Artificial Intelligence

SUSI Server uses DAO in which accounting object is stored as JSONTray. SUSI clients are using this accounting object for user settings data. In this blogpost we will focus on how to use accounting JSONTray to write the user settings, so that a client can use such endpoint to store the user related settings in Susi server. The Susi server provides the required API endpoints to its web and mobile clients. Before starting with the implementation of servlet let’s take a look at Accounting.java file, to check how Susi server stores the accounting data.

public class Accounting {

        private JsonTray parent;
        private JSONObject json;
        private UserRequests requests;
        private ClientIdentity identity;
    ...
}

The JsonTray is class to hold the volume as <String,JsonObject> pairs as a Json file. The UserRequests  class holds all the user activities. The ClientIdentity class extend the base class Client and provides an Identification String for authentication of users. Now that we have understood about accounting in SUSI server let’s proceed for making an API endpoint to Store Webclient User settings. To make an endpoint we will use the HttpServlet class which provides methods, such as doGet and doPost, for handling HTTP-specific services. We will inherit our ChangeUserSettings class from AbstractAPIHandler yand implement APIhandler interface. In Susi Server the AbsrtactAPI handler extends a HTTPServlet which implements doGet and doPost ,all servlet in SUSI Server extends this class to increase code reusability.  

Since a User has to store its setting, set the minimum base role to access this endpoint to User. Apart from ‘User’ there are Admin and Anonymous roles too.

   @Override
    public BaseUserRole getMinimalBaseUserRole() {
        return BaseUserRole.USER;
    }

Next set the path for using this endpoint, by overriding getAPIPath method().

 @Override
    public String getAPIPath() {
        return "/aaa/changeUserSettings.json";
    }

We won’t be dealing with getdefault permissions so null can be return.

  @Override
    public JSONObject getDefaultPermissions(BaseUserRole baseUserRole) {
        return null;
    }

Next we implement serviceImpl method which takes four parameters the query, response, authorization and default permissions.

@Override
    public ServiceResponse serviceImpl(Query query, HttpServletResponse response, Authorization authorization, JsonObjectWithDefault permissions) throws APIException {
       String key = query.get("key", null);
       String value =query.get("value", null);
       if (key == null || value == null ) {
           throw new APIException(400, "Bad Service call, key or value parameters not provided");
       } else {
           if (authorization.getIdentity() == null) {
               throw new APIException(400, "Specified User Setting not found, ensure you are logged in");
           } else {
               Accounting accounting = DAO.getAccounting(authorization.getIdentity());
               JSONObject jsonObject = new JSONObject();
               jsonObject.put(key, value);
               if (accounting.getJSON().has("settings")) {
                   accounting.getJSON().getJSONObject("settings").put(key, value);
               } else {
                   accounting.getJSON().put("settings", jsonObject);
               }
               JSONObject result = new JSONObject();
               result.put("message", "You successfully changed settings to your account!");
               return new ServiceResponse(result);
           }
       }

    }

We will be storing the setting in Json object using key, value pairs. Take the values from user using query.get(“param”,”default value”) and set the default value to null. So that in case the parameters are not present the servlet can return “Bad service call”. To get the accounting object user identity string given by authorization.getIdentity() method is used. Now check if the same user settings is already present, if yes, overwrite it and if not append a new Json object with received key and value. And return the success message through ServiceResponse method.

Proceed to test the working of endpoint at http://127.0.0.1:4000/aaa/changeUserSettings.json?key=theme&value=dark and see if it’s stored using   http://127.0.0.1:4000/aaa/listUserSettings.json.

You have successfully created an endpoint to store user settings and  enhanced Susi Server, take a look and contribute to Susi Server.

Resources

• Read more about Servlets – https://www.tutorialspoint.com/servlets/
• The source code – ListUserSettings.java , ChangeUserSettings.java
• DAO- https://en.wikipedia.org/wiki/Data_access_object

An Alexa skill which can be used to ask susi for answers like: “Alexa, ask susi chat who are you” or “Alexa, ask susi chat what is the temperature in berlin”.

If at any point of time, you are unclear about the code in the blog post, you can check the code of the already made SUSI Alexa skill from the susi_alexa_skill repository.

Getting Started : Alexa Susi AI Skill

Follow the instructions below:

Visit the Amazon developer site and Login.

Click Alexa, on the top bar.

Click Alexa skills kit.

Click on add a new skill button on the top right of the page.

We will be at the skill information tab.

Write the name of the skill Write the invocation name of the skill i.e. the name that will be used to trigger your skill. Like in our case, if we need to ask anything (as we have ‘susi chat’ as the invocation name), we will ask with “Alexa, ask susi chat” as a prefix to our question sentence.

By clicking next, we will be redirected to the second tab i.e. Interaction model. We need to fill two fields here i.e. intent schema and sample utterances. For intent schema, we need to write all the available intents and the parameters for each of them. Like in our case:

{
 "intents": 
 [
   {
     "slots": [
       {
         "name": "query",
         "type": "AMAZON.LITERAL"
       }
     ],
     "intent": "callSusiApi"
   }
 ]
}

We have a single intent that is “callSusiApi” and the parameter it accepts is “query” of type “AMAZON.LITERAL” (in simple words, a string type). Parameters are termed as slots here. The different types of slots available, can be seen from here.

For sample utterances, we need to tell what utterances by the client will lead to what intent. In our case:

We have just one intent and the whole string uttered by the client should be fed to this intent as a “query” slot (parameter).

Let’s click next now.

We will be shifted to the configuration tab.

We will be making a lambda function, which will hold the code for our Susi skill, further we need to link that code to this skill. To do the linking we need to get the Amazon resource name i.e. ARN and fill it in the field named endpoint:

To get the amazon resource name, in a new tab, visit here. Visit “Lambda” followed by get started button. Click on “Create a lambda function”:

We need to select a blueprint for our lambda function. Select the “blank function” option for that.


Click next.

For configure triggers option, click this box and select “Alexa skills kit” option.

Click next.

In configure function tab, just write the name of the function and its description. Let’s code our lambda function:

// basic syntax that should be available in the lambda function
var https = require('http');
exports.handler = (event, context) => {
  try {
    if (event.session.new) {
      // New Session
      console.log("NEW SESSION")
    }
    switch (event.request.type) {
      case "LaunchRequest":
        // Launch Request
        console.log(`LAUNCH REQUEST`)
        context.succeed(
          generateResponse(
            buildSpeechletResponse("Welcome to a Susi Skill, this is an A.I. chatbot developed by Fossasia open source community. Ask anything to me like temperature at a place or rating of a movie or any other thing which you would like to ask?", false),
            {}
          )
        )
        break;
      case "IntentRequest":
        // Intent Request
        console.log(`INTENT REQUEST`)

        switch(event.request.intent.name) {
          case "callSusiApi":
            console.log(event.request.intent.slots.query.value)
            var endpoint = "http://api.susi.ai/susi/chat.json?q="+event.request.intent.slots.query.value; // ENDPOINT GOES HERE
            var body = ""
            https.get(endpoint, (response) => {
              response.on('data', (chunk) => { body += chunk })
              response.on('end', () => {
                var data = JSON.parse(body)
                // fetching answer from susi
                var viewCount = data.answers[0].actions[0].expression;
                if(viewCount.indexOf('I found this on the web') != -1)
                    viewCount = 'I have no idea about it, sorry.';
                context.succeed(
                  generateResponse(
                    buildSpeechletResponse(`${viewCount}`, false),
                    {}
                  )
                )
              })
            })
            break;

          default:
            throw "Invalid intent"
        }

        break;
      case "SessionEndedRequest":
        // Session Ended Request
        console.log(`SESSION ENDED REQUEST`)
        break;
      default:
        context.fail(`INVALID REQUEST TYPE: ${event.request.type}`)
    }
  } catch(error) { context.fail(`Exception: ${error}`) }
}

// Helpers
buildSpeechletResponse = (outputText, shouldEndSession) => {
  return {
    outputSpeech: {
      type: "PlainText",
      text: outputText
    },
    shouldEndSession: shouldEndSession
  }
}

generateResponse = (speechletResponse, sessionAttributes) => {
  return {
    version: "1.0",
    sessionAttributes: sessionAttributes,
    response: speechletResponse
  }
}

Paste this code into the space given below “lambda function code”. In lambda function handler and role, Click the field named role and select “create a custom role” from the dropdown shown.

You will be redirected to a new page. Select the IAM role as lambda_basic_execution:

Click allow button in the bottom right. We will be redirected back to our previous page. We don’t need to worry about other settings on this page.

Click next.

Again cross-check the details shown and click next.

Now we will have our ARN (Amazon resource name) on the top right of the page.


Copy that and paste it into the field “endpoint” on our previously open browser tab:


Click next.

Great that our SUSI AI skill is ready!

Now we can test it with a sample query, when we get redirected to the test tab:

Also we can test it using our voice through reverb app available on play store or echosim by logging into your amazon account.

Till now, the skill can just be invoked or tested from your own amazon id. To make this skill public , you need to fill the other 2 tabs left that are “publishing information” and “privacy and compliance”.

Some sample strings that we can speak to test it: “Alexa, ask susi chat where are you” “Alexa, ask susi chat tell me a joke” “Alexa, ask susi chat what is a table” (where ‘susi chat’ is the invocation name).

This was the video that helped a lot in making this skill for Alexa. It can be referred too.

Being an AI for conversational bots, Hotword detection of SUSI is the top priority to the community. Another requirement was that there should be an option for an offline hotword detection. So, I was searching for an API that has all these capabilities. Sphinx by CMU was the obvious choice. It provides robust mechanism for hotword detection.

What is CMUsphinx?

CMUsphinx is open source and leading speech recognition toolkit. CMUsphinx has different modules for different tasks it needs to perform. Our requirement for SUSI is, that is needs to be lightweight, So we are using Pocketsphinx. Before going into integration let us discuss about basics of speech recognition.

Let us dive into coding and integrating Susi with pocketsphinx.

Building Pocketsphinx .AAR file

Git clone the sphinxbase, pocketsphinx and pocketsphinx-android and put them in the same folder. By following commands below.

git clone http://github.com/cmupshinx/sphinxbase
git clone http://github.com/cmupshinx/pocketsphinx
git clone http://github.com/cmupshinx/pocketsphinx-android

Then import pocketsphinx Android into Android studio. Run the project. .aar files pocketsphinx-android-5prealpha-debug.aar & pocketsphinx-android-5prealpha-release.aar  will be created in the build/outputs/aar.

Integrating Susi with Pocketsphinx

In Android Studio you need to the above generated AAR into your project. Just go to File > New > New module and choose Import .JAR/.AAR Package. After this, We need to change permissions of project. Add the following permissions in AndroidManifest.xml.

<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
<uses-permission android:name="android.permission.RECORD_AUDIO" />

Import the following functions into your main activity.

import edu.cmu.pocketsphinx.Assets;
import edu.cmu.pocketsphinx.Hypothesis;
import edu.cmu.pocketsphinx.RecognitionListener;
import edu.cmu.pocketsphinx.SpeechRecognizer;
import edu.cmu.pocketsphinx.SpeechRecognizerSetup;

Next we need to sync the assets we get from .aar file in to our project. Edit app/build.gradle build file to run assets.xml. We do it by adding following code to build.gradle.

ant.importBuild 'assets.xml'
preBuild.dependsOn(list, checksum)
clean.dependsOn(clean_assets)

Now all the import and sync errors of gradle must disappear and you should be good to go. You can start your recognizer by adding this code to your activity.

recognizer = defaultSetup()
        .setAcousticModel(new File(assetsDir, "en-us-ptm"))
        .setDictionary(new File(assetsDir, 
"cmudict-en-us.dict"))
        .getRecognizer();
recognizer.addListener(this);

Decoder model is lengthy process that contains many operations, so it’s recommended to run in inside async task. These are commands for decoder to run. These commands essentially do acoustic and language modelling of speech.

// Create keyword-activation search.
recognizer.addKeyphraseSearch(KWS_SEARCH, KEYPHRASE);

// Create grammar-based searches.
File menuGrammar = new File(assetsDir, "menu.gram");
recognizer.addGrammarSearch(MENU_SEARCH, menuGrammar);

// Next search for digits
File digitsGrammar = new File(assetsDir, "digits.gram");
recognizer.addGrammarSearch(DIGITS_SEARCH, digitsGrammar);

// Create language model search.
File languageModel = new File(assetsDir, "weather.dmp");
recognizer.addNgramSearch(FORECAST_SEARCH, languageModel);

Speech recognition will end at onEndOfSpeech callback of the recognizer listener.  We can call recognizer.stop or recognizer.cancel(). Cancel will cancel the recognition, stop will cause the final result be passed you in onResult callback. During the recognition, you will get partial results in onPartialResult callback.

Now we have integrated Pocketsphinx with SUSI.AI in Android.