumbUpIcon)
textBubbleView.addSubview(thumbDownIcon)
textBubbleView.addConstraintsWithFormat(format: "H:[v0]-4-[v1(14)]-2-[v2(14)]-8-|", views: timeLabel, thumbUpIcon, thumbDownIcon)
textBubbleView.addConstraintsWithFormat(format: "V:[v0(14)]-2-|", views: thumbUpIcon)
textBubbleView.addConstraintsWithFormat(format: "V:[v0(14)]-2-|", views: thumbDownIcon)
thumbUpIcon.isUserInteractionEnabled = true
thumbDownIcon.isUserInteractionEnabled = true

Here, we add the subviews and assign constraints so that these buttons align to the bottom right next to each other. Also, we enable the user interaction for these buttons.

We know that the user can rate the response by pressing either of the buttons added above. To do that we make an API call to the endpoint below:

BASE_URL+'/cms/rateSkill.json?'+'model='+model+'&group='+group+'&skill='+skill+’&language’+language+’&rating=’+rating

Here, the BASE_URL is the url of the server, the other three params model, group, language and skill are retrieved by parsing the skill location parameter we get with the response. The rating is positive or negative based on which button was pressed by the user. The skill param in the response looks like this:

skills:
[
"/susi_skill_data/models/general/entertainment/en/quotes.txt"
]

Let’s write the method that makes the API call and responds to the UI that it was successful.

if let accepted = response[ControllerConstants.accepted] as? Bool {
  if accepted {
    completion(true, nil)
    return
  }
  completion(false, ResponseMessages.ServerError)
  return
}

Here after receiving a response from the server, we check if the `accepted` variable is true or not. Based on that, we pass `true` or `false` to the completion handler. Below the response we actually receive by making the request.

{
session: {
identity: {
type: "host",
name: "23.105.140.146",
anonymous: true
}
},
accepted: true,
message: "Skill ratings updated"
}

Finally, let’s update the UI after the request has been successful.

if sender == thumbUpIcon {
thumbDownIcon.tintColor = UIColor(white: 0.1, alpha: 0.7)
thumbUpIcon.isUserInteractionEnabled = false
thumbDownIcon.isUserInteractionEnabled = true
feedback = "positive"
} else {
thumbUpIcon.tintColor = UIColor(white: 0.1, alpha: 0.7)
thumbDownIcon.isUserInteractionEnabled = false
thumbUpIcon.isUserInteractionEnabled = true
feedback = "negative"
}
sender.tintColor = UIColor.hexStringToUIColor(hex: "#2196F3")

Here, we check the sender (the thumbs up or down button) and based on that pass the rating (positive or negative) and update the color of the button.

Below is the app in action with the feedback system.

Resources:

Getting Response Feedback In SUSI.AI Web Chat

The SUSI.AI Web Chat provides responses for various queries, but the quality of responses it not always the best possible. Machine learning and deep learning algorithms will help us to solve this step by step. In order to implement machine learning, we need feedback mechanisms. The first step in this direction is to provide users with a way to give feedback to responses with a “thumbs up” or “thumbs down”. In this blog, I explain how we fetch the feedback of responses from the server.

On asking a query like tell me a quote, Susi responses with following message:

Now the user can rate the response by pressing thumbs up or thumbs down button. We store this response on the server. For getting this count of feedback we use the following endpoint:

BASE_URL+'/cms/getSkillRating.json?'+'model='+model+'&group='+group+'&skill='+skill;

Here:

  • BASE_URL: Base URL of our server: http://api.susi.ai/
  • model: Model of the skill from which response is fetched. For example “general”.
  • group: The group of the skill from which response is fetched. For example “entertainment”.
  • skill: name of the skill from which response is fetched. For example “quotes”.

We make an ajax call to the server to fetch the data:

$.ajax({
          url: getFeedbackEndPoint,
          dataType: 'jsonp',
          crossDomain: true,
          timeout: 3000,
          async: false,
          success: function (data) {
            console.log(getFeedbackEndPoint)
            console.log(data);
            if(data.accepted) {
              let positiveCount = data.skill_rating.positive;
              let negativeCount = data.skill_rating.negative;
              receivedMessage.positiveFeedback = positiveCount;
              receivedMessage.negativeFeedback = negativeCount;
            }

}

In the success function, we receive the data, which is in jsonp format. We parse this to get the desired result and store it in variable positiveCount and negativeCount. An example of data response is :

In the client, we can get value corresponding to positive and negative key as follows :

let positiveCount = data.skill_rating.positive;
let negativeCount = data.skill_rating.negative;

This way we can fetch the positive and negative counts corresponding to a particular response. This data can be used in many ways, for example:

  • It can be used to display the number of positive and negative count next to the thumbs:

  • It can be used in machine learning algorithms to improve the response that SUSI.AI provides.

Resources:

Testing Link:

http://chat.susi.ai/