Building the API of Speaker Image Size on Open Event Server

The Open Event Server enables organizers to manage events from concerts to conferences and meetups. It offers features for events with several tracks and venues.It uses the JSON 1.0 Specification and build on top of Flask Rest Json API (for building Rest APIs) and Marshmallow (for Schema). In this blog, we will talk about how to add API for accessing and updating the Speaker Image Size on Open Event Server. The focus is on its API creation.

API Creation

For the SpeakerImageSizeDetail, we’ll make our Schema as follows

Now, let’s try to understand SpeakerImageSizeDetail.

In this feature, we are providing Admin the rights to Get and Update the SpeakerImageSizes

  1. kwargs[‘id’] = 2 states that Image Size model has 2 records and 1st record is used for Event Image Size and 2nd record is used for Speaker Image Size.
  2. decorators = (api.has_permission(‘is_admin’, methods=”PATCH”, id=”2″),) states that for Speaker Image Size, Update API is accessible to Admins only.
  3. methods = [‘GET’, ‘PATCH’] states that this API provides two methods i.e. GET and PATCH.
  4. schema = SpeakerImageSizeSchema states that the schema which is used to return the response is Speaker Image Size Schema.
  5. data_layer = {‘session’: db.session, ‘model’: ImageSizes} states the session and Model used to fetch the records.

Resources

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Add More Languages to a Skill in SUSI.AI

The SUSI SKill CMS provides skills in multiple languages. Often there are similar skills in different languages. For example, there is a News skill in English and Samachar skill in Hindi. Then why not link them together and mark one as the translation of the other. This will help the user to reach and explore the desired skill in an efficient way. Moreover, it may be easier to type ‘News’ than ‘समाचार’ and find the required skill through translations. So here it has been explained how to link two SUSI skills as translations.

Server side implementation

Create a skillSupportedLanguages.json file to store the related skills together as translations and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the language name and the skill name in that language, wrapped in an array.

public static JsonTray skillSupportedLanguages;

Path skillSupportedLanguages_per = skill_status_dir.resolve("skillSupportedLanguages.json");
Path skillSupportedLanguages_vol = skill_status_dir.resolve("skillSupportedLanguages_session.json");
skillSupportedLanguages = new JsonTray(skillSupportedLanguages_per.toFile(), skillSupportedLanguages_vol.toFile(), 1000000);
OS.protectPath(skillSupportedLanguages_per);
OS.protectPath(skillSupportedLanguages_vol);

Now create an API that accepts the skill details and translation details and stores them in the JSON file. Create UpdateSupportedLanguages.java class for the API.

Endpoint: /cms/updateSupportedLanguages.json

Minimum user role: Anonymous

Params:

  • Model
  • Group
  • Language (language of the skill for which translation is to be added)
  • Skill (name of the skill for which translation is to be added)
  • New language (translation language of the skill)
  • New skill name (name of the skill in translated language)

When a new translation is added check if it already exists in the translation group stored in the skillSupportedLanguages.json. Use the DAO object and loop over the array, check is the language name and the language name already exists. If yes then simply return.

if (!alreadyExixts) {
    groupName.put(createSupportedLanguagesArray(language_name, skill_name, new_language_name, new_skill_name));
}

Otherwise, create a new object containing the new language name and the skill name in that language and add it to the translation group.

public JSONArray createSupportedLanguagesArray(String language_name, String skill_name, String new_language_name, String new_skill_name) {
    JSONArray supportedLanguages =  new JSONArray();

    JSONObject languageObject = new JSONObject();
    languageObject.put("language", language_name);
    languageObject.put("name", skill_name);
    supportedLanguages.put(languageObject);

    JSONObject newLanguageObject = new JSONObject();
    newLanguageObject.put("language", new_language_name);
    newLanguageObject.put("name", new_skill_name);
    supportedLanguages.put(newLanguageObject);

    return supportedLanguages;
}

Add this API to SusiServer.java

// Add translation to the skill
UpdateSupportedLanguages.class

Resources

 

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Implementing System Logs in SUSI.AI Admin Panel

The admin panel of SUSI.AI provides a lot of features required by system maintainers and admins to administer and maintain various activities of SUSI. Therefore system logs have been implemented on the admin panel so that admins can check whether SUSI server is working fine or throwing some errors. In this blog we will discuss about how logs are implemented on server and accounts.

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Deploying Susper On Heroku

In Susper, currently we have two branches, master and development branch. The master branch is deployed on susper.com and we needed a deployment of development branch. For this we are using heroku and in this blog I will describe how we have deployed Susper’s development branch on heroku at http://susper-dev.herokuapp.com

Here are the steps:

1.Get a free heroku account:

To deploy our apps on heroku we must have a heroku account which can be created for free on https://www.heroku.com.

2.Create a new app on heroku:

After creating an account we need to create an app with a name and a region, for Susper we have used name susper-dev and region as United States.

3.Link the app to Susper’s development branch:

After successfully creating the app we need to link our app to Susper’s development branch and in Deploy section and then we have to enable automatic deployment from development branch after CI passes.

  1. Setup the Susper Project:

Now we have deployed our and we need to configure it so that it can successfully start on heroku. Following are the steps to configure Susper.

a) Add node and npm engine in package.json:

Now we need to tell heroku which npm and node version to use while running our app,this can be done by defining an engine field in package.json and adding npm and node versions as values as shown here:

"engines": {
   "node": "8.2.1",
   "npm": "6.0.1"
 }

 

b) Adding a postinstall field under scripts field in package.json:

Now we need to tell heroku the command that we will be using to build our app. Since on susper.com we are using ng build –prod –build-optimizer that builds our app for production by optimizing the build artifacts. Therefore on heroku also we will be using the same command:

"postinstall": "ng build --prod --build-optimizer"

 

c) Defining dependencies and typescript version for our project:This can be done by defining the @angular/cli ,@angular/compiler-cli and typescript and their version under dependencies field in package.json. In Susper we are using the following dependency versions.

"@angular/cli": "^1.1.0",
"@angular/compiler": "4.1.3",
"typescript": "~2.3.4"

 

d) Creating a javascript file to install express server and run our app: Locally when we run ng serve angular cli automatically creates a express server where our app is deployed locally but on heroku we will be required to start our express server which will be used by our app using a javascript file. Here is the code for starting the server.

//Install the server
const express = require('express');
const path = require('path');
const app = express();
// Serving the static file from dist folder
app.use(express.static(__dirname + '/dist'));
app.get('/*', function(request,response) {
response.sendFile(path.join(__dirname+'/dist/index.html'));
});
// Starting the app and listening on default heroku port.
app.listen(process.env.PORT || 8080);

 

Now we need to run this file, for this we will change start command in package.json file under script to start this file.

"start": "node server.js"

 

Now everytime a new commit is made to development branch our app will be automatically deployed on heroku at https://susper-dev.herokuapp.com

Resources

 

 

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Multiple Languages Filter in SUSI.AI Skills CMS and Server

There are numerous users of SUSI.AI globally. Most of the users use skills in English languages while some prefer their native languages. Also,there are some users who want SUSI skills of multiple languages. So the process of fetching skills from multiple languages has been explained in this blog.

Server side implementation

The language parameter in ListSkillService.java is modified to accept a string that contains the required languages separated by a comma. Then this parameter is split by comma symbol which returns an array of the required languages.

String language_list = call.get("language", "en");
String[] language_names = language_list.split(",");

Then simple loop over this array language by language and keep adding the the skills’ metadata, in that language into the response object.

for (String language_name : language_names) {
	// fetch the skills in this language.
}

CMS side implementation

Convert the state variable languageValue, in BrowseSkill.js, from strings to an array so that multiple languages can be kept in it.

languageValue: ['en']

Change the language dropdown menu to allow selection of multiple values and attach an onChange listener to it. Its value is the same as that of state variable languageValue and its content is filled by calling a function languageMenuItems().

<SelectField
    multiple={true}
    hintText="Languages"
    value={languageValue}
    onChange={this.handleLanguageChange}
  >
    {this.languageMenuItems(languageValue)}
</SelectField>

The languageMenuItems() function gets the list of checked languages as a parameter. The whole list of languages are stored in a global variable called languages. So this function loops over the list of all the languages and check / uncheck them based on the values passed in the argument. It build a menu item for each language and put the ISO6391 native name of that language into the menu item.

languageMenuItems(values) {
  return languages.map(name => (
    <MenuItem
      insetChildren={true}
      checked={values && values.indexOf(name) > -1}
      value={name}
      primaryText={
        ISO6391.getNativeName(name)
          ? ISO6391.getNativeName(name)
          : 'Universal'
      }
    />
  ));
}

While the language change handler gets the values of the selected languages in the form of an array, from the drop down menu. It simply assigns this value to the state variable languageValue and calls the loadCards() function to load the skills based on the new filter.

this.setState({ languageValue: values }, function() {
    this.loadCards();
  });

 References

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Testing Endpoints on Local Server

All servlets in SUSI.AI have a BaseUserRole defined. It represents the access level you need to access the endpoint corresponding to that servlet. The lowermost BaseUserRole a SUSI.AI servlet can have is ANONYMOUS, which means that anyone can access the endpoint corresponding to these endpoints. But if the BaseUserRole is higher than that, then you need an access token to access the endpoint. This blog post explains how you can get access token to access the endpoints on a local server.

What are endpoints in an API?

An endpoint in API is one end of a communication channel. When an API interacts with another system, the touchpoints of this communication are considered endpoints. For APIs, an endpoint can include a URL of a server or service. Each endpoint is the location from which APIs can access the resources they need to carry out their function.

APIs work using ‘requests’ and ‘responses.’ When an API requests information from a web application or web server, it will receive a response. The place that APIs send requests and where the resource lives, is called an endpoint.

For example, the endpoint for https://api.susi.ai/cms/getSkillRating.json?queryParameters would be /cms/getSkillRating.json.

Servlets and Endpoints in SUSI.AI

All servlets in our SUSI project define an endpoint and also define a BaseUserRole, that is, the amount of privileges required to access the information on those endpoints. If the BaseUserRole defined is ANONYMOUS, then anyone can access the endpoint directly. But if the BaseUserRole is anything higher than that, then we would need an access token to access that.

How to get Access Token?

If you’re trying to access the endpoints with BaseUserRole higher than ANONYMOUS on the actual hosted server, then you can simply login to https://chat.susi.ai and get the access token from the Network tab of the Developers Tool. We can then use that token and pass that as a query parameter along with the other parameters of that particular endpoint. For example,

http://localhost:4000/aaa/listUserSettings.json?access_token=6O7cqoMbzlClxPwg1is31Tz5pjVwo3

 

But, the problem arises when you are trying to access such endpoints on local server. The local User data is completely different from the server User data. Hence, we need to generate an access token in localhost itself.

To generate access token for local server, we need to follow these steps :

  1. First, we need to hit the /aaa/signup.json endpoint with a new account credentials which we want to register for the localhost session. This is done as shown in below example:

http://localhost:4000/aaa/signup.json?signup=anyEmail&password=anyPassword

 

  1. Then, we need to hit the /aaa/login.json endpoint with the same credentials you registered in the previous step. This is done as shown in below example:

http://localhost:4000/aaa/login.json?login=yourEmail&type=access-token&password=yourPassword

 

If you’ve entered the registered credentials correctly, then the output of the /aaa/login.json endpoint would be a JSON as shown below:

{
  "accepted": true,
  "valid_seconds": 604800,
  "access_token": "7JPi7zNwemg1YYnr4d9JIdZMaIWizV",
  "message": "You are logged in as anyemail",
  "session": {"identity": {
    "type": "host",
    "name": "127.0.0.1_4e75edbb",
    "anonymous": true
  }}
}

 

As it can be seen from the above JSON response, we get the access token which we needed. Hence, copy this access token and store it somewhere because you can now use this access token to access the endpoints with BaseUserRole as User for this localhost session.

Note that you’ll have to follow all the above steps again if you start a fresh localhost session.

Resources

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How api.susi.ai responds to a query and send response

A direct way to access raw data for a query is https://api.susi.ai. It is an API of susi server which sends responses to a query by a user in form of a JSON (JavaScript Object Notation) object (more on JSON here). This JSON object is the raw form of any response to a query which is a bunch of key (attribute) value pair. This data is then send from the server to various APIs like chat.susi.ai, susi bots, android and ios apps of SUSI.AI.

Whenever a user in using an API, for example chat.susi.ai, the user send a query to the API. This query is then sent by the API as a request to the susi server. The server then process the request and sends the answer to the query in form of json data as a response to the request. This request is then used by the API to display the answer after applying stylings to the json data.

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Populating Database for different Event Types and Event Topics on Open Event Server

The Open Event Server enables organizers to manage events from concerts to conferences and meetups. It offers features for events with several tracks and venues. It uses the JSON 1.0 Specification and build on top of Flask Rest Json API (for building Rest APIs) and Marshmallow (for Schema). In this blog, we will talk about how to add API for accessing and updating the Speaker Image Size on Open Event Server. The focus is on its API creation.

In this blog, we will talk about how to populate database for different event types and event topics in the Open Event Server.

Populating the Database

Using populate_db,py for populating the database.

Now, let’s try to understand this now.

  1. First of all, we will write two functions create_event_topics and create_event_types in populate_db.py .
  2. In these function we will make a list of all the event topics and event types which we want to populate in database.
  3. We will loop through these lists to create their objects if not present.
  4. Last step is to call these functions in populate and populate_without_print functions in populate_db.py itself.

Resources

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Skill Ratings Over Time

The SUSI SKill CMS provides an option to rate and review a skill. These feedbacks help the skill creators to improve the skills. Also, the ratings and reviews can be updated by the reviewer. But the CMS only provides the current rating of a skill. What if a user or a developer wants to see how that skill has performed over time? Are there any improvements in the skill or not?

For that, we need the skill ratings over time !

Server side implementation

Create a ratingsOverTime.json file to store the monthly average rating of the skills and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the timestamp for every month, the average ratings on a skill in that month and the total number of ratings in that month.

public static JsonTray ratingsOverTime;

Path ratingsOverTime_per = susi_skill_rating_dir.resolve("ratingsOverTime.json");
Path ratingsOverTime_vol = susi_skill_rating_dir.resolve("ratingsOverTime_session.json");
ratingsOverTime = new JsonTray(ratingsOverTime_per.toFile(), ratingsOverTime_vol.toFile(), 1000000);
OS.protectPath(ratingsOverTime_per);
OS.protectPath(ratingsOverTime_vol);

Now whenever a user rates a skill, the data in ratingsOverTime.json needs to be updated. For this fetch the overall rating data of the current month. Multiply the average rating with the total number of ratings (count) of that month.

sum = average_rating X number_of_ratings

Then add the rating given by the current user to this sum and divide by count + 1 to again get the new average rating. Also increment the total number of ratings by 1.

new_sum = sum + rating_by_user

new_avg = new_sum/(count+1)

number_of_ratings =  number_of_ratings + 1

float totalRating = skillRating * ratingCount;
float newAvgRating = (totalRating + skill_stars)/(ratingCount + 1);
ratingObject.put("rating", newAvgRating);
ratingObject.put("count", ratingCount + 1);

Now we have got the ratings over time stored in ratingsOverTime.json file. An API to access this data is also required. So create an API GetRatingOverTime.java returns the ratings over time of a particular skill. The API has the following attributes :

Endpoint : /cms/getRatingsOverTime.json

Minimum user role : anonymous

Parameters : model, group, language and skill

JSONArray skillRatings = languageName.getJSONArray(skill_name);
result.put("skill_name", skill_name);
result.put("ratings_over_time", skillRatings);
return new ServiceResponse(result);

It fetches the data corresponding to the skill from ratingsOverTime.json and returns it to the CMS.

Add this API to SusiServer.java

//Skill ratings over time
GetRatingsOverTime.class

References

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Creating Feedback Logs for Analysis

The thumbs up and thumbs down feedback on the clients is meant for the improvement of the skills in SUSI.AI. So we need to scope the feedback system to a particular interaction rather than skill as a whole. The feedback logs can be used for various kinds of analysis and machine learning.

Server side implementation

Components of Feedback Log:

  • User ID – For identification of a feedback given by a particular user. For consistency in data, the user should not be able to change the feedback over the same interaction.
  • Interaction:
    • User query
    • SUSI Reply
  • Client location – The response of a skill may not be interesting for the users of a particular country. That means the skill should give localised results.
  • Skill path – The path on the server where the skill is stored.

Create a feedbackLogs.json file to store the logs of feedback given by the user and make a JSONTray object for that in src/ai/susi/DAO.java file. The JSON file contains the above mentioned components.

public static JsonTray feedbackLogs; 

Path feedbackLogs_per = susi_skill_rating_dir.resolve("feedbackLogs.json");
Path feedbackLogs_vol = susi_skill_rating_dir.resolve("feedbackLogs_session.json");
feedbackLogs = new JsonTray(feedbackLogs_per.toFile(), feedbackLogs_vol.toFile(), 1000000);
OS.protectPath(feedbackLogs_per);
OS.protectPath(feedbackLogs_vol);

Create FeedbackLogService.java file that acts as an API to create the feedback logs. The API accepts the feedback data from the client and stores it into the json file using DAO object. The user should be logged in to give feedback on an interaction. So keep the minimum user role as USER to access the API.

JSONObject feedbackLogObject = new JSONObject();
feedbackLogObject.put("timestamp", timestamp);
feedbackLogObject.put("uuid", idvalue);
feedbackLogObject.put("feedback", skill_rate);
feedbackLogObject.put("user_query", user_query);
feedbackLogObject.put("susi_reply", susi_reply);
feedbackLogObject.put("country_name", country_name);
feedbackLogObject.put("country_code", country_code);
feedbackLogObject.put("skill_path", skill_path);

The API is accessible at /cms/feedbackLog.json endpoint.

Send feedback log from Web Client

The feedback API should be called only if the user is logged in. When the user presses the feedback buttons fetch the required data for log (access token, user query, susi response, country and user feedback) and POST them on the feedbackLog.json API.

let rateEndPoint =   BASE_URL + '/cms/feedbackLog.json?model=' + skill.model + '&group=' + skill.group + '&language=' + skill.language + '&skill=' + skill.skill + '&rating=' + rating + '&access_token=' + accessToken + '&user_query=' + interaction.userQuery + '&susi_reply=' + interaction.susiReply + '&country_name=' + country.countryName + '&country_code=' + country.countryCode ;

$.ajax({
  url: rateEndPoint,
  success: function(response) {
      console.log('Skill rated successfully');
  }
})

References

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