Lazy loading images in Loklak Search

Loklak Search delivers the media rich content to the users. Most of the media delivered to the users are in the form of images. In the earlier versions of loklak search, these images were delivered to the users imperatively, irrespective of their need. What this meant is, whether the image is required by the user or not it was delivered, consuming the bandwidth and slowing down the initial load of the app as large amount of data was required to be fetched before the page was ready. Also, the 404 errors were also not being handled, thus giving the feel of a broken UI.

So we required a mechanism to control this loading process and tap into its various aspects, to handle the edge cases. This, on the whole, required few new Web Standard APIs to enable smooth working of this feature. These API’s are

  • IntersectionObserver API
  • Fetch API

 

As the details of this feature are involving and comprise of new API standards, I have divided this into two posts, one with the basics of the above mentioned API’s and the outline approach to the module and its subcomponents and the difficulties which we faced. The second post will mostly comprise of the details of the code which went into making this feature and how we tackled the corner cases in the path.

Overview

Our goal here was to create a component which can lazily load the images and provide UI feedback to the user in case of any load or parse error. As mentioned above the development of this feature depends on the relatively new web standards API’s so it’s important to understand the functioning of these AP’s we understand how they become the intrinsic part of our LazyImgComponent.

Intersection Observer

If we see history, the problem of intersection of two elements on the web in a performant way has been impossible since always, because it requires DOM polling constantly for the ClientRect the element which we want to check for intersection, as these operations run on main thread these kinds of polling has always been a source of bottlenecks in the application performance.

The intersection observer API is a web standard to detect when two DOM elements intersect with each other. The intersection observer API helps us to configure a callback whenever an element called target intersects with another element (root) or viewport.

To create an intersection observer is a simple task we just have to create a new instance of the observer.

var observer = new IntersectionObserver(callback, options);

Here the callback is the function to run whenever some observed element intersect with the element supplied to the observer. This element is configured in the options object passed to the Intersection Observer

var options = {
root: document.querySelector('#root'), // Defaults to viewport if null
rootMargin: '0px', // The margin around root within which the callback is triggered
threshold: 1.0
}

The target element whose intersection is to be tested with the main element can be setup using the observe method of the observer.

var target = document.querySelector('#target');
observer.observe(target);

After this setup whenever the target element intersects with the root element the callback method is fired, and this provides the easy standard mechanism to get callbacks whenever the target element intersects with root element.

How this is used in Loklak Search?

Our goal here is to load the images only if required, ie. we should load the images lazily only if they are in the viewport. So the task of checking whether the element is near the viewport is done in a performant way using the Intersection Observer standard.

Fetch API

Fetch API provides interface for fetching resources. It provides us with generic Request and Response interfaces, which can be used as Streaming responses, requests from Service Worker or CacheAPI. This is a very lightweight API providing us with the flexibility and power to make the AJAX requests from anywhere, irrespective of context of the thread or the worker. It is also a Promise driven API. Thus, providing the remedy from the callback hell.

The basic fetch requests are really very easy to setup, all they require is the path to the resource to fetch, and they return a promise on which we can apply promise chaining to transform the response into desired form. A very simple example illustratuing the fetch API is as follows.

fetch('someResource.xyz’')
.then(function(response) {
return response.blob();
})
.then(function(respBlob) {
doSomethingWithBlob(respBlob);
});

The role of fetch api is pretty straight forward in the lazy loading of images, ie. to actually fetch the images when requested.

Lazy Image Component

We start designing our lazy image component by structuring our API. The API our component should provide to the outside world must be similar to Native Image Element, in terms of attributes and methods. So our component’s class should look something like this, with attributes src, alt, title, width and height. Also, we have hooked a load event which host can listen to for loading success or failure of the image.

@Component({
selector: 'app-lazy-img',
templateUrl: './lazy-img.component.html',
styleUrls: ['./lazy-img.component.scss'],
})
export class LazyImgComponent {
@Input() src: string;
@Input() width: number;
@Input() height: number;
@Input() alt: string;
@Input() title: string;
@Output() load: EventEmitter<boolean> = new EventEmitter<boolean>();
}

This basic API provides us with our custom <app-lazy-img> tag which we can use with the attributes, instead of standard <img> element to load the images when needed.

So our component is basically a wrapper around the standard img element to provide lazy loading behaviour.

This is the basic outline of how our component will be working.

  • The component registers with an Intersection observer, which notifies the element when it comes near the viewport.
  • Upon this notification, the component fetches the resource image.
  • When the fetch is complete, the retrieved binary stream is fed to the native image element which eventually renders the image on the screen.

This is the basic setup and working logic behind our lazy image component. In next post, I will be discussing the details of how this logic is achieved inside the LazyImgComponent, and how we solve the problems with a large number of elements rendered at once in the application.

Resources and Links

  • Intersection observer API
  • Intersection Observer polyfill for the browsers which don’t support Intersection Observer
  • Fetch API documentation
  • Fetch API polyfill for the browsers which don’t support fetch.
  • Loklak Search Repo

Updating Page Titles Dynamically in Loklak Search

Page titles are native in the web platform, and are prime ways to identify any page. The page titles have been in the web platform since ages. They tell the browsers, the web scrapers and search engines about the page content in 1-2 words. Since the titles are used for wide variety of things from presentation of the page, history references and most importantly by the search engines to categorise the pages, it becomes very important for any web application to update the title of the page appropriately. In earlier implementation of loklak search the page title was a constant and was not updated regularly and this was not a good from presentation and SEO perspective.

Problem of page titles with SPA

Since loklak search is a single page application, there are few differences in the page title implementation in comparison to a server served multi page application. In a server served multi page application, the whole application is divided into pages and the server knows what page it is serving thus can easily set the title of the page while rendering the template. A simple example will be a base django template which holds the place to have a title block for the application.

<!-- base.html -->

<title>{% block title %} Lokalk Search {% endblock %}</title>

<!-- Other application blocks -->

Now for any other page extending this base.html it is very simple to update the title block by simply replacing it with it’s own title.

<!-- home.html -->

{% extendsbase.html%}

{% block title %} Home Page - Loklak Search {% endblock %}

<!-- Other page blocks -->

When the above template is rendered by the templating engine it replaces the title block of the base.html with the updated title block specific to the page, thus for each page at the rendering time server is able to update the page title, appropriately.

But in a SPA, the server just acts as REST endpoints, and all the templating is done at the client side. Thus in an SPA the page title never changes automatically, from the server, as only the client is in control of what page (route) it is showing. Thus it becomes the duty of the client side to update the title of the page, appropriately, and thus this issue of static non informative page titles is often overlooked.

Updating page titles in Loklak Search

Before being able to start solving the issue of updating the page titles it is certainly very important to understand what all are the points of change in the application where we need to update the page title.

  • Whenever the route in the application changes.
  • Whenever new query is fetched from the server.

These two are the most important places where we definitely want to update the titles. The way we achieved is using the Angular Title Service. The title service is a platform-browser service by angular which abstracts the workflow to achieve the title updation. There are are two main methods of this service get and set title, which can be used to achieve our desired behaviour. What title service do is abstract the extraction of Title Node and get and set the title values.

For updation of title for each page which is loaded we just attach an onInit lifecycle hook to the parent component of that page and, onInit we use the title service to update the title accordingly.

@Component({
selector: 'app-home',
templateUrl: './home.component.html',
})
export class HomeComponent implements OnInit, OnDestroy {
constructor(
private titleService: Title
) { }

ngOnInit() {
this.titleService.setTitle(Loklak Search');

// Other initialization attributes and methods
}
}

Similarly other pages according to their context update the page titles accordingly using the simple title service. This solves the basic case of updation of the titles of the page when the actual route path changes, and thus component’s onInit lifecycle hook is the best place to change the title of the page.

@Component({
selector: 'app-home',
templateUrl: './home.component.html',
})
export class HomeComponent implements OnInit, OnDestroy {
constructor(
private titleService: Title
) { }

ngOnInit() {
this.titleService.setTitle(Loklak Search');

// Other initialization attributes and methods
}
}

But when the actual route path doesn’t change and we want to update the title according to the query searched then it is not possible to do it using lifecycle hooks of the component. But fortunately, we are using the ngrx effects in our application and thus this task also again becomes much simpler to achieve in the application. In this situation again what we do is hook up a title change effect to SearchCompleteSuccessAction, and there we change the title accordingly.

@Effect({ dispatch: false })
resetTitleAfterSearchSuccess$: Observable<void>
= this.actions$
.ofType(apiAction.ActionTypes.SEARCH_COMPLETE_SUCCESS,
apiAction.ActionTypes.SEARCH_COMPLETE_FAIL)
.withLatestFrom(this.store$)
.map(([action, state]) => {
const displayString = state.query.displayString;
let title = `${displayString} - Loklak Search`;
if (action.type === apiAction.ActionTypes.SEARCH_COMPLETE_FAIL) {
title += ' - No Results';
}
this.titleService.setTitle(title);
});

Now if we look closely this effect is somewhat different from all the other effects. Firstly, the effect observable is of type void instead of type Action which is the case with other effects, and also there is is a { dispatch: false } argument passed to the constructor. Both these things are important of our resetTitle effect. As our reset title effect has no action to dispatch on it it’s execution the the observable is of type void instead of type Action, and we never want to dispatch an effect whose type is not an Action thus we set dispatch to false. Rest of the code for the effect is fairly simple, we filter all the actions and take SearchSuccess and SearchFail actions, then we get the latest value of the query display string from the store, and we use our title service to reset the title accordingly.

Conclusion

The titles are the important part of the web platform and are used by browsers and search engines to present and rank the relevance of the page. While developing a SPA it is even more important to maintain an updated title tag, as it is the only thing which actually changes about the page according to the context of the page. In loklak search, the title service is now used to update the titles of the page according to the search results.

Resources and Links

Search Engine Optimization and Meta Tags in Loklak Search

Ranking higher in search results is very important for any website’s, productivity and reach. These days modern search engines use algorithms and scrape the sites for relevant data points, then these data points are processed to result in a relevance number aka the page ranking. Though these days the algorithms which search engines use are very advanced and are able to generate the context of the page by the page content, but still there are some key points which should be followed by the developers to enable a higher page ranking on search results along with better presentation of search results on the pages. Loklak Search is also a web application thus it is very important to get these crucial points correct.

So the first thing which search engines see on a website is their landing or index page, this page gives information to the search engine what the data is about in this site and then search engines follow links to to crawl the details of the page. So the landing page should be able to provide the exact context for the page. The page can provide the context using the meta tags which store the metadata information about the page, which can be used by the search engines and social sites

 

Meta Charset

    • The first and foremost important tag is the charset tag. It specifies the character set being used on the page and declares the page’s character encoding which is used by browsers encoding algorithm.
    • It very important to determine a correct and compatible charset for security and presentation reasons.
    • Thus the most widely used charset UTF-8 is used in loklak search.
<meta charset="utf-8">


Meta Viewport

    • The mobile browsers often load the page in a representative viewport which is usually larger than the actual screen of the device so that the content of the screen is not crammed into small space.
    • This makes the user to pan-zoom around the page to reach the desired content. But this approach often undesirable design for the most of the mobile websites.
    • For this at loklak search we use

 

<meta name="viewport" content="width=device-width, initial-scale=1">

 

    • This specifies the relation between CSS pixel and device pixel. Here the relationship is actually computed by the browser itself, but this meta tag says that the width for calculating that ratio should be equal to device width and the initial-scale should be 1 (or zoom should be 0).

      Meta Description

      • The meta description tag is the most important tad for the SEO as this is the description which is used by search engines and social media sites while showing the description of the page

<!– HTML generated using hilite.me –>

<meta name="description"
   content="Search social media on Loklak Search. Our mission is to make the world’s social media information openly accessible and useful generating open knowledge for all">
  • This is how this description tag is used by google on the Google Search

Social Media meta tags

    • The social media meta tags are important for the presentation of the content of the page when the link is shared in these sites.
    • As the link sharing is fairly simple in social media the links should always be able to show some context about the page in a visual form without the need to click the link actually.
    • For this purpose Facebook and Twitter read special kinds of meta tags to read and display information regarding the page whenever the link is shared.
    • For twitter cards data we have used
<!-- Twitter Card data -->
 <meta name="twitter:card" content="summary">
 <meta name="twitter:site" content="@fossasia">
 <meta name="twitter:title" content="Loklak Search">
 <meta name="twitter:description" content="Search social media on Loklak Search. Our mission is to make the world’s social media information openly accessible and useful generating open knowledge for all">
 <meta name="twitter:image" content="http://loklak.net/assets/images/cow_400x466.png">
    • This specifies the card to be of type summary card. There are many types of cards which are provided by twitter and  can be found here.
    • The facebook also uses the similar approach but with different meta tag names for their graph API.
 <!-- Open Graph data -->
 <meta property="og:title" content="Loklak Search">
 <meta property="og:type" content="website">
 <meta property="og:url" content="http://loklak.net">
 <meta property="og:image" content="http://loklak.net/assets/images/cow_400x466.png">
 <meta property="og:description" content="Search social media on Loklak Search. Our mission is to make the world’s social media information openly accessible and useful generating open knowledge for all">
 <meta property="og:site_name" content="Loklak Search">
  • Conclusion

Presentation of the content in the web plays a crucial role in reach and usage of the application. Wider reach and high usage are the core goals of Loklak Search project so that open data reaches the masses. To achieve this we are slowly moving for better sharing experience for the loklak pages using standard techniques. By using these we hope that we will be able to reach a wider audience, thus delivering the open content to masses.

Resources and Links

Adding IBM Watson TTS Support in Susi Assistant on Raspberry Pi

Susi Hardware project aims at creating a smart assistant for your home that you can run on your Raspberry Pi or similar Development Boards.
I previously wrote a blog on choosing a perfect Text to Speech engine for Susi AI and had used Flite as the solution for it. While Flite is an Open Source solution that can run locally on a client, it does not provide the same quality of voice and speed as cloud providers. We always crave for a more natural voice for better interaction with our assistant. It is always good to have more options. We, therefore, added IBM Watson Text to Speech API in SUSI Hardware project.

IBM Watson TTS can be added to a Python Project easily using the IBM Watson Developer SDK.

For using the IBM Watson Developer SDK for Text to Speech, first of all, we need to sign up for Bluemix
https://console.bluemix.net/registration/

After that, we will get the empty dashboard without any service added currently. We need to create a Text to Speech Service. To do so, click on Create Watson Service button

    

Select Watson on the left pane and then select Text to Speech service from the list.

Select the standard plan from the options and then click on create button.

You will get service credentials for your newly created text to speech service. Save it for future reference.

After that, we need to add Watson developer cloud python package.

sudo pip3 install watson-developer-cloud

On Ubuntu with Python 3.5 watson-developer-cloud has some extra dependencies. Install them using the following command.

sudo apt install libssl-dev

Now we can add Text to Speech to our project. For that, we need to first import TextToSpeechV1 library. It can be added using following import statement.

from watson_developer_cloud import TextToSpeechV1

Now we need to create a new TextToSpeechV1 object using the Service Credentials we created earlier.

text_to_speech = TextToSpeechV1(
   username='API_USERNAME',
   password='API_PASSWORD')

We can now perform synthesis of a text input and write the incoming speech stream from IBM Watson API to a file.

with open('output.wav', 'wb') as audio_file:
   audio_file.write(
       text_to_speech.synthesize(text, accept='audio/wav’, voice='en-US_AllisonVoice'))

In the above code snippet,  we are opening an output file ‘output.wav’ for writing. We then write the binary audio data returned by text_to_speech.synthesize method. IBM Watson provides many free voices. We supply an argument specifying which voice we need to use. We are using English female ‘en-US_AllisonVoice’. You may test out more voices in the online demo here and select the voice that you find best.

We can play the ‘output.wav’ file using the play command from SoX. To do so, we need to install SoX binary.

sudo apt install sox libsox-fmt-all

We can play the file easily now using the following code.

import os
os.system('play output.wav')

The above code invokes the ‘play’ command from the SoX package to play the audio file. We can also use PyAudio to play the audio file but it would require us to manage the audio thread separately. Thus, SoX is a better solution.

Resources:

Setup SUSI Assistant on Raspberry Pi in under 30 minutes

With our ever growing list of list of platforms supported by Susi AI, we now have a client that can run on Raspberry Pi and you can access it hands-free!! Here is a video that you can refer for its working.

But it might have left you wondering how you can replicate such a setup yourself? It is fairly easy and will be done fairly easy. Just follow the following instructions.

You need to have following hardware in order to have your own SUSI Assistant running on Raspberry Pi.

  • A Raspberry Pi (prefer 2 or 3) with Raspbian Jessie OS.
  • A stable internet connection.  ( Recommended 4 Mbps )
  • A USB Microphone /  USB Webcam with Microphone. You may buy one like this.
  • A Speaker that connects through 3.5mm jack. You may buy one like this.

After you get all the above items in order, you need to get access to a terminal of your Raspberry Pi. You can have that by either connecting a monitor to Raspberry Pi temporarily or by connecting to Raspberry Pi over SSH.

Once this is done, next step is the installation of the dependencies. The installation of the SUSI on Raspberry is automated after dependencies are installed. Run the following command on Raspberry Pi terminal.

sudo apt install git swig3.0 portaudio19-dev pulseaudio libpulse-dev unzip sox libatlas-dev libatlas-base-dev libsox-fmt-all python3

After this, you may check if your output and input devices are working alright. For this, run rec recording.wav . It will start recording audio and saving it to a file named recording.wav. Play back the file using play recording.wav If you hear your audio clearly, setup is done right else you need to configure your Audio Devices correctly.  Most of the time the configuration of Audio works out the box and devices are plug and play so you would not encounter any errors. If you are successful in configuring your devices, install extra dependencies for SUSI Hardware by running the automated install script. In your terminal run,

$ git clone https://github.com/fossasia/susi_hardware.git
$ cd susi_hardware
$ ./install.sh 

This will install all the remaining dependencies. After the above step is complete, you may run configuration file generator script to choose the Text to Speech and Speech to Text service according to your wish. For doing so, you need to run

$ python3 config_generator.py

Follow the instructions in the script. It will ask you to configure the default service for Text to Speech and Speech to Text and other options. After the configuration is complete, you can simply run the following command to start SUSI.

$ python3 main.py

This will start SUSI in a continuously listening mode. You may invoke SUSI anytime, just by saying SUSI followed by a query. The query will be answered by SUSI subsequently.

Since configurations for different hardware devices may vary, you may encounter some problems. In such a scenario, you may refer to the following resources to solve the issues.

Resources:

Building Meilix in Travis using Heroku

Suppose you have to trigger (start) Travis but not through making a commit but through clicking a button on the webapp of the Meilix Generator. Through the webapp of Meilix Generator, we can pass the tag of the build which will be initiated and can also get the build link which is built by Travis.
Heroku is the place where we have deployed our webapp and through a button on the webapp that we used to start the build on the Travis. We have the access to give a tag to the build and with the help of this, we can even predict the URL of the build beforehand. So one can use it for its own personal project in a number of ways. And how I used this feature in Meilix Generator using Meilix script is described below:

How I used this idea

FOSSASIA meilix repository consists the script of a Linux Operating System based on Lubuntu. It uses Travis to build that script to result in a release of an iso file.

Now we thought an idea of building an autonomous system to start this build and get the release and in the meanwhile also make some required changes to the script to get it into the OS. We came up with an idea of a webapp which ask user its email id and tag of the build and till now a picture from the user which will be set as a wallpaper. It means the user would be able to config its distro according to its need through the graphical interface without a single line to code from the user end.

Through the webapp, a build button is taken as an input to go to a build page which triggers the Travis with the same user configuration to build the iso and deploy it on Github page. The user gets the link to the build on the next page only.

How I implemented this idea

Thanks to Travis API without which our idea is impossible to implement. We used a shell script to outframe our idea. The script takes the input of the user’s, repository, and branch to decide to where the trigger to take place.

There are two files one as travis_token as:

fossasia meilix master    # in the format of user repo branch

And script.sh as:  

#!/bin/bash
cat travis_tokens | while read line;     # this lines takes input of the user, repo and branch
do
    array=(${line})
    user="${array[0]}"
    project="${array[1]}"
    len=${#array[@]}
    for ((i=2; i<len; i++)); do
        branch="${array[i]}"            # supplied each value as variable
        body="{\"request\":{
            \"branch\":\"${branch}\",
            \"config\":{
                \"env\":{
                    \"email\":\"${email}\",    # supplied email and travis tag as environment variable
                    \"TRAVIS_TAG\":\"${TRAVIS_TAG}\"
                }
            }
    }}"
    echo "This Link Will be ready in approx 20 minutes"
    echo "https://github.com/fossasia/meilix/releases/download/${TRAVIS_TAG}/meilix-zesty-`date +%Y%m%d`-i386.iso"                  # a pre-predication of the link, we provide tag from user and date from system.
        curl -s -X POST \           # sending an API POST request to Travis to trigger the build of most recent commit 
            -H "Content-Type: application/json" \
            -H "Accept: application/json" \
            -H "Travis-API-Version: 3" \
            -H "Authorization: token ${KEY}" \     # this is stored in Heroku as KEY as environment variable and supplied from there only
            -d "${body}" \
            "https://api.travis-ci.org/repo/${user}%2F${project}/requests"  #%2 is used to interpret user and repo name as a single URL segment.
    done
done

After the trigger, you will get email which consists of a downloadable link to the iso.

How can this idea be helpful to a developer

There are lots of ways a developer can use this idea out. If a developer wants their user to automatically trigger the build and get the release build directly.

One can use it to set even the commit message through the shell script and customizing build configuration like replace, merge or deep_merge a configuration with the original .travis.yml file present in source repo.

Useful repositories and link which uses this:

Know more about Travis API v3:
Triggering the build
API blog

Have a look at our webapp and generate your own iso:
https://melix-generator.herokuapp.com/

Source code here:
https://github.com/fossasia/meilix-generator
https://github.com/fossasia/meilix

Making loklak Server’s Kaizen Harvester Extendable

Harvesting strategies in loklak are something that the end users can’t see, but they play a vital role in deciding the way in which messages are collected with loklak. One of the strategies in loklak is defined by the Kaizen Harvester, which generates queries from collected messages.

The original strategy used a simple hash queue which drops queries once it is full. This effect is not desirable as we tend to lose important queries in this process if they come up late while harvesting. To overcome this behaviour without losing important search queries, we needed to come up with new harvesting strategy(ies) that would provide a better approach for harvesting. In this blog post, I am discussing the changes made in the kaizen harvester so it can be extended to create different flavors of harvesters.

What can be different in extended harvesters?

To make the Kaizen harvester extendable, we first needed to decide that what are the parts in the original Kaizen harvester that can be changed to make the strategy different (and probably better).

Since one of the most crucial part of the Kaizen harvester was the way it stores queries to be processed, it was one of the most obvious things to change. Another thing that should be allowed to configure across various strategies was the decision of whether to go for harvesting the queries from the query list.

Query storage with KaizenQueries

To allow different methods of storing the queries, KaizenQueries class was introduced in loklak. It was configured to provide basic methods that would be required for a query storing technique to work. A query storing technique can be any data structure that we can use to store search queries for Kaizen harvester.

public abstract class KaizenQueries {

     public abstract boolean addQuery(String query);

     public abstract String getQuery();

     public abstract int getSize();

     public abstract int getMaxSize();

     public boolean isEmpty() {
         return this.getSize() == 0;
     }
}

[SOURCE]

Also, a default type of KaizenQueries was introduced to use in the original Kaizen harvester. This allowed the same interface as the original queue which was used in the harvester.

Another constructor was introduced in Kaizen harvester which allowed setting the KaizenQueries for an instance of its derived classes. It solved the problem of providing an interface of KaizenQueries inside the Kaizen harvester which can be used by any inherited strategy –

private KaizenQueries queries = null;

public KaizenHarvester(KaizenQueries queries) {
    ...
    this.queries = queries;
    ...
}

public void someMethod() {
    ...
    this.queries.addQuery(someQuery);
    ...
}

[SOURCE]

This being added, getting new queries or adding new queries was a simple. We just need to use getQuery() and addQuery() methods without worrying about the internal implementations.

Configurable decision for harvesting

As mentioned earlier, the decision taken for harvesting should also be configurable. For this, a protected method was implemented and used in harvest() method –

protected boolean shallHarvest() {
    float targetProb = random.nextFloat();
    float prob = 0.5F;
    if (this.queries.getMaxSize() > 0) {
        prob = queries.getSize() / (float)queries.getMaxSize();
    }
    return !this.queries.isEmpty() && targetProb < prob;
}

@Override
public int harvest() {
    if (this.shallHarvest()) {
        return harvestMessages();
    }

    grabSuggestions();

    return 0;
}

[SOURCE]

The shallHarvest() method can be overridden by derived classes to allow any type of harvesting decision that they want. For example, we can configure it in such a way that it harvests if there are any queries in the queue, or to use a different probability distribution instead of linear (maybe gaussian around 1).

Conclusion

This blog post explained about the changes made in loklak’s Kaizen harvester which allowed other harvesting strategies to be built on its top. It discussed the two components changed and how they allowed ease of inheriting the original Kaizen harvester. These changes were proposed in PR loklak/loklak_server#1203 by @singhpratyush (me).

Resources

Accessing Child Component’s API in Loklak Search

Loklak search being an angular application, comprises of components. Components provide us a way to organize the application in a more consistent way, along with providing the ability to reuse code in the application. Each component has two type of API’s public and private. Public API is the API which it exposes to the outer world for manipulating the working of the component, while private API is something which is local to the component and cannot be directly accessed by the outside world. Now when this distinction between the two is clear, it is important to state the need of these API’s, and why are they required in loklak search.

The components can never live in isolation, i.e. they have to communicate with their parent to be able to function properly. Same is the case with components of loklak search. They have to interact with others to make the application work. So how this, interaction looks like,

The rule of thumb here is, data flows down, events flow up. This is the core idea of all the SPA frameworks of modern times, unidirectional data flow, and these interactions can be seen everywhere in loklak search.

<feed-header
   [query]="query"
   (searchEvent)="doSearch($event)"></feed-header>

This is how a simple component’s API looks in loklak search. Here our component is FeedHeader and it exposes some of it’s API as inputs and outputs.

export class FeedHeaderComponent {

 @Input() query: string;

 @Output() searchEvent: EventEmitter<string> = new EventEmitter<string>();

  // Other methods and properties of the component
}

The FeedHeaderComponent ‘s class defines some inputs which it takes. These inputs are the data given to the component. Here the input is a simple query property, and the parent at the time of instantiating the component, passes the value to it’s child as [query]=”query”. This enables the one direction of API, from parent to child. Now, we also need a way for parent to be able to events generated by the child on interaction with user. For example, here we need to have a way to tell the parent to perform a search whenever user presses search button. For this the Output property searchEvent is used. The search event can be emitted by the child component independently. While the parent, if it wants to listen to child components simply do so by binding to the event and running a corresponding function whenever event is emitted (searchEvent)=”doSearch($event)”. Here the event which parent listens to is searchEvent and whenever such an event is emitted by the child a function doSearch is run by the parent. Thus this completes the event flow, from child to parent.

Now it is worth noticing that all these inputs for data and outputs for events is provided by the child component itself. They are the API of the child and parent’s job is just to bind to these inputs and outputs to bind to data and listen to events. This allows the component interactions in both directions.

@ViewChild and triggering child’s methods

The inputs are important to carry data from the parent to the child, declaratively but sometimes it is necessary for the parent to access the public API of it’s child more directly, specially the API methods to trigger an action. These methods require the way for the parent to access its child component. This is done by @ViewChild decorator. The child element which the parent wants access to, have to declare the component as, one of it’s attributes. Like in our example, the FeedHeaderComponent needs access to its child component SuggestBoxComponent, to show/hide suggest box as and when required. So here the feed header component gets the access to its child using viewchild decorator.

export class FeedHeaderComponent {

  @ViewChild(‘#suggestBox) suggestBox: SuggestBoxComponent;

  // Other properties and methods

  toggleSuggestBox() {

     this.suggestBox.toggle();
  }
}

The SuggestBoxComponent here has a public method toggle() which toggles the visibility state of the suggest box. This method is available as a component’s public API method. The parent of this component calls this method using the @ViewChild reference which it grabbed at the time of view instantiation.

export class SuggestBoxComponent {

  private suggestBoxVisible = true;

  public toggle() {

     this.suggestBoxVisible = !this.suggestBoxVisible;
  }
}

Resources and Links

  • Angular document pages
  • Basic Usage in Angular tour of heroes tutorial
  • In depth usage blog for Inputs and Outputs SitePoint Tutorial
  • Loklak Search Repo

Building the Meilix Generator with Flask

Meilix Generator is a webapp which is used to trigger the Travis build of Meilix and mail the user the link of the iso. Meilix Generator webapp is based on Flask. This blog shows that how easy is to build a webapp and take the HTML files to render it into the webapp as well as to call and pass various function. Here I used Flask, the Python framework to render the HTML templates and send requests for various purposes (mentioned later in the article) without coding everything from scratch because of import facility of the Flask.

What is Flask?

Flask is a Python micro web framework based on Werkzeug, Jinja 2 template engine. It is used as the backbone of the webapp. It features us with a whole set of Python from which we can easily generate webapp. It is micro as it has no tools and no library itself. It come up with minimum requirements and one who needs can import different library and use it. And I used several import function for Meilix Generator like render_template, send_from_directory, etc.

Implementation (The use case in Meilix Generator)

First of all, the installation process: We will do the installation in a virtual environment. We prefer virtual environment to differentiate the Python working environment since few programs are there which require different Python versions to work.
Install virtual environment 

sudo pip install virtualenv

Now go to the folder (project) and activate it using

. venv/bin/activate

Now install Flask

pip install flask
Creating your project

Now it’s time to create a simple project in the directory.
Let’s use HTML as the frontend. In the folder create styles.css for styling and index.html template for the frontend of the page.We will make one app.py file which would look similar to this: 

from flask import Flask, render_template
app = Flask(__name__)
@app.route('/')
def index():
	"""Index page"""
	return render_template("index.html")
if __name__ == '__main__':
    app.run()

Flask looks for the / (root) path and here the root return the main template (index.html) which is the main function.

Compiling it to view the page:

export FLASK_DEBUG=1 FLASK_APP=app.py
flask run

You will find your page at http://127.0.0.1:5000

More options (how more it can help you)

  • Add more HTML template options and refer it in app.py
  • Easily use Github API  from a different .py file (this file should get import to app.py) to fetch data like: https://api.github.com/users/user_name : It will fetch user name, repos, followers and many more important information.

How I used this idea for FOSSASIA (Meilix Generator)

I used Flask for the backbone of project Meilix Generator. First, I used from function to import various library needed for the project and then made several functions for the same. Let’s understand the concept using few example:

from flask import Flask, render_template
@app.route('/about')
def about():
		#About page
		return render_template("about.html")

or

from flask import Flask, send_from_directory
@app.route('/uploads/<filename>')
def uploaded_file(filename):
		return send_from_directory(app.config['UPLOAD_FOLDER'],filename)

For more details file app.py can be found here of the Meilix Generator repository where we used the above idea.

Important Links and Repositories:

How to Store and Retrieve User Settings from SUSI Server in SUSI iOS

Any user using the SUSI iOS client can set preferences like enabling or disabling the hot word recognition or enabling input from the microphone. These settings need to be stored, in order to be used across all platforms such as web, Android or iOS. Now, in order to store these settings and maintain a synchronization between all the clients, we make use of the SUSI server. The server provides an endpoint to retrieve these settings when the user logs in.

First, we will focus on storing settings on the server followed by retrieving settings from the server. The endpoint to store settings is as follows:

http://api.susi.ai/aaa/changeUserSettings.json?key=key&value=value&access_token=ACCESS_TOKEN

This takes the key value pair for storing a settings and an access token to identify the user as parameters in the GET request. Let’s start by creating the method that takes input the params, calls the API to store settings and returns a status specifying if the executed successfully or not.

 let url = getApiUrl(UserDefaults.standard.object(forKey: ControllerConstants.UserDefaultsKeys.ipAddress) as! String, Methods.UserSettings)

        _ = makeRequest(url, .get, [:], parameters: params, completion: { (results, message) in
            if let _ = message {
                completion(false, ResponseMessages.ServerError)
            } else if let results = results {
                guard let response = results as? [String : AnyObject] else {
                    completion(false, ResponseMessages.ServerError)
                    return
                }
                if let accepted = response[ControllerConstants.accepted] as? Bool, let message = response[Client.UserKeys.Message] as? String {
                    if accepted {
                        completion(true, message)
                        return
                    }
                    completion(false, message)
                    return
                }
            }
        })

Let’s understand this function line by line. First we generate the URL by supplying the server address and the method. Then, we pass the URL and the params in the `makeRequest` method which has a completion handler returning a results object and an error object. Inside the completion handler, check for any error, if it exists mark the request completed with an error else check for the results object to be a dictionary and a key `accepted`, if this key is `true` our request executed successfully and we mark the request to be executed successfully and finally return the method. After making this method, it needs to be called in the view controller, we do so by the following code.

Client.sharedInstance.changeUserSettings(params) { (_, message) in
  DispatchQueue.global().async {
    self.view.makeToast(message)
  }
}

The code above takes input params containing the user token and key-value pair for the setting that needs to be stored. This request runs on a background thread and displays a toast message with the result of the request.

Now that the settings have been stored on the server, we need to retrieve these settings every time the user logs in the app. Below is the endpoint for the same:

http://api.susi.ai/aaa/listUserSettings.json?access_token=ACCESS_TOKEN

This endpoint accepts the user token which is generated when the user logs in which is used to uniquely identify the user and his/her settings are returned. Let’s create the method that would call this endpoint and parse and save the settings data in the iOS app’s User Defaults.

if let _ = message {
  completion(false, ResponseMessages.ServerError)
} else if let results = results {
  guard let response = results as? [String : AnyObject] else {
    completion(false, ResponseMessages.ServerError)
    return
  }
  guard let settings = 
response[ControllerConstants.Settings.settings.lowercased()] as? [String:String] else {
    completion(false, ResponseMessages.ServerError)
    return
  }
  for (key, value) in settings {
    if value.toBool() != nil {
      UserDefaults.standard.set(value.toBool()!, forKey: key)
    } else {
      UserDefaults.standard.set(value, forKey: key)
    }
  }
  completion(true, response[Client.UserKeys.Message] as? String ?? "error")
}

Here, the creation of the URL is same as we created above the only difference being the method passed. We parse the settings key value into a dictionary followed by a loop which loop’s through all the keys and stores the value in the User Defaults for that key. We simply call this method just after user log in as follows:

Client.sharedInstance.fetchUserSettings(params as [String : AnyObject]) { (success, message) in
  DispatchQueue.global().async {
    print("User settings fetch status: \(success) : \(message)")
  }
}

That’s all for this tutorial where we learned how to store and retrieve settings on the SUSI Server.

References