Fetch Five Star Skill Rating from getSkillList API in SUSI.AI Android

SUSI.AI had a thumbs up/down rating system till now, which has now been replaced by a five star skill rating system. Now, the user is allowed to rate the skill based on a five star rating system. The UI components include a rating bar and below the rating bar is a section that displays the skill rating statistics – total number of ratings, average rating and a graph showing the percentage of users who rated the skill with five stars, four stars and so on.

SUSI.AI Skills are rules that are defined in SUSI Skill Data repo which are basically the processed responses that SUSI returns to the user queries. When a user queries something from the SUSI Android app, a query to SUSI Server is made which in turn fetches data from SUSI Skill Data and returns a JSON response to the app. Similarly, to get skill ratings, a call to the ‘/cms/getSkillList.json’ API is made. In this API, the server checks the SUSI Skill Data repo for the skills and returns a JSON response consisting of all the required information like skill name, author name, description, ratings, etc. to the app. Then, this JSON response is parsed to extract individual fields to display the appropriate information in the skill details screen of the app.

API Information

The endpoint to fetch skills is ‘/cms/getSkillList.json’
The endpoints takes three parameters as input –

  • 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 All.
  • language – It tells the language to which the skill belongs. The default value is set to en.

Since all skills have to be fetched, this API is called for every group individually. For instance, call “https://api.susi.ai/cms/getSkillList.json?group=Knowledge” to get all skills in group “Knowledge”. Similarly, call for other groups.

Here is a sample response of a skill named ‘Capital’ from the group Knowledge :

"capital": {
      "model": "general",
      "group": "Knowledge",
      "language": "en",
      "developer_privacy_policy": null,
      "descriptions": "A skill to tell user about capital of any country.",
      "image": "images/capital.png",
      "author": "chashmeet singh",
      "author_url": "https://github.com/chashmeetsingh",
      "skill_name": "Capital",
      "terms_of_use": null,
      "dynamic_content": true,
      "examples": ["What is the capital of India?"],
      "skill_rating": {
        "negative": "0",
        "positive": "4",
        "feedback_count" : 0,
        "stars": {
          "one_star": 0,
          "four_star": 1,
          "five_star": 0,
          "total_star": 1,
          "three_star": 0,
          "avg_star": 4,
          "two_star": 0
        }
      },
      "creationTime": "2018-03-17T17:11:59Z",
      "lastAccessTime": "2018-06-06T00:46:22Z",
      "lastModifiedTime": "2018-03-17T17:11:59Z"
    },


It consists of all details about the skill called ‘Capital’:

  1. Model (model)
  2. Group (group)
  3. Language (language)
  4. Developer Privacy Policy (developer_privacy_policy)
  5. Description (descriptions)
  6. Image (image)
  7. Author (author)
  8. Author URL (author_url)
  9. Skill name (skill_name)
  10. Terms of Use (terms_of_use)
  11. Content Type (dynamic_content)
  12. Examples (examples)
  13. Skill Rating (skill_rating)
  14. Creation Time (creationTime)
  15. Last Access Time (lastAccessTime)
  16. Last Modified Time (lastModifiedTime)

From among all this information, the information of interest for this blog is Skill Rating. This blog mainly deals with showing how to parse the JSON response to get the skill rating star values, so as to display the actual data in the skill rating graph.

A request to the getSkillList API is made for each group using the GET method.

@GET("/cms/getSkillList.json")
Call<ListSkillsResponse> fetchListSkills(@Query("group") String groups);

It returns a JSON response consisting of all the aforementioned information. Now, to parse the JSON response, do the following :

  1. Add a response for the response received as a result of API call. ListSkillsResponse contains two objects – group and skills.
    This blog is about getting the skill rating, so let us proceed with parsing the required response. The skills object contains the skill data that we need. Hence, next a SkillData class is created.

    class ListSkillsResponse {
       val group: String = "Knowledge"
       val skillMap: Map<String, SkillData> = HashMap()
    }
  2. Now, add the SkillData class. This class defines the response that we saw for ‘Capital’ skill above. It contains skill name, author, skill rating and so on.

    class SkillData : Serializable {
       var image: String = ""
       @SerializedName("author_url")
       @Expose
       var authorUrl: String = ""
       var examples: List<String> = ArrayList()
       @SerializedName("developer_privacy_policy")
       @Expose
       var developerPrivacyPolicy: String = ""
       var author: String = ""
       @SerializedName("skill_name")
       @Expose
       var skillName: String = ""
       @SerializedName("dynamic_content")
       @Expose
       var dynamicContent: Boolean? = null
       @SerializedName("terms_of_use")
       @Expose
       var termsOfUse: String = ""
       var descriptions: String = ""
       @SerializedName("skill_rating")
       @Expose
       var skillRating: SkillRating? = null
    }
    
  3. Now, add the SkillRating class. As what is required is the skill rating, narrowing down to the skill_rating object. The skill_rating object contains the actual rating for each skill i.e. the stars values. So, this files defines the response for the skill_rating object.

    class SkillRating : Serializable {
       var stars: Stars? = null
    }
    
  4. Further, add a Stars class. Ultimately, the values that are needed are the number of users who rated a skill at five stars, four stars and so on and also the total number of users and the average rating. Thus, this file contains the values inside the ‘stars’ object.

    class Stars : Serializable {
       @SerializedName("one_star")
       @Expose
       var oneStar: String? = null
       @SerializedName("two_star")
       @Expose
       var twoStar: String? = null
       @SerializedName("three_star")
       @Expose
       var threeStar: String? = null
       @SerializedName("four_star")
       @Expose
       var fourStar: String? = null
       @SerializedName("five_star")
       @Expose
       var fiveStar: String? = null
       @SerializedName("total_star")
       @Expose
       var totalStar: String? = null
       @SerializedName("avg_star")
       @Expose
       var averageStar: String? = null
    }
    

Now, the parsing is all done. It is time to use these values to plot the skill rating graph and complete the section displaying the five star skill rating.

To plot these values on the skill rating graph refer to the blog on plotting horizontal bar graph using MPAndroid Chart library. In step 5 of the linked blog, replace the second parameter to the BarEntry constructor by the actual values obtained by parsing.

Here is how we do it.

  • To get the total number of ratings
val  totalNumberofRatings: Int = skillData.skillRating?.stars?.totalStars

 

  • To get the average rating
val averageRating: Float = skillData.skillRating?.stars?.averageStars

 

  • To get number of users who rated the skill at five stars
val fiveStarUsers: Int = skillData.skillRating?.stars?.fiveStar

Similarly, get the number of users for fourStar, threeStar, twoStar and oneStar.

Note : If the totalNumberOfRatings equals to zero, then the skill is unrated. In this case, display a message informing the user that the skill is unrated instead of plotting the graph.

Now, as the graph shows the percentage of users who rated the skill at a particular number of stars, calculate the percentage of users corresponding to each rating, parse the result to Float and place it as the second parameter to the BarEntry constructor  as follows :

 entries.add(BarEntry(4f, (fiveStarUsers!!.toFloat() / totalUsers) * 100f)))

Similarly, replace the values for all five entries. Finally, add the total ratings and average rating section and display the detailed skill rating statistics for each skill, as in the following figure.

Resources

 

 

Continue Reading

Invalidating user login using JWT in Open Event Orga App

User authentication is an essential part of Open Event Orga App (Github Repo), which allows an organizer to log in and perform actions on the event he/she organizes. Backend for the application, Open Event Orga Server sends an authentication token on successful login, and all subsequent privileged API requests must include this token. The token is a JWT (Javascript Web Token) which includes certain information about the user, such as identifier and information about from when will the token be valid, when will it expire and a signature to verify if it was tampered.

Parsing the Token

Our job was to parse the token to find two fields:

  • Identifier of user
  • Expiry time of the token

We stored the token in our shared preference file and loaded it from there for any subsequent requests. But, the token expires after 24 hours and we needed our login model to clear it once it has expired and shown the login activity instead.

To do this, we needed to parse the JWT and compare the timestamp stored in the exp field with the current timestamp and determine if the token is expired. The first step in the process was to parse the token, which is essentially a Base 64 encoded JSON string with sections separated by periods. The sections are as follows:

  • Header ( Contains information about algorithm used to encode JWT, etc )
  • Payload ( The data in JWT – exp. Iar, nbf, identity, etc )
  • Signature ( Verification signature of JWT )

We were interested in payload and for getting the JSON string from the token, we could have used Android’s Base64 class to decode the token, but we wanted to unit test all the util functions and that is why we opted for a custom Base64 class for only decoding our token.

So, first we split the token by the period and decoded each part and stored it in a SparseArrayCompat

public static SparseArrayCompat<String> decode(String token) {
   SparseArrayCompat<String> decoded = new SparseArrayCompat<>(2);

   String[] split = token.split("\\.");
   decoded.append(0, getJson(split[0]));
   decoded.append(1, getJson(split[1]));

   return decoded;
}

 

The getJson function is primarily decoding the Base64 string

private static String getJson(String strEncoded) {
   byte[] decodedBytes = Base64Utils.decode(strEncoded);
   return new String(decodedBytes);
}

The decoded information was stored in this way

0={"alg":"HS256","typ":"JWT"},  1={"nbf":1495745400,"iat":1495745400,"exp":1495745800,"identity":344}

Extracting Information

Next, we create a function to get the expiry timestamp from the token. We could use GSON or Jackson for the task, but we did not want to map fields into any object. So we simply used JSONObject class which Android provides. It took 5 ms on average to parse the JSON instead of 150 ms by GSON

public static long getExpiry(String token) throws JSONException {
   SparseArrayCompat<String> decoded = decode(token);

   // We are using JSONObject instead of GSON as it takes about 5 ms instead of 150 ms taken by GSON
   return Long.parseLong(new JSONObject(decoded.get(1)).get("exp").toString());
}

 

Next, we wanted to get the ID of user from token to determine if a new user is logging in or an old one, so that we can clear the database for new user.

public static int getIdentity(String token) throws JSONException {
   SparseArrayCompat<String> decoded = decode(token);

   return Integer.parseInt(new JSONObject(decoded.get(1)).get("identity").toString());
}

Validating the token

After this, we needed to create a function that tells if a stored token is expired or not. With all the right functions in place, it was just a matter of comparing current time with the stored timestamp

public static boolean isExpired(String token) {
   long expiry;

   try {
       expiry = getExpiry(token);
   } catch (JSONException jse) {
       return true;
   }

   return System.currentTimeMillis()/1000 >= expiry;
}

 

Since the token provides timestamp from epoch in terms of seconds, we needed to divide the current time in milliseconds by 1000 and the function returned true if current timestamp was greater than the expiry time of token.

After writing a few unit tests for both functions, we just needed to plug them in our login model at the time of authentication.

At the time of starting of the application, we use this function to check if a user is logged in or not:

public boolean isLoggedIn() {
   String token = utilModel.getToken();

   return token != null && !JWTUtils.isExpired(token);
}

 

So, if there is no token or the token is expired, we do not automatically login the user and show the login screen.

Implementing login

The next task were

  • Sequest the server to login
  • Store the acquired token
  • Delete database if it is a new user

Before implementing the above logic, we needed to implement a function to determine if the person logging in is previous user, or new one. For doing so, we first loaded the saved user from our database, if the query is empty, surely it is a new user logging in. So we return false, and if there is a user in the database, we match its ID with the logged in user’s ID:

public Single<Boolean> isPreviousUser(String token) {
   return databaseRepository.getAllItems(User.class)
       .first(EMPTY)
       .map(user -> !user.equals(EMPTY) && user.getId() == JWTUtils.getIdentity(token));
}

 

We have added a default user EMPTY in the first operator so that RxJava returns it if there are no users in the database and then we simply map the user to a boolean denoting if they are same or different using the EMPTY user and getIdentity method from JWTUtils

Finally, we use all this information to implement our self contained login request:

eventService
   .login(new Login(username, password))
   .flatMapSingle(loginResponse -> {
       String token = loginResponse.getAccessToken();
       utilModel.saveToken(token);

       return isPreviousUser(token);
   })
   .flatMapCompletable(isPrevious -> {
       if (!isPrevious)
           return utilModel.deleteDatabase();

       return Completable.complete();
   });

 

Let’s see what is happening here. A request using username and password is made to the server which returns a login response containing a JWT, which we store for future use. Next, we flatMapSingle to the Single returned by the isPreviousUser method. And we finally clear the database if it is not a previous user.

Creating these self contained models help reduce complexity in presenter or view layer and all data is handled in one layer making presenter layer model agnostic.

To learn more about JWT and some of the Rx operators I mentioned here, please visit these links:

Continue Reading
Close Menu
%d bloggers like this: