Countrywise Usage Analytics of a Skill in SUSI.AI

The statistics regarding which country the skills are being used is quite important. They help in updating the skill to support the native language of those countries. SUSI.AI must be able to understand as well as reply in its user’s language. So mainly the server side and some client side (web client) implementation of country wise skill usage statistics is explained in this blog.

Fetching the user’s location on the web client

  1. Add a function in chat.susi.ai/src/actions/API.actions.js to fetch the users location. The function makes a call to freegeoip.net API which returns the client’s location based on its IP address. So country name and code are required for country wise usage analytics.

export function getLocation(){
  $.ajax({
    url: 'https://cors-anywhere.herokuapp.com/http://freegeoip.net/json/',
    success: function (response) {
      _Location = {
        lat: response.latitude,
        lng: response.longitude,
        countryCode: response.country_code,
        countryName: response.country_name
      };
    },
  });
}
  1. Send the location parameters along with the query while fetching reply from SUSI server in chat.json API. The parameters are country_name and country_code.

if(_Location){
	url += '&latitude='+_Location.lat+'&longitude='+_Location.lng+'&country_code='+_Location.countryCode+'&country_name='+_Location.countryName;
}

Storage of Country Wise Skill Usage Data on SUSI Server

  1. Create a countryWiseSkillUsage.json file to store the country wise skill usage stats and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the country name, country code and the usage count in that country.
  1. Modify the src/ai/susi/server/api/susi/SusiService.java file to fetch country_name and country_code from the query parameters and pass them SusiCognition constructor.

String countryName = post.get("country_name", "");
String countryCode = post.get("country_code", "");
...

SusiCognition cognition = new SusiCognition(q, timezoneOffset, latitude, longitude, countryCode, countryName, language, count, user.getIdentity(), minds.toArray(new SusiMind[minds.size()]));
  1. Modify the src/ai/susi/mind/SusiCognition.java file to accept the countryCode and countryName in the constructor parameters. Check which skill is being currently used for the response and update the skill usage stats for that country in countryWiseSkillUsage.json. Call the function updateCountryWiseUsageData() to update the skill usage data.

if (!countryCode.equals("") && !countryName.equals("")) {
    List<String> skills = dispute.get(0).getSkills();
    for (String skill : skills) {
        try {
            updateCountryWiseUsageData(skill, countryCode, countryName);
        }
        catch (Exception e) {
            e.printStackTrace();
        }
    }
}

The updateCountryWiseUsageData() function accepts the skill path , country name and country code. It parses the skill path to get the skill metadata like its model name, group name, language etc. The function then checks if the country already exists in the JSON file or not. If it exists then it increments the usage count by 1 else it creates an entry for the skill in the JSON file and initializes it with the current country name and usage count 1.

for (int i = 0; i < countryWiseUsageData.length(); i++) {
  countryUsage = countryWiseUsageData.getJSONObject(i);
  if (countryUsage.get("country_code").equals(countryCode)) {
    countryUsage.put("count", countryUsage.getInt("count")+1);
    countryWiseUsageData.put(i,countryUsage);
  }
}

API to access the Country Wise Skill Usage Data

  1. Create GetCountryWiseSkillUsageService.java file to return the usage stats stored in countryWiseSkillUsage.json

public ServiceResponse serviceImpl(Query call, HttpServletResponse response, Authorization rights, final JsonObjectWithDefault permissions) {
        
  ...  // Fetch the query parameters
  JSONArray countryWiseSkillUsage = languageName.getJSONArray(skill_name);
  return new ServiceResponse(result);
}
  1. Add the API file to src/ai/susi/server/api/susi/SusiServer.java

services = new Class[]{
	...
	//Skill usage data
	GetCountryWiseSkillUsageService.class
	...
}

 

Endpoint : /cms/getCountryWiseSkillUsage.json

Parameters

  • model
  • group
  • language
  • Skill

Sample query: /cms/getCountryWiseSkillUsage.json?model=general&group=Knowledge&language=en&skill=aboutsusi

Sample response:

{  
   "skill_usage":[  
      {  
         "country_code":"MYS",
         "country_name":"Malaysia",
         "count":1
      },
      {  
         "country_code":"MYS",
         "country_name":"Malaysia",
         "count":1
      }
   ],
   "session":{  
      "identity":{  
         "type":"host",
         "name":"162.158.154.147_81c88a10",
         "anonymous":true
      }
   },
   "skill_name":"ceo",
   "accepted":true,
   "message":"Country wise skill usage fetched"
}

Resources

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Showing top metrics from skill groups

SUSI.AI shows top metrics on the home page. They include highest rated skills, most used skills, latest skills and skills with most feedbacks etc. Now the idea is to include top skills from a particular category also. For example “SUSI, what are your top games”? So how to fetch the required metrics in a generalized way.

Updating the skill metrics data API

Add an API parameter in SkillMetricsDataService.java to specify the names of groups to fetch the required metrics. It accepts a semicolon (;) separated list of group names. If no group is passed then by default it shows the top games.

String metrics_list = call.get("metrics", "Games, Trivia and Accessories");
String[] metrics_names = metrics_list.split(";");

Split the metrics parameter by semicolon and store in an array. This array contains all the groups of which top skills are to be displayed on the CMS home page. Loop over the array, group by group and filter out the skills that don’t belong to the current metrics group. Sort the filtered skills in decreasing order of the overall rating. But this sorting does not simply arrange the skills in decreasing order of their overall rating. Actually, it divides the skills into 2 halves. The first half contains the skills that have been rated by at least 10 users in decreasing order of the overall ratings. And the second half contains the rest of the skills in decreasing order of their overall rating.

for (String metric_name : metrics_names) {
    try {
        metric_name = metric_name.trim();
        List<JSONObject> groupJsonValues = new ArrayList<JSONObject>();
        for (int i = 0; i < jsonArray.length(); i++) {
            if (jsonArray.getJSONObject(i).get("group").toString().equals(metric_name)) {
                groupJsonValues.add(jsonArray.getJSONObject(i));
            }
        }
        // Get skills based on ratings of a particular group
        SusiSkill.sortByAvgStar(groupJsonValues, false);

        JSONArray topGroup = new JSONArray();
        topGroup = getSlicedArray(groupJsonValues, count);
        skillMetrics.put(metric_name, topGroup);
    }
    catch (Exception e) {
        e.printStackTrace();
    }
}

So if top games and news skills are to be shown on the CMS then the API endpoint looks like :

https://api.susi.ai/cms/getSkillMetricsData.json?metrics=Games, Trivia and Accessories; News

And the response is like :

{
	accepted: true,
	model: "general",
	group: "All",
	language: "en",
	metrics: {
		+newest: [...],
		+latest: [...],
		+rating: [...],
		+usage: [...],
		+feedback: [...],
		+staffPicks: [...],
		+Games, Trivia and Accessories: [...],
		+News: [...]
	},
	message: "Success: Fetched skill data based on metrics"
}

Resources

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Make a cumulative API to return skills based on standard metrics in SUSI.AI

In this blog post, we are going to discuss on how to make a cumulative API which returns skills based on different standard metrics. It was implemented to combine various API calls, that were made from various clients, thereby reducing the number of API calls made. It lead to an optimization in the page load time of various clients and also helped to reduce the 503 errors that were received, due to very frequent API hits. The API endpoint for it is https://api.susi.ai/cms/getSkillMetricsData.json.

It accepts 5 optional parameters –

  • model – It represents the model for which the skill are fetched. The default value is set to General.
  • group – It represents the group(category) for which the skill are fetched. The default value is set to Knowledge.
  • language – It represents the language for which the skill are fetched. The default value is set to en.
  • duration – It represents the duration based on which skills are fetched by standard metrics like usage.
  • count – It represents the number of skills to be returned on a particular metric.

The minimalUserRole is set to ANONYMOUS for this API, as the data is required for home-page and doesn’t need the user to be logged-in.

Going through the API development

  • The parameters are first extracted via the call object that is passed to the main function. The  parameters are then stored in variables. If any parameter is absent, then it is set to the default value.
  • There is a check if the count param exists. If exists, then it is checked if it is of valid data-type. If no, an exception is thrown. And if count param doesn’t exist, it is set to default of 10.
  • This code snippet discusses the above two points –
@Override
public ServiceResponse serviceImpl(Query call, HttpServletResponse response, Authorization rights, final JsonObjectWithDefault permissions) throws APIException {

        String model_name = call.get("model", "general");
        File model = new File(DAO.model_watch_dir, model_name);
        String group_name = call.get("group", "All");
        String language_name = call.get("language", "en");
        int duration = call.get("duration", 0);
        JSONArray jsonArray = new JSONArray();
        JSONObject json = new JSONObject(true);
        JSONObject skillObject = new JSONObject();
        String countString = call.get("count", null);
        Integer count = null;

        if(countString != null) {
            if(Integer.parseInt(countString) < 0) {
                throw new APIException(422, "Invalid count value. It should be positive.");
            } else {
                try {
                    count = Integer.parseInt(countString);
                } catch(NumberFormatException ex) {
                    throw new APIException(422, "Invalid count value.");
                }
            }
        } else {
            count = 10;
        }
.
.
.

 

  • Then the skills are fetched based on the group name and then stored in a JSONArray named jsonArray. This array basically contains the metadata objects of each skill.
  • Now, we need to sort and filter these skills based on standard metrics like – Rating, Feedback Count, Usage Count, Latest skills.
  • The implementation for getting the skills based on the creation time is as follows. Rest, all the metrics were also implemented in the same fashion.

JSONObject skillMetrics = new JSONObject();
List<JSONObject> jsonValues = new ArrayList<JSONObject>();

// temporary list to extract objects from skillObject
for (int i = 0; i < jsonArray.length(); i++) {
    jsonValues.add(jsonArray.getJSONObject(i));
}

// Get skills based on creation date - Returns latest skills
Collections.sort(jsonValues, new Comparator<JSONObject>() {
    private static final String KEY_NAME = "creationTime";
    @Override
    public int compare(JSONObject a, JSONObject b) {
        String valA = new String();
        String valB = new String();
        int result = 0;

        try {
            valA = a.get(KEY_NAME).toString();
            valB = b.get(KEY_NAME).toString();
            result = valB.compareToIgnoreCase(valA);
        } catch (JSONException e) {
            e.printStackTrace();
        }
        return result;
    }
});

JSONArray creationDateData = getSlicedArray(jsonValues, count);
skillMetrics.put("latest", creationDateData);

.
.
.

 

  • The above code snippet deals with sorting the skills based on the creation time, that helps us to fetch the latest skills. The latest skills are stored on the skillMetrics object with the key name latest.
  • Similarly, the skills based on metrics like rating, feedback count and usage are stored with key names rating, feedback & usage respectively.

From the above snippet, we can also see a call to the function getSlicedArray. It takes a list of skills and count as input paramters. It returns the first ‘count’ skills from the list depending on the count value. The implementation of it is as follows –

private JSONArray getSlicedArray(List<JSONObject> jsonValues, Integer count)
{
    JSONArray slicedArray = new JSONArray();
    for (int i = 0; i < jsonValues.size(); i++) {
        if(count == 0) {
            break;
        } else {
            count --;
        }
        slicedArray.put(jsonValues.get(i));
    }
    return slicedArray;
}

 

  • The response object is then sent with 6 (six) key values mainly, apart from the session object. They are
    • accepted –  true – It tells that the skills have been fetched.
    • message – “Success: Fetched skill data based on metrics”
    • model –  It is the model that is sent on request params or the default value.
    • group –  It is the group that is sent on request params or the default value.
    • language – It is the language that is sent on request params or the default value.
    • metrics –  It is the main object of relevance for this API, which contains 4 child keys with values as an array of Skill Metadata objects.

{
  "accepted": true,
  "model": "general",
  "group": "All",
  "language": "en",
  "metrics": {
    "feedback": [
    {skill_1}, {skill_2}, ...
    ],
    "usage": [
    {skill_1}, {skill_2}, ...
    ],
    "rating": [
    {skill_1}, {skill_2}, ...
    ],
    "latest": [
    {skill_1}, {skill_2}, ...
    ]
  },
  "message": "Success: Fetched skill data based on metrics",
  "session": {
   ....
  }
}

 

I hope the development of creating the aforesaid API and the purpose of it is clear and proved to be helpful for your understanding.

References

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Add Feature to Post Feedback for a Skill in SUSI.AI Server

In this blog post, we are going to discuss on how the feature to post user feedback was implemented on the SUSI. AI server side. The API endpoint by which a user can post feedback for a Skill is https://api.susi.ai/cms/feedbackSkill.json.

It accepts five url parameters –

  • model – It tells the model to which the skill belongs. The default value is set to General.
  • group – It tells the group(category) to which the skill belongs. The default value is set to Knowledge.
  • language – It tells the language to which the skill belongs. The default value is set to en.
  • skill – It is the name of the skill to be given a feedback. It is a compulsory param.
  • feedback – It is the parameter that contains the string type feedback of the skill, that the user wants to update.

The minimalUserRole is set to USER for this API, as only logged-in users can use this API.

Going through the API development

  • The parameters are first extracted via the call object that is passed to the main function. The  parameters are then stored in variables. If any parameter is absent, then it is set to the default value.
  • There is a check if the skill exists. It is done by checking, if there exists a file corresponding to that skill. If no, then an exception is thrown.
  • This code snippet discusses the above two points –

@Override
public ServiceResponse serviceImpl(Query call, HttpServletResponse response, Authorization authorization, final JsonObjectWithDefault permissions) throws APIException {

        String model_name = call.get("model", "general");
        File model = new File(DAO.model_watch_dir, model_name);
        String group_name = call.get("group", "Knowledge");
        File group = new File(model, group_name);
        String language_name = call.get("language", "en");
        File language = new File(group, language_name);
        String skill_name = call.get("skill", null);
        File skill = SusiSkill.getSkillFileInLanguage(language, skill_name, false);
        String skill_feedback = call.get("feedback", null);

        JSONObject result = new JSONObject();
        if (!skill.exists()) {
            throw new APIException(422, "Skill does not exist.");
        }
.
.
.

 

  • Then the  feedbackSkill.json that was initialized in the DAO object, is then opened. It is a JSONTray, which is then parsed.
  • It is checked if the skill is previously rated or not. If yes, has the user rated it before or not. In the case, when the user has given a feedback for it already, we just need to make changes in the feedbackSkill.json file as the feedback_count key present in the skillRatings.json doesn’t change. In case, the user hasn’t given the feedback earlier, the changes also needs to be reflected on the skillRatings.json file. For this use,  the utility function addToSkillRatingJSON is called. The feedback_count of that skill is increased by one, as it is a unique feedback and changes need to be reflected on the file.
  • The response object is then sent with three key values mainly, apart from the session object. They are
    • accepted –  true – It tells that the skill feedback has been updated.
    • message – “Skill feedback updated”
    • feedback –  <feedback> – It is the string that the client has sent as feedback.

Here are code snippets and data objects that will help in understanding the API well –

  • addToSkillRating function –
    public void addToSkillRatingJSON(Query call) {
        String model_name = call.get("model", "general");
        String group_name = call.get("group", "Knowledge");
        String language_name = call.get("language", "en");
        String skill_name = call.get("skill", null);

        JsonTray skillRating = DAO.skillRating;
        JSONObject modelName = new JSONObject();
        JSONObject groupName = new JSONObject();
        JSONObject languageName = new JSONObject();
        JSONObject skillName = new JSONObject();
        if (skillRating.has(model_name)) {
            modelName = skillRating.getJSONObject(model_name);
            if (modelName.has(group_name)) {
                groupName = modelName.getJSONObject(group_name);
                if (groupName.has(language_name)) {
                    languageName = groupName.getJSONObject(language_name);
                    if (languageName.has(skill_name)) {

                        skillName = languageName.getJSONObject(skill_name);
                    }
                }
            }
        }

        if (skillName.has("feedback_count")) {

            int skillFeedback = skillName.getInt("feedback_count");
            skillName.put("feedback_count", skillFeedback + 1 );
        } else {

            skillName.put("feedback_count", 1);
        }
                        
        if (!skillName.has("stars")) {

            JSONObject skillStars = new JSONObject();
            skillStars.put("one_star", "0");
            skillStars.put("two_star", "0");
            skillStars.put("three_star", "0");
            skillStars.put("four_star", "0");
            skillStars.put("five_star", "0");
            skillStars.put("avg_star", "0");
            skillStars.put("total_star", "0");
            skillName.put("stars", skillStars);
        }

        languageName.put(skill_name, skillName);
        groupName.put(language_name, languageName);
        modelName.put(group_name, groupName);
        skillRating.put(model_name, modelName, true);
        return;
    }
}

 

  • Sample  of feedbackSkill.json file –

{
  "general": {
    "Knowledge": {
      "en": {
        "aboutsusi": [
          {
            "feedback": "The skill is awesome!",
            "email": "[email protected]",
            "timestamp": "2018-06-06 02:20:20.245"
          },
          {
            "feedback": "Great!",
            "email": "[email protected]",
            "timestamp": "2018-06-06 02:20:20.245"
          }
        ],
        "quotes": [
          {
            "feedback": "Wow!",
            "email": "[email protected]",
            "timestamp": "2018-06-06 02:19:53.86"
          }
        ]
      }
    }
  }
}
  • Sample  of skillRatings.json file –

{
  "general": {
    "Knowledge": {
      "en": {
        "aboutsusi": {
          "negative": "0",
          "positive": "0",
          "stars": {
            "one_star": 0,
            "four_star": 2,
            "five_star": 0,
            "total_star": 5,
            "three_star": 3,
            "avg_star": 3.4,
            "two_star": 0
          },
          "feedback_count": 2
        },
        "quotes": {
          "negative": "0",
          "positive": "0",
          "stars": {
            "one_star": 0,
            "four_star": 0,
            "five_star": 0,
            "total_star": 0,
            "three_star": 0,
            "avg_star": 0,
            "two_star": 0
          },
          "feedback_count": 1
        }
      }
    }
  }
}

 

I hope the development of creating the aforesaid API is clear and proved to be helpful for your understanding.

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Showing only those languages for which skills are available

SUSI.AI is available for almost all the internationally recognised languages of the world. An author is allowed to create a skill in any of these languages. But there are some languages for which skills have not been created yet. So only those languages should be shown in the SUSI Skill CMS for which the skills are available. The approach is that all the languages must be listed while creating the skills but only non-empty languages must be listed while filtering skills on the CMS category page.

Updating the get languages API

  1. Add an API parameter in GetAllLanguages.java to fetch the group name. It is used to fetch the list of languages for which skills are available in that particular group. If no group is passed it means that the all the languages are to be listed. For that, we can use any group and show all the languages in that group. Say “Knowledge”.

String group_name = call.get("group", null);
if (group_name == null) {
    File group = new File(model, "Knowledge");
}

 

  1. Now check if the file inside the group folder is a directory. If yes then add it to the list of languages to be returned.

String[] languages = group.list((current, name) -> new File(current, name).isDirectory());

 

  1. If the languages corresponding to a particular category are to be fetched then first checked if the group is “All” or any specific group. Since the “All” category is not stored as such so we need to iterate over all the groups present in the parent directory ie, the model directory.

String[] group_names = model.list((current, name) -> new File(current, name).isDirectory());

 

  1. Now iterate over all the groups present in the group_names array and list the files present in it. Apply a filter to the list that accepts a file only if it is a directory and not empty ie, contains at least 1 language. Add that file to the list of languages.

group.list(new FilenameFilter() {
	@Override
	public boolean accept(File file, String s) {
		Boolean accepted = new File(file, s).list().length > 1;
		if (accepted) {
			if (!languages.contains(s)) {
		    	languages.add(s);
			}
		}
		return accepted;
	}
});

 

  1. The processing of getting languages for a particular group is same, just the iteration over the model directory is not required.

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SUSI AI 5 Star Skill Rating System

For making a system more reliable and robust, continuous evaluation is quite important. So is in case of SUSI AI. User feedback is important to improve SUSI skills and create new ones. Previously we had only thumbs up / thumbs down as a feedback method, from the susi chat client. But now a 5 star rating system has been added to the SUSI Skill CMS so that users can rate a skill there. Before the implementation of API  let’s look how data is stored in SUSI AI Susi_server uses DAO in which skill rating is stored as JSONTray.

The server side implementation

A new java class has been created for the API, FiveStarRateSkillService.java.

public class FiveStarRateSkillService extends AbstractAPIHandler implements APIHandler {
    private static final long serialVersionUID =7947060716231250102L;
    @Override
    public BaseUserRole getMinimalBaseUserRole() {
        return BaseUserRole.USER;
    }
    @Override
    public JSONObject getDefaultPermissions(BaseUserRole baseUserRole) {
        return null;
    }
    @Override
    public String getAPIPath() {
        return "/cms/rateSkill.json";
    }
...
}

The getMinimalBaseRole method tells the minimum User role required to access this servlet it can also be ADMIN, USER or ANONYMOUS. In our case it is USER. A user needs to be logged in to rate a skill on a scale of 1-5 stars.  The API runs at “/cms/fiveStarRateSkill.json” endpoint.

Next, create serviceImpl method in the above class to handle the request from the client and respond to it.

1. Fetch the required query parameters and store them in variables. They include skill model, group, language, skill name and starts that the user has given in the rating.

String skill_name = call.get("skill", null);
String skill_stars = call.get("stars", null);

2. Then check if the skill exists. If not them throw an exception. Otherwise, increment the count of the corresponding rating. The rating object has keys as one_star, two_star, three_star, four_star and five_star that has the count of that star rating.       

if (skill_stars.equals("1")) {
    skillName.put("one_star", skillName.getInt("one_star") + 1 + "");
}
else if (skill_stars.equals("2")) {
    skillName.put("two_star", skillName.getInt("two_star") + 1 + "");
}
else if (skill_stars.equals("3")) {
    skillName.put("three_star", skillName.getInt("three_star") + 1 + "");
}
else if (skill_stars.equals("4")) {
    skillName.put("four_star", skillName.getInt("four_star") + 1 + "");
}
else if (skill_stars.equals("5")) {
    skillName.put("five_star", skillName.getInt("five_star") + 1 + "");
}

3. Re-calculate the total rating done on that skill and its average rating and update the object. If the skill has not been already rated then create a new rating object and initialize it with the 0 star counts.

public JSONObject createRatingObject(String skill_stars) {
        JSONObject skillName = new JSONObject();
        JSONObject skillStars = new JSONObject();

        skillStars.put("one_star", 0);
        skillStars.put("two_star", 0);
        skillStars.put("three_star", 0);
        skillStars.put("four_star", 0);
        skillStars.put("five_star", 0);
        skillStars.put("avg_star", 0);
        skillStars.put("total_star", 0);

        skillName.put("stars", skillStars);
}

The complete FiveStarRateSkillService.java is available here : –

https://github.com/fossasia/susi_server/blob/development/src/ai/susi/server/api/cms/FiveStarRateSkillService.java

Rating a skill

Sample endpoint

https://api.susi.ai/cms/fiveStarRateSkill.json?model=general&group=Knowledge&language=en&skill=aboutsusi&stars=3&callback=p&_=1526813916145

This gives 3 star rating to the “aboutsusi” skill.

Parameters

  • Model
  • Group
  • Language
  • Skill
  • Stars

Response

{
  "ratings": {
    "one_star": 0,
    "four_star": 0,
    "five_star": 1,
    "total_star": 1,
    "three_star": 0,
    "avg_star": 5,
    "two_star": 0
  },
  "session": {"identity": {
    "type": "email",
    "name": "[email protected]",
    "anonymous": false
  }},
  "accepted": true,
  "message": "Skill ratings updated"
}

Getting the stats of Skill Ratings

Sample endpoint

http://127.0.0.1:4000/cms/getSkillRating.json?model=general&group=Knowledge&language=en&skill=aboutsusi&callback=p&_=1526813916145

This fetches the current ratings of the “aboutsusi” skill.

Parameters

  • Model
  • Group
  • Language
  • Skill

Response

{
    "session": {
        "identity": {
            "type": "host",
            "name": "172.68.144.159_81c88a10",
            "anonymous": true
        }
    },
    "skill_name": "aboutsusi",
    "accepted": true,
    "message": "Skill ratings fetched",
    "skill_rating": {
        "negative": "0",
        "positive": "0",
        "stars": {
            "one_star": 0,
            "four_star": 2,
            "five_star": 1,
            "total_star": 4,
            "three_star": 1,
            "avg_star": 4,
            "two_star": 0
        },
        "feedback_count": 3
    }
}

Conclusion

So this 5 star rating system will help in improving the SUSI skills. Also, it will help in making better decisions when we have multiple similar skills and we have to choose one to respond to the user query.

References

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Implementation of Set Different Language for Query in SUSI Android

SUSI.AI has many skills. Some of which are displaying web search of a certain query, provide a list of relevant information of a topic, displaying a map of the certain position and simple text message of any query. Previously SUSI.AI reply all query in English language but now one important feature is added in SUSI.AI and that feature is, reply query of the user in the language that user wants. But to get the reply in different language user has to send language code of that language along with query to SUSI Server. In this blog post, I will show you how it is implemented in SUSI Android app.

Different language supported in SUSI Android

Currently, different languages added in option in SUSI Android and their language code are:

Language Language Code
English en
German de
Spanish es
French fr
Italian it
Default Default language of the device.

Layout design

I added an option for choosing the different language in settings. When the user clicks on Language option a dialog box pops up. I used listpreference to show the list of different languages.

<ListPreference

  android:title=“@string/Language”

  android:key=“Lang_Select”

  android:entries=“@array/languagentries”

  android:entryValues=“@array/languagentry”>

</ListPreference>

“title” is used to show the title of setting, “entries” is used to show the list of entry to the user and “entryValue” is the value corresponding to each entry. I used listpreference because it has own UI so we don‘t have to develop our own UI for it and also it stores the string into the SharedPreferences so we don’t need to manage the values in SharedPreference. SharedPreference needed to set value in Language in settings so that once user close app and again open it setting will show same value otherwise it will show default value. We used an array of string to show the list of languages.

<string-array name=“languagentries”>

  <item>Default</item>

  <item>English</item>

  <item>German</item>

  <item>Spanish</item>

  <item>French</item>

  <item>Italian</item>

</string-array>

SetLanguage implementation

To set language user must choose Language option in setting.

On clicking Language option a dialog box pop up with the list of different languages. When the user chooses a language then we save corresponding language code in preference with key “prefLanguage” so that we can use it later to send it to the server along with the query. It also uses to send language to the server to store user language on the server, so that user can use the same language in the different client.

querylanguage.setOnPreferenceChangeListener { _, newValue ->

  PrefManager.putString(Constant.LANGUAGE, newValue.toString())

  if(!settingsPresenter.getAnonymity()) {

      settingsPresenter.sendSetting(Constant.LANGUAGE, newValue.toString(), 1)

  }

}

newValue.toString() is the value i.e language code of corresponding language.

Now when we query anything from SUSI.AI we send language code along with query to the server. Default language is default language of the device. Before sending language to the server we check language is default language or any specific language.

val language = if (PrefManager.getString(Constant.LANGUAGE, Constant.DEFAULT).equals(Constant.DEFAULT))

Locale.getDefault().language

else PrefManager.getString(Constant.LANGUAGE, Constant.DEFAULT)

And after that, we send the corresponding language along with query to the server.

clientBuilder.susiApi.getSusiResponse(timezoneOffset, longitude, latitude, source, language, query)

Reference

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Reset Password Option in SUSI Android App

Login and signup are an important feature for some android apps like chat apps because the user will want to save and secure personal messages from others. In SUSI Android app we provide a token to a logged-in user for a limit period of time so that once the user logs in and someone else gets access to the device, then he/she can’t use the user account for a long period of time. It is a security provided from our side but the user also has to maintain some security. Cyber security risks have increased and hacking technologies have improved a lot in the past 10 years. So, using the same password for a long period of time absolutely puts your account security at risk. So to keep your account secure you should change/reset your password regularly. In this blog post, I will show you how reset password option is implemented in SUSI Android app.

Layout design for reset password

Reset password option is added in the setting. When the user clicks on reset password option a dialog box pops up. There are three textinputlayout boxes – each for the current password, new password and confirm password. I have used textinputlayout instead of simple edittext box because it helps user to show first “hint” and when user taps on, hint will come up with text over it as floating label so that the user can understand what to add in that box and also in case of error we can show that error to user.

Reset Password implementation

On clicking reset password option a dialog box appears in which user inserts the current password, new password and confirm password to confirm the new password. Before sending new password to the server we perform two checks

  1. New password should not be empty and length of new password should be at least six.
  2. New password and confirm password must be same.
if (!CredentialHelper.isPasswordValid(newPassword)) {

settingView?.passwordInvalid(Constant.NEW_PASSWORD)

return

}

if (newPassword != conPassword) {

settingView?.invalidCredentials(false, Constant.NEW_PASSWORD)

return

}

And when these two checks are passed we send “new password” to server.

Endpoint use to reset password is

http://api.susi.ai/aaa/changepassword.json?changepassword=your mail id&password=current password&newpassword=newpassword

As you can see it needed three parameters

  • changepassword: Your email id
  • password : Your current password
  • newpassword: Your new password

When user logs in, we save user’s email id so that the user doesn’t have to provide it again and again when the user wants to change the password.

utilModel.saveEmail(email)

The user provides current password and new password through dialog box. We used resetPassword method to reset the password. We send these three parameters to the server using resetPassword method and if the password changed successfully then server sends a message.

override fun resetPassword(password: String, newPassword: String, listener: ISettingModel.onSettingFinishListener) {

  val email = PrefManager.getString(Constant.SAVE_EMAIL, null)

  resetPasswordResponseCall = ClientBuilder().susiApi

          .resetPasswordResponse(email,password,newPassword)

  resetPasswordResponseCall.enqueue(object : Callback<ResetPasswordResponse> {

  } )

}

We used retrofit library for network call and resetPasswordResponse is a model class using which we are retrieving server response.

Reference

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Adding Servlet for SUSI Service

Whenever we ask anything to SUSI, we get a intelligent reply. The API endpoints which clients uses to give the reply to users is http://api.susi.ai/susi/chat.json, and this API endpoint is defined in SUSIService.java. In this blog post I will explain how any query is processed by SUSI Server and the output is sent to clients.

public class SusiService extends AbstractAPIHandler implements APIHandler

This is a public class and just like every other servlet in SUSI Server, this extends AbstractAPIHandler class, which provides us many options including AAA related features.

    public String getAPIPath() {
           return "/susi/chat.json";
    }

The function above defines the endpoint of the servlet. In this case it is  “/susi/chat.json”. The final API Endpoint will be API http://api.susi.ai/susi/chat.json.

This endpoint accepts 6 parameters in GET request . “q” is the query “parameter”, “timezoneOffset” and “language” parameters are for giving user a reply according to his local time and local language, “latitude” and “longitude” are used for getting user’s location.

      String q = post.get("q", "").trim();
        int count = post.get("count", 1);
        int timezoneOffset = post.get("timezoneOffset", 0); // minutes, i.e. -60
        double latitude = post.get("latitude", Double.NaN); // i.e. 8.68
        double longitude = post.get("longitude", Double.NaN); // i.e. 50.11
        String language = post.get("language", "en"); // ISO 639-1

After getting all the parameters we do a database update of the skill data. This is done using DAO.susi.observe() function.

Then SUSI checks that whether the reply was to be given from a etherpad dream, so we check if we are dreaming something.

if (etherpad_dream != null && etherpad_dream.length() != 0) {
    String padurl = etherpadUrlstub + "/api/1/getText?apikey=" + etherpadApikey + "&padID=$query$";

If SUSI is dreaming something then we call the etherpad API with the API key and padID.

After we get the response from the etherpad API we parse the JSON and store the text from the skill in a local variable.

JSONObject json = new JSONObject(serviceResponse);
String text = json.getJSONObject("data").getString("text");

After that, we fill an empty SUSI mind with the dream. SUSI susi_memory_dir is a folder named as “susi” in the data folder, present at the server itself.

SusiMind dream = new SusiMind(DAO.susi_memory_dir); 

We need the memory directory here to get a share on the memory of previous dialogues, otherwise, we cannot test call-back questions.

JSONObject rules = SusiSkill.readEzDSkill(new BufferedReader(new InputStreamReader(new ByteArrayInputStream(text.getBytes(StandardCharsets.UTF_8)), StandardCharsets.UTF_8)));
dream.learn(rules, new File("file://" + etherpad_dream));

When we call dream.learn function, Susi starts dreaming. Now we will try to find an answer out of the dream.

The SusiCognition takes all the parameters including the dream name, query and user’s identity and then try to find the answer to that query based on the skill it just learnt.

    SusiCognition cognition = new SusiCognition(dream, q, timezoneOffset, latitude, longitude, language, count, user.getIdentity());

If SUSI finds an answer then it replies back with that answers else it tries to answer with built-in intents. These intents are both existing SUSI Skills and internal console services.

if (cognition.getAnswers().size() > 0) {          DAO.susi.getMemories().addCognition(user.getIdentity().getClient(), cognition);
    return new ServiceResponse(cognition.getJSON());
}

This is how any query is processed by SUSI Server and the output is sent to clients.

Currently people use Etherpad (http://dream.susi.ai/) to make their own skills, but we are also working on SUSI CMS where we can edit and test the skills on the same web application.

References

Java Servlet Docs: http://docs.oracle.com/javaee/6/tutorial/doc/bnafd.html

Embedding Jetty: http://www.eclipse.org/jetty/documentation/9.4.x/embedding-jetty.html

Server-Side Java: http://www.javaworld.com/article/2077634/java-web-development/how-to-get-started-with-server-side-java.html

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