Implementing Tweet Search Suggestions in Loklak Wok Android

Loklak Wok Android not only is a peer harvester for Loklak Server but it also provides users to search tweets using Loklak’s API endpoints. To provide a better search tweet search experience to the users, the app provides search suggestions using suggest API endpoint. The blog describes how “Search Suggestions” is implemented.

Third Party Libraries used to Implement Suggestion Feature

  • Retrofit2: Used for sending network request
  • Gson: Used for serialization, JSON to POJOs (Plain old java objects).
  • RxJava and RxAndroid: Used to implement a clean asynchronous workflow.
  • Retrolambda: Provides support for lambdas in Android.

These libraries can be installed by adding the following dependencies in app/build.gradle

android {
   .
   // removes rxjava file repetations
   packagingOptions {
      exclude 'META-INF/rxjava.properties'
   }
}

dependencies {
   // gson and retrofit2
    compile 'com.google.code.gson:gson:2.8.1'
    compile 'com.squareup.retrofit2:retrofit:2.3.0'
    compile 'com.squareup.retrofit2:converter-gson:2.3.0'
    compile 'com.squareup.retrofit2:adapter-rxjava2:2.3.0'

   // rxjava and rxandroid
    compile 'io.reactivex.rxjava2:rxjava:2.0.5'
    compile 'io.reactivex.rxjava2:rxandroid:2.0.1'
    compile 'com.jakewharton.rxbinding2:rxbinding:2.0.0'
}

 

To add retrolambda

// in project's build.gradle
dependencies {
    
    classpath 'me.tatarka:gradle-retrolambda:3.2.0'
}

// in app level build.gradle at the top
apply plugin: 'me.tatarka.retrolambda'

 

Fetching Suggestions

Retrofit2 sends a GET request to search API endpoint, the JSON response returned is serialized to Java Objects using the models defined in models.suggest package. The models can be easily generated using JSONSchema2Pojo. The benefit of using Gson is that, the hard work of parsing JSON is easily handled by it. The static method createRestClient creates the retrofit instance to be used for network calls

private static void createRestClient() {
   sRetrofit = new Retrofit.Builder()
           .baseUrl(BASE_URL) // base url : https://api.loklak.org/api/
           .addConverterFactory(GsonConverterFactory.create(gson))
           .addCallAdapterFactory(RxJava2CallAdapterFactory.create())
           .build();
}

 

The suggest endpoint is defined in LoklakApi interface

public interface LoklakApi {

   @GET("/api/suggest.json")
   Observable<SuggestData> getSuggestions(@Query("q") String query);

   @GET("/api/suggest.json")
   Observable<SuggestData> getSuggestions(@Query("q") String query, @Query("count") int count);

   .
}

 

Now, the suggestions are obtained using fetchSuggestion method. First, it creates the rest client to send network requests using createApi method (which internally calls creteRestClient implemented above). The suggestion query is obtained from the EditText. Then the RxJava Observable is subscribed in a separate thread which is specially meant for doing IO operations and finally the obtained data is observed i.e. views are inflated in the MainUI thread.

private void fetchSuggestion() {
   LoklakApi loklakApi = RestClient.createApi(LoklakApi.class); // rest client created
   String query = tweetSearchEditText.getText().toString(); // suggestion query from EditText
   Observable<SuggestData> suggestionObservable = loklakApi.getSuggestions(query); // observable created
   Disposable disposable = suggestionObservable
           .subscribeOn(Schedulers.io()) // subscribed on IO thread
           .observeOn(AndroidSchedulers.mainThread()) // observed on MainUI thread
           .subscribe(this::onSuccessfulRequest, this::onFailedRequest); // views are manipulated accordingly
   mCompositeDisposable.add(disposable);
}

 

If the network request is successful onSuccessfulRequest method is called which updates the data in the RecyclerView.

private void onSuccessfulRequest(SuggestData suggestData) {
   if (suggestData != null) {
       mSuggestAdapter.setQueries(suggestData.getQueries()); // data updated.
   }
   setAfterRefreshingState();
}

 

If the network request fails then onFailedRequest is called which displays a toast saying “Cannot fetch suggestions, Try Again!”. If requests are sent simultaneously and they fail, the previous message i.e. the previous toast is removed.

private void onFailedRequest(Throwable throwable) {
   Log.e(LOG_TAG, throwable.toString());
   if (mToast != null) { // checks if a previous toast is present
       mToast.cancel(); // removes the previous toast.
   }
   setAfterRefreshingState();
   // value of networkRequestError: "Cannot fetch suggestions, Try Again!"
   mToast = Toast.makeText(getActivity(), networkRequestError, Toast.LENGTH_SHORT); // toast is crated
   mToast.show(); // toast is displayed
}

 

Lively Updating suggestions

One way to update suggestions as the user types in, is to send a GET request with a query parameter to suggest API endpoint and check if a previous request is incomplete cancel it. This includes a lot of IO work and seems unnecessary because we would be sending request even if the user hasn’t completed typing what he/she wants to search. One probable solution is to use a Timer.

private TextWatcher searchTextWatcher = new TextWatcher() {  
    @Override
    public void afterTextChanged(Editable arg0) {
        // user typed: start the timer
        timer = new Timer();
        timer.schedule(new TimerTask() {
            @Override
            public void run() {
                String query = editText.getText()
                // send network request to get suggestions
            }
        }, 600); // 600ms delay before the timer executes the „run" method from TimerTask
    }

    @Override
    public void beforeTextChanged(CharSequence s, int start, int count, int after) {}

    @Override
    public void onTextChanged(CharSequence s, int start, int before, int count) {
        // user is typing: reset already started timer (if existing)
        if (timer != null) {
            timer.cancel();
        }
    }
};

 

This one looks good and eventually gets the job done. But, don’t you think for a simple stuff like this we are writing too much of code that is scary at the first sight.

Let’s try a second approach by using RxBinding for Android views and RxJava operators. RxBinding simply converts every event on the view to an Observable which can be subscribed and worked upon.

In place of TextWatcher here we use RxTextView.textChanges method that takes an EditText as a parameter. textChanges creates Observable of character sequences for text changes on view, here EditText. Then debounce operator of RxJava is used which drops observables till the timeout expires, a clean alternative for Timer. The second approach is implemented in updateSuggestions.

private void updateSuggestions() {
   Disposable disposable = RxTextView.textChanges(tweetSearchEditText) // generating observables for text changes
           .debounce(400, TimeUnit.MILLISECONDS) // droping observables i.e. text changes within 4 ms
           .subscribe(charSequence -> {
               if (charSequence.length() > 0) {
                   fetchSuggestion(); // suggestions obtained
               }
           });
   mCompositeDisposable.add(disposable);
}

 

CompositeDisposable is a bucket that contains Disposable objects, which are returned each time an Observable is subscribed to. So, all the disposable objects are collected in CompositeDisposable and unsubscribed when onStop of the fragment is called to avoid memory leaks.

A SwipeRefreshLayout is used, so that user can retry if the request fails or refresh to get new suggestions. When refreshing is not complete a circular ProgressDialog is shown and the RecyclerView showing old suggestions is made invisible, executed by setBeforeRefreshingState method

private void setBeforeRefreshingState() {
   refreshSuggestions.setRefreshing(true);
   tweetSearchSuggestions.setVisibility(View.INVISIBLE);
}

 

Similarly, once refreshing is done ProgessDialog is stopped and the visibility of RecyclerView which now contains the updated suggestions is changed to VISIBLE. This is executed in setAfterRefreshingState method

private void setAfterRefreshingState() {
   refreshSuggestions.setRefreshing(false);
   tweetSearchSuggestions.setVisibility(View.VISIBLE);
}

 

References:

Implementing JSON API for ‘settings/contact-info’ route in Open Event Frontend

In Open Event Frontend, under the settings route, there is a ‘contact-info’ route which allows the user to change his info (email and contact). Previously to achieve this we were using the mock response from the server. But since we have the JSON API now we could integrate and use the JSON API for it so as to let the user modify his/her email and contact info. In the following section, I will explain how it is built:

The first thing to do is to create the model for the user so as to have a skeleton of the database and include our required fields in it. The user model looks like:

export default ModelBase.extend({
  email        : attr('string'),
  password     : attr('string'),
  isVerified   : attr('boolean', { readOnly: true }),
  isSuperAdmin : attr('boolean', { readOnly: true }),
  isAdmin      : attr('boolean', { readOnly: true }),

  firstName : attr('string'),
  lastName  : attr('string'),
  details   : attr('string'),
  contact   : attr('string'),
});

Above is the user’s model, however just the related fields are included here(there are more fields in user’s model). The above code shows that email and contact are two attributes which will accept ‘string’ values as inputs. We have a contact-info form located at ‘settings/contact-info’ route. It has two input fields as follows:

<form class="ui form {{if isLoading 'loading'}}" {{action 'submit' on='submit'}} novalidate>
  <div class="field">
    <label>{{t 'Email'}}</label>
    {{input type='email' name='email' value=data.email}}
  </div>
  <div class="field">
    <label>{{t 'Phone'}}</label>
    {{input type='text' name='phone' value=data.contact}}
  </div>
  <button class="ui teal button" type="submit">{{t 'Save'}}</button>
</form>

The form has a submit button which triggers the submit action. We redirect the submit action from the component to the controller so as to maintain ‘Data down, actions up’. The code is irrelevant, hence not shown here. Following is the action which is used to update the user which we are handling in the contact-info.js controller.

updateContactInfo() {
      this.set('isLoading', true);
      let currentUser = this.get('model');
      currentUser.save({
        adapterOptions: {
          updateMode: 'contact-info'
        }
      })
        .then(user => {
          this.set('isLoading', false);
          let userData = user.serialize(false).data.attributes;
          userData.id = user.get('id');
          this.get('authManager').set('currentUserModel', user);
          this.get('session').set('data.currentUserFallback', userData);
          this.get('notify', 'Updated information successfully');
        })
        .catch(() => {
        });
    }

We are returning the current user’s model from the route’s model method and storing it into ‘currentUser’ variable. Since we have data binding ember inputs in our contact-info form, the values will be grabbed automatically once the form submits. Thus, we can call ‘save’ method on the ‘currentUser’ model and pass an object called ‘adapterOptions’ which has key ‘updateMode’. We send this key to the ‘user’ serializer so that it picks only the attributes to be updated and omits the other ones. We have customized our user serializer as:

if (snapshot.adapterOptions && snapshot.adapterOptions.updateMode === 'contact-info') {
        json.data.attributes = pick(json.data.attributes, ['email', 'contact']);
}

The ‘save’ method on the ‘currentUser’ ‘currentUser.save()’ returns a promise. We resolve the promise by setting the ‘currentUserModel’ as the updated ‘user’ as returned by the promise. Thus, we are able to update the email and contact-info using the JSON API.

Resources:

Ember data official guide

Blog on models and Ember data by Embedly

How RSS Action Type is Implemented in SUSI Android

Important skills of SUSI.AI are to display web search queries, a map of any location and provide a list of relevant information of a topic. RSS action type is similar to websearch action type but when the web search is to be performed on the client side, it is denoted by websearch action type and when the web search is performed by the server itself, it is denoted by rss action type. In this blog, I will show you how rss action type is implemented in SUSI Android.

In case of RSS action type server searches the internet and using RSS feeds, returns an array of objects containing :

  • Title
  • Description
  • Link
{
  “title”: “dog-doh: Definitions Index”,
  “description”: “dog-doh: Definitions Index. dog dog and pony show dog biscuit dog collar dog days …”,
  “link”: “http://websters.yourdictionary.com/index/dog-doh/”,
}

title: Title related to user query

description: Description of user query.

link: If user want to know more information then user can use link to find more information.

How rss action type is parsed in SUSI Android

SUSI  reply in json format. It should be parsed properly to show it in android app. We used retrofit library developed by square to parse json data. Retrofit library parse data according to model class. We code model class according to expected json reply. For example, each susi response contains answer jsonarray. There are two jsonarray data and action inside answer jsonarray. We made a different model class for each jsonarray.

First model class is SusiResponse. We used this model class to parse ‘answers’ jsonarray.

@SerializedName(“answers”)
private List<Answer> answers = new ArrayList<>();

Here we used List<>  because ‘answers’ jsonarray contain a list of jsonarray and jsonobject. Answer is second model class. We used it to parse two important jsonarray ‘data’ and ‘action’. The ‘action’ attribute has information about action type.

public class Answer {
[email protected](“data”)
   private RealmList<Datum> data = new RealmList<>();
}

Here also we used the list because data jsonarray also contains a list of jsonobject but instead of simple list we used RealmList<> because after parsing we save data using realm. ‘data’ jsonarray contain multiple jsonobject and each jsonobject contain three important information ‘title’, ‘description’ and ‘link’.

Datum class is the main model class which is used to save and retrieve ‘title’, ‘description’ and ‘link’. setTitle, setLink and setDescription method of Datum class are used to save ‘title’, ‘description’ and ‘link’ and getTitle, getDescription and getLink method are use to retrieve ‘title’, ‘description’ and ‘link’.

How rss action type data is retrieved and saved

As already mentioned we used retrofit to retrieve data and realm to save data. susiResponse is response we received from SUSI server. We used susiResponse to retrieve a list of Datum class type data.

List<Datum> datumList = susiResponse.getAnswers().get(0).getData();

We then loop through datumList and from each element we extract ‘title’, ‘description’ and ‘link’ using getTitle(), getDescription() and getLink() method respectively. Datum class is model class for both retrofit and realm. realmDatum is instance of Datum class and datumRealmList is an instance of RealmList of Datum class type. After extracting data we save data using setTitle(), setDescription() and setLink().

for (Datum datum : datumList) {
          Datum realmDatum = bgRealm.createObject(Datum.class);
          realmDatum.setDescription(datum.getDescription());
          realmDatum.setLink(datum.getLink());
          realmDatum.setTitle(datum.getTitle());
         datumRealmList.add(realmDatum);
       }      

Layout design to show rss action type reply

There are three textview with id ‘title’, ‘description’ and ‘link’ to show ‘title’, ‘description’ and ‘link’ retrieved from SUSI’s reply. We used recyclerview to show list of results.

Datum datum = datumList.get(position);

holder.titleTextView.setText(Html.fromHtml(datum.getTitle()));

holder.descriptionTextView.setText(Html.fromHtml(datum.getDescription()));

holder.descriptionTextView.setText(Html.fromHtml(datum.getDescription()));

Resources

How Anonymous Mode is Implemented in SUSI Android

Login and signup are an important feature for some android apps like chat app because user wants to save messages and secure messages from others. In SUSI Android we save messages for logged in user on the server corresponding to their account. But  users can also  use the app without logging in. In this blog, I will show you how the anonymous mode is implemented in SUSI Android .

When the user logs in using the username and password we provide a token to user for a limited amount of time, but in case of anonymous mode we never provide a token to the user and also we set ANONYMOUS_LOGGED_IN flag true which shows that the user is using the app anonymously.

PrefManager.clearToken()

PrefManager.putBoolean(Constant.ANONYMOUS_LOGGED_IN, true)

We use ANONYMOUS_LOGGED_IN flag to check user is using the app anonymously or not. When a user opens the app we first check user is already logged in or not. If the user is not logged in then we check ANONYMOUS_LOGGED_IN flag is true or false. The true means user is using the app in anonymous mode.

if(PrefManager.getBoolean(Constant.ANONYMOUS_LOGGED_IN, false)) {

 intent = new Intent(LoginActivity.this, MainActivity.class);

startActivity(intent);

}

Else we show login page to the user. The user can use app either by login using username and password or anonymously by clicking skip button. On clicking skip button ANONYMOUS_LOGGED_IN flag set to true.

public void skip() {

Intent intent = new Intent(LoginActivity.this,MainActivity.class);

PrefManager.clearToken();

PrefManager.putBoolean(Constant.ANONYMOUS_LOGGED_IN, true);

startActivity(intent);

}

If the user is using the app in anonymous mode but he/she want to login then he/she can login. There is an option for login in menu.

When the user selects login option from the menu, then it redirects the user to the login screen and ANONYMOUS_LOGGED_IN flag is set to false. ANONYMOUS_LOGGED_IN flag is set to false to ensure that instead of login if the user closes the app and again open it, then he/she can’t use the app until logged in or click skip button.

case R.id.action_login:

PrefManager.putBoolean(Constant.ANONYMOUS_LOGGED_IN, false);

Reference

Using NodeJS modules of Loklak Scraper in Android

Loklak Scraper JS implements scrapers of social media websites so that they can be used in other platforms, like Android or in a native Java project. This way there will be only a single source of scraper, as a result it will be easier to update the scrapers in response to the change in websites. This blog explains how Loklak Wok Android, a peer for Loklak Server on Android platform uses the Twitter JS scraper to scrape tweets.

LiquidCore is a library available for android that can be used to run standard NodeJS modules. But Twitter scraper can’t be used directly, due to the following problems:

  • 3rd party NodeJS libraries are used to implement the scraper, like cheerio and request-promise-native and LiquidCore doesn’t support 3rd party libraries.
  • The scrapers are written in ES6, as of now LiquidCore uses NodeJS 6.10.2, which doesn’t support ES6 completely.

So, if 3rd party NodeJS libraries can be included in our scraper code and ES6 can be converted to ES5, LiquidCore can easily execute Twitter scraper.

3rd party NodeJS libraries can be bundled into Twitter scraper using Webpack and ES6 can be transpiled to ES5 using Babel.

The required dependencies can be installed using:

$npm install --save-dev webpack
$npm install --save-dev babel-core babel-loader babel-preset-es2015

Bundling and Transpiling

Webpack does bundling based on the configurations provided in webpack.config.js, present in root directory of project.

var fs = require('fs');

function listScrapers() {
   var src = "./scrapers/"
   var files = {};
   fs.readdirSync(src).forEach(function(data) {
       var entryName = data.substr(0, data.indexOf("."));
       files[entryName] = src+data;
   });
   return files;
}

module.exports = {
 entry: listScrapers(),
 target: "node",
 module: {
     loaders: [
         {
             loader: "babel-loader",
             test: /\.js?$/,
             query: {
                 presets: ["es2015"],
             }
         },
     ]
 },
 output: {
   path: __dirname + '/build',
   filename: '[name].js',
   libraryTarget: 'var',
   library: '[name]',
 }
};

 

Now let’s break the config file, the function listScrapers returns a JSONObject with key as name of scraper and value as relative location of scraper, ex:

{
   twitter: "./scrapers/twitter.js",
   github: "./scrapers/github.js"
   // same goes for other scrapers
}

The parameters in module.exports as described in the documentation of webpack for multiple inputs and to use the generated output externally:

  • entry: Since a bundle file is required for each scraper we provide the  the JSONObject returned by listScrapers function. The multiple entry points provided generate multiple bundled files.
  • target: As the bundled files are to be used in NodeJS platform,  “node” is set here.
  • module: Using webpack the code can be directly transpiled while bundling, the end users don’t need to run separate commands for transpiling. module contains babel configurations for transpiling.
  • output: options here customize the compilation of webpack
    • path: Location where bundled files are kept after compilation, “__dirname” means the current directory i.e. root directory of the project.
    • filename: Name of bundled file, “[name]“ here refers to the key of JSONObject provided in entry i.e. key of JSONObect returned from listScrapers. Example for Twitter scraper, the filename of bundled file will be “twitter.js”.
    • libraryTarget: by default the functions or methods inside bundled files can’t be used externally – can’t be imported. By providing the “var” the functions in bundled module can be accessed.
    • library: the name of the library.

Now, time to do the compilation work:

$ ./node_modules/.bin/webpack

The bundled files can be found in build directory. But, the generated bundled files are large files – around 77,000 lines. Large files are not encouraged for production purposes. So, a “-p” flag is used to generate bundled files for production – around 400 lines.

$ ./node_modules/.bin/webpack -p

Using LiquidCore to execute bundled files

The generated bundled file can be copied to the raw directory in res (resources directory in Android). Now, events are emitted from Activity/Fragment and in response to those events the scraping function is invoked in the bundled JS file, present in raw directory, the vice-versa is also possible.

So, we handle some events in our JS file and send some events to the android Activity/Fragment. The event handling and event creating code in JS file:

var query = "";
LiquidCore.on("queryEvent", function(msg) {
  query = msg.query;
});

LiquidCore.on("fetchTweets", function() {
  var twitterScraper = new twitter();
  twitterScraper.getTweets(query, function(data) {
    LiquidCore.emit("getTweets", {"query": query, "statuses": data});
  });
});

LiquidCore.emit('start');

 

First a “start” event is emitted from JS file, which is consumed in TweetHarvestingFragment by getScrapedTweet method using startEventListener.

EventListener startEventListener = (service, event, payload) -> {
   JSONObject jsonObject = new JSONObject();
   try {
       jsonObject.put("query", query);
       service.emit(LC_QUERY_EVENT, jsonObject); // value of LC_QUERY_EMIT is  "queryEvent"
   } catch (JSONException e) {
       Log.e(LOG_TAG, e.toString());
   }
   service.emit(LC_FETCH_TWEETS_EVENT); //value of  LC_FETCH_TWEETS_EVENT is  "fetchTweets"
};

 

The startEventListener then emits “queryEvent” with a JSONObject that contains the query to search tweets for scraping. This event is consumed in JS file by:

var query = "";
LiquidCore.on("queryEvent", function(msg) {
  query = msg.query;
});

 

After “queryEvent”, “fetchTweets” event is emitted from fragment, which is handled in JS file by:

LiquidCore.on("fetchTweets", function() {
  var twitterScraper = new twitter(); // scraping object is created
  twitterScraper.getTweets(query, function(data) { // function that scrapes twitter
    LiquidCore.emit("getTweets", {"query": query, "statuses": data});
  });
});

 

Once the scraped data is obtained, it is sent back to fragment by emitting “getTweets” event from JS file, “{“query”: query, “statuses”: data}” contains scraped data. This event is consumed in android by getTweetsEventListener.

EventListener getTweetsEventListener = (service, event, payload) -> { // payload contains scraped data
   Push push = mGson.fromJson(payload.toString(), Push.class);
   emitter.onNext(push);
};

 

LiquidCore creates a NodeJS instance to execute the bundled JS file. The NodeJS instance is called MicroService in LiquidCore terminology. For all this event handling to work, the NodeJS instance is created inside the method with a ServiceStartListner where all EventListener are added.

MicroService.ServiceStartListener serviceStartListener = (service -> {
   service.addEventListener(LC_START_EVENT, startEventListener);
   service.addEventListener(LC_GET_TWEETS_EVENT, getTweetsEventListener);
});
URI uri = URI.create("android.resource://org.loklak.android.wok/raw/twitter"); // Note .js is not used
MicroService microService = new MicroService(getActivity(), uri, serviceStartListener);
microService.start();

Resources

Skill History Component in Susi Skill CMS

SUSI Skill CMS is an editor to write and edit skill easily. It is built on ReactJS framework and follows an API centric approach where the Susi server acts as API server. Using Skill CMS we can browse history of a skill, where we get commit ID, commit message and name the author who made the changes to that skills. In this blog post, we will see how to add skill revision history component in Susi Skill CMS.

One text file represents one skill, it may contain several intents which all belong together. Susi skills are stored in susi_skill_data repository. We can access any skill based on four tuples parameters model, group, language, skill.

<Menu.Item key="BrowseRevision">
 <Icon type="fork" />
   Browse Skills Revision
 <Link to="/browseHistory"></Link>
</Menu.Item>

First let’s create a option in sidebar menu, and link it “/browseHistory” route created at index.js 

 <Route path="/browseHistory" component={BrowseHistory} />

Next we will be adding skill versioning using endpoints provided by Susi Server, to select a skill we will create a drop down list, for this we will be using Select Field a component of  Material UI.

request('http://cors-anywhere.herokuapp.com/api.susi.ai/cms/getModel.json').then((data) => {
    console.log(data.data);
    data = data.data;
    for (let i = 0; i < data.length; i++) {
        models.push(<MenuItem value={i} key={data[i]} primaryText={`${data[i]}`}/>);
    }

    console.log(models);
});
 <SelectField
   floatingLabelText="Expert"
   style={{width: '50px'}}
   value={this.state.value}
   onChange={this.handleChange}>
      {experts}
  </SelectField>

We store the models, groups and languages in array using the endpoints api.susi.ai/cms/getModel.json, api.susi.ai/cms/getGroups.json, api.susi.ai/cms/getAllLanguages.json set the values in respective select fields. The request functions takes the url as string and the parses the json and fetches the object containing data or error depending on the response from the server. Once run your project using

npm start

And you would be able to see the drop down list working

Next, we will use Material UI tables for displaying the organized data. For using  table component we need to import table, it’s body, header and row column from Material ui class. 

import {Table, TableBody, TableHeader, TableHeaderColumn, TableRow, TableRowColumn} from "material-ui/Table";

We then make our header Columns, in our case it’s three, namely Commit ID, Commit Message, Author name.

 <TableRow>
  <TableHeaderColumn tooltip="Commit ID">Commit ID</TableHeaderColumn>
  <TableHeaderColumn tooltip="Commit Message">Commit Message</TableHeaderColumn>
  <TableHeaderColumn tooltip="Author Name">Author Name</TableHeaderColumn>
 </TableRow>

To get the history of modification of a skill, we will use endpoint “http://api.susi.ai/cms/getSkillHistory.json”. It uses JGit for managing version control in skill data repository. JGit is a library which implements the Git functionality in Java. An endpoint is accessed based on userRoles, which can be Admin, Privilege, User, Anonymous. In our case it is Anonymous. Thus a User need not to log in to access this endpoint.

 url = "http://api.susi.ai/cms/getSkillHistory.json?model="+models[this.state.modelValue].key+"&group="+groups[this.state.groupValue].key+"&language="+languages[this.state.languageValue].key+"&skill="+this.state.expertValue;

After getting the url, we will next make a ajax network call to get the modification history of skill., if the method returns success, we get the desired data in table array and display it in rows through its render() method, which checks if the data is set in state -if so, it renders the contents otherwise we display the error occurred while processing the request.

 $.ajax({
            url: url,
            jsonpCallback: 'pccd',
            dataType: 'jsonp',
            jsonp: 'callback',
            crossDomain: true,
            success: function(data) {
                data = data.commits;
                let array = [];
                for(let i=0;i<data.length;i++){
                    array.push(data[i]);
                }
{tableData.map((row, index) => (
  <TableRow key={index}>
    <TableRowColumn>{row.commitID}</TableRowColumn>
    <TableRowColumn>{row.commit_message}</TableRowColumn>
    <TableRowColumn>{row.author}</TableRowColumn>
   </TableRow>
))}

Test the final output  on http://skills.susi.ai/browseHistory or http://localhost:3000/browseHistory , select the model, group , language and skill and get the history of that skill.


Next time when you need drop down list or tables to organize your data, do check out https://github.com/callemall/material-ui for examples, which can help in providing good outlines to you apps. For contributions to susi_skill_cms, join our chat channel  on gitter: https://gitter.im/fossasia/susi_server and browse https://github.com/fossasia/susi_skill_cms for complete code.

Resources

Adding Manual ISO Controls in Phimpme Android

The Phimpme Android application comes with a well-featured camera to take high resolution photographs. It features an auto mode in the camera as well as a manual mode for users who likes to customise the camera experience according to their own liking. It provides the users to select from the range of ISO values supported by their devices with a manual mode to enhance the images in case the auto mode fails on certain circumstances such as low lighting conditions.

In this tutorial, I will be discussing how we achieved this in Phimpme Android with some code snippets and screenshots.

To provide the users with an option to select from the range of ISO values, the first thing we need to do is scan the phone for all the supported values of ISO and store it in an arraylist to be used to display later on. This can be done by the snippet provided below:

String iso_values = parameters.get("iso-values");
if( iso_values == null ) {
 iso_values = parameters.get("iso-mode-values"); // Galaxy Nexus
 if( iso_values == null ) {
    iso_values = parameters.get("iso-speed-values"); // Micromax A101
    if( iso_values == null )
       iso_values = parameters.get("nv-picture-iso-values"); // LG dual P990

Every device supports a different set of keyword to provide the list of ISO values. Hence, we have tried to add every possible keywords to extract the values. Some of the keywords used above covers almost 90% of the android devices and gets the set of ISO values successfully.

For the devices which supports the ISO values but doesn’t provide the keyword to extract the ISO values, we can provide the standard list of ISO values manually using the code snippet provided below:

values.add("200");
values.add("400");
values.add("800");
values.add("1600");

After extracting the set of ISO values, we need to create a list to display to the user and upon selection of the particular ISO value as depicted in the Phimpme camera screenshot below

Now to set the selected ISO value, we first need to get the ISO key to set the ISO values as depicted in the code snippet provided below:

if( parameters.get(iso_key) == null ) {
 iso_key = "iso-speed"; // Micromax A101
 if( parameters.get(iso_key) == null ) {
    iso_key = "nv-picture-iso"; // LG dual P990
    if( parameters.get(iso_key) == null ) {
       if ( Build.MODEL.contains("Z00") )
          iso_key = "iso"; // Asus Zenfone 2 Z00A and Z008

Getting the key to set the ISO values is similar to getting the key to extract the ISO values from the device. The above listed ISO keys to set the values covers most of the devices.

Now after we have got the ISO key, we need to change the camera parameter to reflect the selected change.

parameters.set(iso_key, supported_values.selected_value);
setCameraParameters(parameters);

To get the full source code on how to set the ISO values manually, please refer to the Phimpme Android repository.

Resources

  1. Stackoverflow – Keywords to extract ISO values from the device: http://stackoverflow.com/questions/2978095/android-camera-api-iso-setting
  2. Open camera Android source code: https://sourceforge.net/p/opencamera/code/ci/master/tree/
  3. Blog – Learn more about ISO values in photography: https://photographylife.com/what-is-iso-in-photography

Handling High Resolution Images in Phimpme Android

In Android, loading heavy and high resolution images is a difficult task. For instance, if we try to load a photo clicked at a resolution four times that of the screen and try to load it in an imageview, it may result in an app’s crash due to the OutOFMemory exception. It happens because at the run time of our application some limited memory is allocated to our application and if we exceed that by loading a high quality images. To make a perfect gallery application, one must take care of all the possible causes for application crashes. In Phimpme Android, we have done this with the help of Glide library with some other tweaks to help catch all possible causes for the OutOfMemory exceptions. In this tutorial, I will be discussing how we have displayed heavy images with the help of Glide library and other tweaks to avoid the above mentioned exception.

Step 1:

To avoid the OutOFMemory exception, first we have to add the below line of code in the AndroidManifest.xml file.

android:largeHeap=”true”

What this piece of code does is that it increases the amount of heap memory that is allocated at the time of run time of the application. Hence, more heap memory, less chance of running out of memory.

Step 2:

To load the images into the image view we can make use of the Glide library as it is the most recommended way to do it according to the Google’s  Android developer page to cache bitmaps. The below code helps us to load the image in the imageView using a pager adapter.

Glide.with(getContext())
          .load(img.getUri())
          .asBitmap().format(DecodeFormat.PREFER_RGB_565)
          .signature(useCache ? img.getSignature(): new StringSignature(new Date().getTime()+""))
          .diskCacheStrategy(DiskCacheStrategy.SOURCE)
          .thumbnail(0.5f)
          .transform(new RotateTransformation(getContext(), img.getOrientation(), false))
          .animate(R.anim.fade_in)
          .into(new SimpleTarget<Bitmap>() {
[email protected]
              public void onResourceReady(Bitmap bitmap, GlideAnimation<? super Bitmap> glideAnimation) {
                  photoView.setImageBitmap(bitmap);
              }
          });

This is the way we have done it in the Phimpme Android application using the Glide library. We are loading the image as a bitmap and by preferring the bitmap RGB565 as it consumes 50% less memory than the RGB8888 model which may be the type of the original image. Of course the image quality will seem bit less but it is not noticeable until we zoom in to full extent.

The next thing we are doing is caching the image in the memory using the below line of code using Glide library.

.diskCacheStrategy(DiskCacheStrategy.SOURCE)

As caching images offers faster access to the images. It also helps in avoiding the OutOfMemory crashes when we are using a large list of images. Link to cache images without using the Glide library is mentioned in the resources section below. After this, we are loading the image in the PhotoView which is a module we are using in the Phimpme Android application, which extends to the imageView and comes with many zooming images functionalities.

This is how we have implemented the loading of images in the gallery view in Phimpme Android application so that it can handle resolution of any sizes without running out of memory. To get the full source code on how to load high resolution images, please refer to the Phimpme Android repository.

Resource

  1. Introduction to glide image loader: https://inthecheesefactory.com/blog/get-to-know-glide-recommended-by-google/en
  2. Google developer guide to cache bitmaps without using glide library : https://developer.android.com/topic/performance/graphics/cache-bitmap.html.
  3. Google developer guide to OutOfMemory Exceptions: https://developer.android.com/reference/java/lang/OutOfMemoryError.html
  4. LeafPic GitHub repository: https://github.com/HoraApps/LeafPic

Implementing Intelligence Feature in Susper

Susper gives answers to your questions using SUSI AI. We want to give users best experience while they are searching for solutions to their questions. To achieve this, we have incorporated with features like infobox and intelligence using SUSI.

Google has this feature where users can ask questions like ‘Who is president of USA?’ and get answers directly without encouraging the users to deep-dive into the search results to know the answer.

Similarly Susper gives answer to the user:

It also gives answer to question which is related to real time data like temperature.

 

How we have implemented this feature?

We used the API Endpoint of SUSI at http://api.asksusi.com/

Using SUSI API is as simple as sending query as a URL parameter in GET request http://api.susi.ai/susi/chat.json?q=YOUR_QUERY

You can also get various action types in the response. Eg: An anwser type response for http://api.susi.ai/susi/chat.json?q=hey%20susi is:

actions: [
  {
    type: "answer",
    expression: "Hi, I'm Susi"
  }
],

 

Documentation regarding SUSI is available at here.

Implementation in Susper:

We have created an Intelligence component to display answer related to a question. You can check it here: https://github.com/fossasia/susper.com/tree/master/src/app/intelligence

It takes care about rendering the information and styling of the rendered data received from SUSI API.

The intelligence.component.ts makes a call to Intelligence Service with the required query and the intelligence service makes a GETrequest to the SUSI API and retrieves the results.

Intelligence.component.ts

this.intelligence.getintelligentresponse(data.query).subscribe(res => {
  if (res && res.answers && res.answers[0].actions) {
     this.actions = res.answers[0].actions;
       for (let action of this.actions) {
         if (action.type === 'answer' && action.mood !== 'sabta') {
           this.answer = action.expression;
         } else {
             this.answer = '';
         }
      }
   } else {
       this.answer = '';
   }
});

 

Intelligence.service.ts

export class IntelligenceService {
 server = 'http://api.susi.ai';
 searchURL = 'http://' + this.server + '/susi/chat.json';
 constructor(private http: Http, private jsonp: Jsonp, private store: Store<fromRoot.State>) {
 }
 getintelligentresponse(searchquery) {
   let params = new URLSearchParams();
   params.set('q', searchquery);
   params.set('callback', 'JSONP_CALLBACK');
   return this.jsonp
     .get('http://api.asksusi.com/susi/chat.json', {search: params}).map(res =>
       res.json()

     );
 }

Whenever the getintelligenceresponse of intelligenceService is called, it creates a URLSearchParams() object and set required parameters in it and send them in jsonp.get request. We also set callback to ‘JSONP_CALLBACK’ to inform the API to send us data in JSONP.

Thereby, the intelligence component retrieves the answer and displays it with search resultson Susper.

Source code for this implementation could be found in this pull:

https://github.com/fossasia/susper.com/pull/569

Resources:

Registering Organizations’ Repositories for Continuous Integration with Yaydoc

Among various features implemented in Yaydoc was the introduction of a modal in the Web Interface used for Continuous Deployment. The modal was used to register user’s repositories to Yaydoc. All the registered repositories then had their documentation updated continuously at each commit made to the repository. This functionality is achieved using Github Webhooks.

The implementation was able to perform the continuous deployment successfully. However, there was a limitation that only the public repositories owned by a user could be registered. Repositories owned by some organisations, which the user either owned or had admin access to couldn’t be registered to Yaydoc.

In order to perform this enhancement, a select tag was added which contains all the organizations the user have authorized Yaydoc to access. These organizations were received from Github’s Organization API using the user’s access token.

/**
 * Retrieve a list of organization the user has access to
 * @param accessToken: Access Token of the user
 * @param callback: Returning the list of organizations
 */
exports.retrieveOrgs = function (accessToken, callback) {
  request({
    url: ‘https://api.github.com/user/orgs’,
    headers: {
      ‘User-Agent’: ‘request’,
      ‘Authorization’: ‘token ’ + accessToken
    }
  }, function (error, response, body) {
    var organizations = [];
    var bodyJSON = JSON.parse(body);
    bodyJSON.forEach(function (organization) {
      organizations.push(organization.login);
    });
    return callback(organizations);
  });
};

On selecting a particular organization from the select tag, the list of repositories is updated. The user then inputs a query in a search input which on submitting shows a list of repositories that matches the tag. An AJAX get request is sent to Github’s Search API in order to retrieve all the repositories matching the keyword.

$(function () {
  ....
$.get(`https://api.github.com/search/repositories?q=user:${username}+fork:true+${searchBarInput.val()}`, function (result) {
    ....
    result.items.forEach(function (repository) {
      options +=<option>+ repo.full_name +</option>’;
    });
    ....
  });
  ....
});

The selected repository is then submitted to the backend where the repository is registered in Yaydoc’s database and a hook is setup to Yaydoc’s CI, as it was happening with user’s repositories. After a successful registration, every commit on the user’s or organization’s repository sends a webhook on receiving which, Yaydoc performs the documentation generation and deployment process.

Resources:

  1. Github’s Organization API: https://developer.github.com/v3/orgs/
  2. Github’s Search API: https://developer.github.com/v3/search/
  3. Simplified HTTP Request Client: https://github.com/request/request