Tutorial

Read more about the article Adding Image Editor in Phimpme Android app

Adding Image Editor in Phimpme Android app

Image editing is a core feature of our Phimpme Android Application. We are using this already existing components Image Editor. Currently our app flow is Gallery → Open images. Now we add an option menu in the top named “Edit” which redirect to the edit Image Activity with that image path. Perform the edit operations there and apply the changed and finally updated the album with new file.

The task is to first get the image path pass to the EditImageActivity and then save the edited image in different folder after user finish with editing.

Below are the high level architecture of how it is achieved with help of ImageEditor.

Steps to integrate Editor:

  • Get the selected Image URI

Firstly, add the permission in manifest file

<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />

Get the local images in your internal or SD Card using Media Store content provider.

Uri uri = MediaStore.Images.Media.EXTERNAL_CONTENT_URI; 

String[] projection = { MediaStore.Images.Media._ID, MediaStore.Images.Media.BUCKET_ID,MediaStore.Images.Media.BUCKET_DISPLAY_NAME }; 

Cursor cursor = getContentResolver().query(uri, projection, null, null, null);

After that you can iterate the cursor to end and get details.

  • Create a separate folder where edited images will be saved.

Now after editing we have to place the edited image into a seperate folder with a new name. You can create folder like this:

File aviaryFolder = new File(baseDir, FOLDER_NAME);

Generating new file which will be saved as edited image

public static File getEmptyFile(String name) {

  File folder = FileUtils.createFolders();

  if (folder != null) {

     if (folder.exists()) {

        File file = new File(folder, name);

        return file;

     }

  }

  return null;

}
FileUtils.getEmptyFile("edited"

     + System.currentTimeMillis() + ".png");

 

It will save new image as named edited<timeinmillis> so it will be unique.

  • StartActivityforResult with the image URI and edit activity

So firstly, when user open image and click on edit. We create an empty file using the above method. Getting the image uri and pass it into the editImageActivity. Other than Uri, also pass the output file path.

And startActivityForResult with intent and request code.

You can track the editing done by user and finally save if there is some edit done, like in Phimpme we keep a counter which increased on change of Bitmap.

Update the Album by putting the image Media Store

ContentValues values = new ContentValues(2);

String extensionName = getExtensionName(dstPath);

values.put(MediaStore.Images.Media.MIME_TYPE, "image/" + (TextUtils.isEmpty(extensionName) ? "jpeg" : extensionName));

values.put(MediaStore.Images.Media.DATA, dstPath);

context.getContentResolver().insert(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, values);

Please visit the whole code here: github.com/fossasia/phimpme-android

Resources

Continue ReadingAdding Image Editor in Phimpme Android app

Auto Deploying accounts.susi.ai on gh-pages

While migrating web apps from susi server repository to accounts.susi.ai repository. Our team autodeployed accounts.susi.ai on gh-pages branch i.e., each time changes are made to code, it gets auto deployed on gh-pages branch. And changes are automatically live and visible on site.

To do this we have to setup travis in our repository. Travis can be easily set in github repository just by adding .travis.yml file into root directory your repo. Depending on type of repository you are dealing with, the configuration of travis changes. For accounts.susi.ai, It is a written on top of ReactJs framework. So we used the following code.

sudo: required
dist: trusty
language: node_js
node_js:
  - 6
script:
  - npm test
deploy:
  provider: script
  script: "./deploy.sh"
  env:
  global:
  - ENCRYPTION_LABEL: "<.... encryption label from previous step ....>"
  - COMMIT_AUTHOR_EMAIL: "you@example.com"
cache:
  directories:
    - node_modules
branches:
  only:
    - master

 

The above travis configuration file, After every commit checks whether the build is passing or not by running npm test command. After checking the commit, next script deploy.sh is run on the commit. The last line tells us to only execute this script for commits in master branch. deploy.sh can be placed anywhere, we jus need to modify its path in .tavis.yml file. Here is code of deploy.sh :

#!/bin/bash
set -e 

SOURCE_BRANCH="master"
TARGET_BRANCH="gh-pages"

if [ "$TRAVIS_PULL_REQUEST" != "false" -o "$TRAVIS_BRANCH" != "$SOURCE_BRANCH" ]; then
    echo "Skipping deploy; just doing a build."
    doCompile
    exit 0
fi
# Save some useful information
REPO=`git config remote.origin.url`
SSH_REPO=${REPO}
SHA=`git rev-parse --verify HEAD`

git clone $REPO out
cd out
git checkout $TARGET_BRANCH || git checkout --orphan $TARGET_BRANCH
cd ..
git config user.name "Travis CI"
git config user.email "travis-ci@github.com"

if git diff --quiet; then
    echo "No changes to the output on this push; exiting."
    exit 0
fi

# Commit the "changes", i.e. the new version.
# The delta will show diffs between new and old versions.
git add -A .
git commit -m "Deploy to GitHub Pages: ${SHA}"

# Get the deploy key by using Travis's stored variables to decrypt deploy_key.enc
ENCRYPTED_KEY_VAR="encrypted_${ENCRYPTION_LABEL}_key"
ENCRYPTED_IV_VAR="encrypted_${ENCRYPTION_LABEL}_iv"
ENCRYPTED_KEY=${!ENCRYPTED_KEY_VAR}
ENCRYPTED_IV=${!ENCRYPTED_IV_VAR}
openssl aes-256-cbc -K $ENCRYPTED_KEY -iv $ENCRYPTED_IV -in ../deploy_key.enc -out ../deploy_key -d
chmod 600 ../deploy_key
eval `ssh-agent -s`
ssh-add deploy_key

# Now that we're all set up, we can push.
git push $SSH_REPO $TARGET_BRANCH

 

deploy.sh is automatically deploying master on gh pages branch. To perform this task, it needs admin authorisation of github repo. We do this authentication by encrypted keys.
To create encrypted keys we need to generate a new SSH key, SSH keys can be generated by running following command in terminal.

ssh-keygen -t rsa -b 4096 -C "your_email@example.com"

 

Then add this key to your project’s git repository at the github repo of project.

https://github.com/<your name>/<your repo>/settings/keys

Now, We can generate encrypted keys by running following command.

travis encrypt-file deploy_key

 

This command generates deploy_key.enc file, which shall be placed in the repo. Its location needs to be updated in the placeholders of .travis.yml and deploy.sh.

Hence, we achieved the task of auto deployment to gh-pages in accounts.susi.ai. Please ensure that account through which encrypted keys are created, always have admin access to repository for continued deployment.

Continue ReadingAuto Deploying accounts.susi.ai on gh-pages
Read more about the article Scraping in JavaScript using Cheerio in Loklak

Scraping in JavaScript using Cheerio in Loklak

FOSSASIA recently started a new project loklak_scraper_js. The objective of the project is to develop a single library for web-scraping that can be used easily in most of the platforms, as maintaining the same logic of scraping in different programming languages and project is a headache and waste of time. An obvious solution to this was writing scrapers in JavaScript, reason JS is lightweight, fast, and its functions and classes can be easily used in many programming languages e.g. Nashorn in Java.

Cheerio is a library that is used to parse HTML. Let’s look at the youtube scraper.

Parsing HTML

Steps involved in web-scraping:

  1. HTML source of the webpage is obtained.
  2. HTML source is parsed and
  3. The parsed HTML is traversed to extract the required data.

For 2nd and 3rd step we use cheerio.

Obtaining the HTML source of a webpage is a piece of cake, and is done by function getHtml, sync-request library is used to send the “GET” request.

Parsing of HTML can be done using the load method by passing the obtained HTML source of the webpage, as in getSearchMatchVideos function.

var $ = cheerio.load(htmlSourceOfWebpage);

 

Since, the API of cheerio is similar to that of jquery, as a convention the variable to reference cheerio object which has parsed HTML is named “$”.

Sometimes, the requirement may be to extract data from a particular HTML tag (the tag contains a large number of nested children tags) rather than the whole HTML that is parsed. In that case, again load method can be used, as used in getVideoDetails function to obtain only the head tag.

var head = cheerio.load($("head").html());

“html” method provides the html content of the selected tag i.e. <head> tag. If a parameter is passed to the html method then the content of selected tag (here <head>) will be replaced by the html of new parameter.

Extracting data from parsed HTML

Some of the contents that we see in the webpage are dynamic, they are not static HTML. When a “GET” request is sent the static HTML of webpage is obtained. When Inspect element is done it can be seen that the class attribute has different value in the webpage we are using than the static HTML we obtain from “GET” request using getHtml function. For example, inspecting the link of one of suggested videos, see the different values of class attribute :

 

  • In website (for better view):

  • In static HTML, obtained from “GET” request using getHtml function (for better view):

So, it is recommended to do a check first, whether attributes have same values or not, and then proceed accordingly.

Now, let’s dive into the actual scraping stuff.

As most of the required data are available inside head tag in meta tag. extractMetaAttribute function extracts the value of content attribute based on another provided attribute and its value.

function extractMetaAttribute(cheerioObject, metaAttribute, metaAttributeValue) {
	var selector = 'meta[' + metaAttribute + '="' + metaAttributeValue + '"]';
	return cheerioFunction(selector).attr("content");
}

“cheerioObject” here will be the “head” object created above.

For example, our final JSONObject contains a og_url key-value pair, to get that we need to obtain the following html element.

<meta property="og:url" content="https://www.youtube.com/watch?v=KVGRN7Z7T1A">

 

This can be obtained by:

  1. Writing a selector for property attribute of meta. The selector would be ‘meta[property=”og:url”]’.
  2. The selector is passed to cheerioObject.
  3. Then attr method is used to obtain the value of content attribute.
  4. Finally, we set the obtained value of content attribute as the value of JSONObject’s key.

Similarly og:site_name, og:url and other values can be extracted, which in the final JSONObject would be the value of keys og_site_name, og_url and similarly. Since, a lot of data needs to be extracted this way, the extractMetaAttribute function generalizes it, where metaAttribute is “property” and metaAttributeValue is “og:url” in the above example.

If one parameter is provided in attr method, then it is used as a getter method, the value of that attribute is returned. If two parameters are provided then first parameter is the name of attribute and second parameter is the value of attribute, in this case it is used as a setter method.

Now, what if the provided selector matches more than one html element and we need to extract data or perform some operations on all of them. The answer is using each method on the cheerio Object, it iterates over the matched elements and executes the passed function – as a parameter – on them. The passed function has two parameters, the index of matched element and the matched element itself. To break out of the loop early, false is returned.

One of the use case of each method in youtube scraper is to extract related “tags” of the video.

Selector for this would be ‘meta[property=”og:video:tag”]’ and as it is inside a head tag, we can use the already created head tag. Applying the each method, it becomes:

head('meta[property="og:video:tag"]').each(function(i, element) {
    // the logic goes here
});

 

Here for the first iteration the value of “i” will be “0” and “element” will be

<meta property="og:video:tag" content="Iggy">

 

and so on. We need to obtain the value of content attribute, so we can use attr method as used above. Finally all the values are pushed to an array. Hence, the final code snippet with logic.

var ary = [];
head('meta[property="og:video:tag"]').each(function(i, element) {
    ary.push(head(element).attr("content"));
});

 

The same functionality is implemented in extractMetaProperties method.

function extractMetaProperties(cheerioObj, metaProperty) {
	var properties = [];
	var selector = 'meta[property="' + metaProperty + '"]';
	cheerioObj(selector).each(function(i, element) {
		properties.push(cheerioObj(element).attr("content"));
	});
	return properties;}
Continue ReadingScraping in JavaScript using Cheerio in Loklak
Read more about the article Adding Custom Scrollbar to SUSI AI Web Chat

Adding Custom Scrollbar to SUSI AI Web Chat

Scrollbar represents the depth of content on your current screen. It appears when the content has overflown the depth of screen and cannot fit it anymore. We see scrollbars everywhere. By default, the scrollbar provided by the browser is not very attractive but efficient in doing its job.

We decided that as our SUSI.AI Web App is improving in both UI and functionality, let’s add a custom scrollbar to it.

Earlier we had a standard  scrollbar in our SUSI.AI webchat:

For adding a custom scrollbar to our web chat we decided to use react-custom-scrollbars npm-package.

Our reasons to choose this package were:

  • Auto Hide feature in the scrollbar, after a specific period of time, which we can modify too.
  • No requirement for extra CSS styles to style our scrollbar.
  • It is well tested and trusted by many developers in open source

To install this npm package:

npm install -S react-custom-scrollbars

Now comes the usage part, we need to import this into our JavaScript file:

 import { Scrollbars } from 'react-custom-scrollbars';

After importing, wrap it around the data where you want to show a custom scrollbar. In our case it was messageListItems, the code snippet looked like:

<Scrollbars>
 {messageListItems}
</Scrollbars>

This made our scrollbar look much better than the default one:

Now to add Auto Hide functionality to our scrollbar we need to add some attributes to our <Scrollbars>  tag.

    1. autoHide: It allows the auto-hide feature to our scrollbar.
    2. autoHideTimeout: It allows us to set custom time of hiding delay of a scrollbar in milli-seconds.
    3. autoHideDuration: it allows us to set the duration of hiding animation in milliseconds.

After adding the above-mentioned attributes our code changes to:

<Scrollbars
 autoHide
 autoHideTimeout={1000}
 autoHideDuration={200}>
 {messageListItems}
</Scrollbars>

Resources:

A lot more of custom attributes can be found in the documentation of Malte Wessel here.

Testing Link:

Now we had a much better scrollbar for our web chat which can be tested here.

Continue ReadingAdding Custom Scrollbar to SUSI AI Web Chat
Read more about the article Refactor of Dropdown Menu in Susper

Refactor of Dropdown Menu in Susper

The first version of the Susper top menu was providing links to resources and tutorials. In the next version of the menu, we were looking for a menu with more colorful icons, a cleaner UI design and a menu that should appear on the homepage as well. In this blog, I will discuss about refactoring the dropdown menu. This is how earlier dropdown of Susper looks like:

We decided to create a separate component for the menu DropdownComponent.

At first, I created a drop down menu with matching dimensions similar to what Google follows. Then, I gave padding: 28px to create similar UI to market leader. This will make a dropdown menu with clean UI design. I replaced the old icons with colorful icons. In the dropdown we have:

  • Added more projects of FOSSASIA like eventyay, loklak, susi and main website of FOSSASIA. Here how it looks now :

The main problem I faced was aligning the content inside the dropdown and they should not get disturbed when the screen size changes.
I kept the each icon dimensions as 48 x 48 inside drop down menu. I also arranged them in a row. It was easy to use div element to create rows rather than using ul and li tags which were implemented earlier.

To create a horizontal grey line effect, I used the hr element. I made sure, padding remained the same above and below the horizontal line.

At the end of drop down menu, @mariobehling suggested instead of writing ‘more’, it should redirect to projects page of FOSSASIA.

This is how I worked on refactoring drop down menu and added it on the homepage as well.

Resources

Continue ReadingRefactor of Dropdown Menu in Susper
Read more about the article How SUSI AI Searches the Web For You

How SUSI AI Searches the Web For You

SUSI is now capable of performing web search to answer your queries. When SUSI doesn’t know how to answer your queries, it performs a web search on the client side and displays all the results as horizontally swipeable tiles with each tile giving a brief description and also providing a link to the relevant source.

Lets visit SUSI WebChat and try it out.

Query : Search for Google
Response : <Web Search Results>

How does SUSI know when to perform a websearch?

It uses action types to identify if a web search is to be performed or not. The API Response is parsed to check for the action types and if a websearch action type is present, then an API call is made using the duckduckgo api with the relevant query and the results are displayed as tiles with :

  • Category : The Topic related to the given query
  • Text : The result from the websearch
  • Image : Image related to the query if present
  • Link : A url redirecting to the relevant source

Parsing the actions :

Let us look at the API response for a query.

Sample Query: search for google

Response: <API Response>

"actions": [
  {
    "type": "answer",
    "expression": "Here is a web search result:"
  },
  {
    "type": "websearch",
    "query": "google"
  }
]

Note: The API Response has been trimmed to show only the relevant content

We find a websearch type action and the query to be searched as google . So we now make a API call using duckduckgo api to get our websearch results.

API Call Format : api.duckduckgo.com/?q={query}&format=json

API Call for query google : http://api.duckduckgo.com/?q=google&format=json

And from the duckduckgo API response we generate our websearch tiles showing relevant data using the fields present in each object.

This is the sample object from duckduckgo API response under the RelatedTopics , which we use to create our websearch result tiles.

{
  "Result": "<a href=\"https:\/\/duckduckgo.com\/Google\">Google<\/a> An American multinational technology company specializing in Internet-related services and...",
  "Icon": {
    "URL": "https:\/\/duckduckgo.com\/i\/8f85c93f.png",
    "Height": "",
    "Width": ""
  },
  "FirstURL": "https:\/\/duckduckgo.com\/Google",
  "Text": "Google An American multinational technology company specializing in Internet-related services and..."
},

Let us look at the code for querying data from the API

if (actions.indexOf('websearch')>=0) {

  let actionIndex = actions.indexOf('websearch');
  let query = response.answers[0].actions[actionIndex].query;

  $.ajax({
    url: 'http://api.duckduckgo.com/?format=json&q='+query,
    dataType: 'jsonp',
    crossDomain: true,
    timeout: 3000,
    async: false,

    success: function (data) {
      receivedMessage.websearchresults = data.RelatedTopics;

      if(data.AbstractText){
        let abstractTile = {
          Text:'',
          FirstURL:'',
          Icon:{URL:''},
        }
        abstractTile.Text = data.AbstractText;
        abstractTile.FirstURL = data.AbstractURL;
        abstractTile.Icon.URL = data.Image;
        receivedMessage.websearchresults.unshift(abstractTile);
    }

    let message =  ChatMessageUtils.getSUSIMessageData(
receivedMessage, currentThreadID);

    ChatAppDispatcher.dispatch({
      type: ActionTypes.CREATE_SUSI_MESSAGE,
      message
    });
  },

    error: function(errorThrown) {
      console.log(errorThrown);
      receivedMessage.text = 'Please check your internet connection';
    }

  });

}

Here, from the actions object, we get the query needed to search the web. We then make a ajax call using that query to the duckduckgo API. If the API call succeeds then we collect the required data to create tiles as array of objects and store it as websearchresults. and dispatch the message with the websearchresults which gets reflected in the message store and when passed to the components we use it to create the result tiles.

<MuiThemeProvider>
  <Paper zDepth={0} className='tile'>
    <a rel='noopener noreferrer'
    href={tile.link} target='_blank'
    className='tile-anchor'>
    {tile.icon &&
    (<Paper className='tile-img-container'>
      <img src={tile.icon}
      className='tile-img' alt=''/>
     </Paper>
    )}
  <Paper className='tile-text'>
    <p className='tile-title'>
      <strong>
        {processText(tile.title,'websearch-rss')}
      </strong>
    </p>
    {processText(tile.description,'websearch-rss')}
  </Paper>
  }
  </a>
  </Paper>
</MuiThemeProvider>

We then display the tiles as horizontally swipeable carousel ensuring a good and interactive UX.

React-Slick module was used to implement the horizontal swiping feature.

function renderTiles(tiles){

if(tiles.length === 0){
  let noResultFound = 'NO Results Found';
  return(<center>{noResultFound}</center>);
}

let resultTiles = drawTiles(tiles);

var settings = {
  speed: 500,
  slidesToShow: 3,
  slidesToScroll: 1,
  swipeToSlide:true,
  swipe:true,
  arrows:false
};

return(
    <Slider {...settings}>
      {resultTiles}
    </Slider>
);

}

Here we are handling the corner case when there are no results to display by rendering `NO Results found`. We then have our web search results displayed as swipeable tiles with a image, title, description and link to the source.

This is how SUSI performs web search to respond to user queries ensuring that no query goes unanswered! Don’t forget to swipe left and go through all the results displayed!

Resources

Continue ReadingHow SUSI AI Searches the Web For You
Read more about the article React Routes to Deploy 404 page on gh-pages and surge

React Routes to Deploy 404 page on gh-pages and surge

Web applications need 404 page to handle broken urls. If we can have a productive 404 page we can keep users with our application. This is how we made 404 page to SUSI.AI web chat application.
React routes ?
We use react routes to navigate throughout the application.we have to define which page to go from each and every route. If user is trying to go to broken Link application should show 404 page.
In the SUSI Web Chat application we have setuped routes in index.js file which is on the root of the application.

<Router history={hashHistory}>
        <MuiThemeProvider>
            <Switch>
                <Route exact path="/" component={ChatApp} />
                <Route exact path="/signup" component={SignUp} />
                <Route exact path="/logout" component={Logout} />
                <Route exact path="/forgotpwd" component={ForgotPassword} />
                <Route exact path="*" component={NotFound} />
            </Switch>
        </MuiThemeProvider>
    </Router>

 

“<Route exact path=”*” component={NotFound} />”  This line defines the 404 page route . this should be defined after all other routes. Because application should first search for defined routes.If the requested route is not defined, Application should show the 404 route.
To use these JSX elements we have to import this dependency on top of the index.js

import {
    BrowserRouter as Router,
    Route,
    Switch,
    hashHistory
} from 'react-router-dom';

 

After you define like this It will work correctly on your localhost after you deployed it on gh-pages or surge it will not be work there as we wish.
When we try to access URL directly or when we try to access wrong URL it will redirect to default github 404 page.

After we built our application we get static index.html file and another set of files.when we try to access chat.susi.ai it will load index.html file. If we access another url it will automatically loads default github pages 404 page.
So we can do a little hack for use direct URLs like this .
We can add same copy of index.html file as 404.html then user tries to go to different URL instead of chat.susi.ai It loads our 404.html file since it contains all other routes it redirects to the correct route.if there is no matching route it shows our 404 page instead of default github pages 404 page.
Since all our deployment tasks are handle by we have to add this actions into deploy.sh file like this.

rm -rf node_modules/	 
mv ../build/* .
cp index.html 404.html   <--- this part

 

Then it will create 404.html file with the content of index.html file after each and every commit.
If you need to do the same thing on surge.sh (we use surge for show previews of every PR ) we have to add a copy of index.html file as 200.html .
You can do this after you run the

npm run deploy

 

Just before giving the deployment URL you need to create a copy of index.html file on build folder and it should be renamed as 200.html and continue.now it will work as we wish. This is all for today.

Resources

  • Read More – Adding a 200 page for client-side routing : https://surge.sh/help/adding-a-200-page-for-client-side-routing
Continue ReadingReact Routes to Deploy 404 page on gh-pages and surge
Read more about the article How SUSI AI Tabulates Answers For You

How SUSI AI Tabulates Answers For You

SUSI is an artificial intelligence chat bot that responds to all kinds of user queries. It isn’t any regular chat bot replying in just plain text. It supports various response types which we refer to as ‘actions’. One such action is the “table” type. When the response to a user query contains a list of answers which can be grouped, it is better visualised as a table rather than plain text.

Lets visit SUSI WebChat and try it out. In our example we ask SUSI for the 2009 race statistics of British Formula 1 racing driver Lewis Hamilton.

Query: race stats of hamilton in f1 season 2009

Response: <table> (API response)

 

 

How does SUSI do that? Let us look at the skill teaching SUSI to give table responses. 

# Returns race stats as a table

race summary of  * in f1 season *|race stats of  * in f1 season *
!console:
{
  "url":"http://ergast.com/api/f1/$2$/drivers/$1$/status.json",
  "path":"$.MRData.StatusTable.Status",
  "actions":[{
     "type":"table",
     "columns":{"status":"Race Status","count":"Number Of Races"}
   }]
}
eol

Here, we are telling SUSI that the data type is a table through type attribute in actions and also defining column names and under which column each value must be put using their respective keys. Using this information SUSI generates a response accordingly with the table schema and data points.

How do we know when to render a table?

We know it through the type attribute in the actions from the API response.

"actions": [{
  "type": "table",
  "columns": {
    "status": "Race Status",
    "count": "Number Of Races"
  },
  "count": -1
  }]
}],

We can see that the type is table so we now know that we have to render a table.

But what is the table schema? What do we fill it with?

There is a columns key under actions and from the value of the columns key we get a object whose key value pairs give us column names and what data to put under each column.

Here, we have two columns – Race Status and Number Of Races

And the data to put under each column is found in answers[0].data with same keys as those for each column name i.e ‘status’ and ‘count’.

Sample data object from the API response:

{
  "statusId": "2",
  "count": "1",
  "status": "Disqualified"
}

The value under ‘status’ key is ‘Disqualified’ and the column name for ‘status’ key is ‘Race Status’, so Disqualified is entered under Race Status column in the table. Similarly 1  is entered under Number Of Races column. We thus have a row of our table. We populate the table for each object in the data array using the same procedure.

let coloumns = data.answers[0].actions[index].columns;
let count = data.answers[0].actions[index].count;
let table = drawTable(coloumns,data.answers[0].data,count);

We also have a ’count’ attribute in the API response . This is used to denote how many rows to populate in the table. If count = -1 , then it means infinite or to display all the results.

function drawTable(coloumns,tableData,count){

let parseKeys;
let showColName = true;

if(coloumns.constructor === Array){
  parseKeys = coloumns;
  showColName = false;
}
else{
  parseKeys = Object.keys(coloumns);
}

let tableheader = parseKeys.map((key,i) =>{
return(<TableHeaderColumn key={i}>{coloumns[key]}</TableHeaderColumn>);
});

let rowCount = tableData.length;

if(count > -1){
  rowCount = Math.min(count,tableData.length);
}

let rows = [];

for (var j=0; j < rowCount; j++) {

  let eachrow = tableData[j];

  let rowcols = parseKeys.map((key,i) =>{
    return(
        <TableRowColumn key={i}>
          <Linkify properties={{target:'_blank'}}>
            {eachrow[key]}
          </Linkify>
        </TableRowColumn>
      );
  });

  rows.push(
      <TableRow key={j}>{rowcols}</TableRow>
  );

}

const table =
  <MuiThemeProvider>
    <Table selectable={false}>
      <TableHeader displaySelectAll={false} adjustForCheckbox={false}>
        { showColName && <TableRow>{tableheader}</TableRow>}
      </TableHeader>
      <TableBody displayRowCheckbox={false}>{rows}</TableBody>
    </Table>
  </MuiThemeProvider>

return table;

}

Here we first determine how many rows to populate using the count attribute and then parse the columns to get the column names and keys. We then loop through the data and populate each row.

This is how SUSI responds with tabulated data!

You can create your own table skill and SUSI will give the tabulated response you need. Check out this tutorial to know more about SUSI and the various other action types it supports.

Resources

Continue ReadingHow SUSI AI Tabulates Answers For You

How to Make SUSI AI Slack Bot

To make SUSI slack bot we will use real time messaging api of slack which will allow users to receive messages from bot in real time. To make SUSI slack bot you have to follow following steps:

Steps:

  1. First of all you have to create a team on slack in where your bot will be running. To create a team go to https://slack.com/ and create a new team.
  2. After creating sign in to your team and got to apps and integration option by clicking on left corner.
  3. Click manage on top right corner and go to custom integrations to add configuration to Bots.
  4. After adding configuration data,bot username and copying API Token now we have to write code for setting bot in slack. To set up code see below steps: 
  5. Install Node.js from the link below on your computer if you haven’t installed it already. https://nodejs.org/en/
  6. Create a folder with any name and open shell and change your current directory to the new folder you created.
  7. Type npm init in command line and enter details like name, version and entry point.
  8. Create a file with the same name that you wrote in entry point in above given step. i.e index.js and it should be in same folder you created.
  9. Type following commands in command line  npm install –save @slack/client. After slack/client is installed type npm install –save express after express is installed type npm install –save request and then npm install –save http when all the modules are installed check your package.json modules will be included within dependencies portion.
  10. Your package.json file should look like this. 
    {
"name": "slack-bot",
"version": "1.0.0",
"description": "SUSI Slack Bot",
"main": "index.js",
"dependencies": {
       "express": "^4.15.3",
       "http": "0.0.0",
       "request": "^2.81.0"
},
"scripts": {
       "test": "echo \"Error: no test specified\" && exit 1",
       "start": "node index.js"
}
}
  11. Copy following code into file you created i.e index.js. 
    var Slack = require('@slack/client');
var request = require('request');
var express = require('express');
var http = require('http');
var app = express();
var RtmClient = Slack.RtmClient; 
var RTM_EVENTS = Slack.RTM_EVENTS;
var token = process.env.APIToken;

var rtm = new RtmClient(token, { logLevel: 'info' }); 
rtm.start();

//to ping heorku app after 20 minutes to keep it active

setInterval(function() {
        http.get(process.env.HerokuUrl);
    }, 1200000);

rtm.on(RTM_EVENTS.MESSAGE, function(message) { 
var channel = message.channel;

var options = {
       method: 'GET',
       url: 'http://api.asksusi.com/susi/chat.json',
       qs: {
           timezoneOffset: '-330',
           q: message.text
       }
   };

//sending request to SUSI API for response
   request(options, function(error, response, body) {
       if (error) throw new Error(error);
       var ans = (JSON.parse(body)).answers[0].actions[0].expression;
       rtm.sendMessage(ans, channel);
   })
});

const port = process.env.PORT || 3000;
app.listen(port, () => {
   console.log(`listening on ${port}`);
});
 


  12. Now we have to deploy this code to heroku.
  13. Before deploying we have to make a github repository for chatbot to make github repository follow these steps:

    In command line change current directory to folder we created above and write

    git init
    git add .
    git commit -m”initial”
    git remote add origin <URL for remote repository>
    git remote -v
    git push -u origin master

    You will get URL for remote repository by making repository on your github and copying this link of your repository.

  14. To deploy your bot to heroku you need an account on Heroku and after making an account make an app.
  15. Deploy app using github deployment method.
  16. Select Automatic deployment method.
  17. Add APIToken and HerokuUrl variable to heroku app in settings options.
  18. Your SUSI Slack bot is ready enjoy chatting with it.If you want to learn more about slack API refer to https://api.slack.com
Continue ReadingHow to Make SUSI AI Slack Bot
Read more about the article OpenLayers 3 Map that Animates Emojis Using LokLak API

OpenLayers 3 Map that Animates Emojis Using LokLak API

OpenLayers3 maps are fully functional maps which offer additional interactive features. In the Emoji Heatmapper app in Loklak Apps, I am using interactive OpenLayers3 maps to visualize the data. In this blog post, I am going to show you how to build an OpenLayers 3 map that animates emojis according to the query entered and location tracked from the LokLak Search API.

We start with a simple map using just one background layer in a clean style.

var map = new ol.Map({
target: 'map',  // The DOM element that will contains the map
renderer: 'canvas', // Force the renderer to be used
layers: [
// Add a new Tile layer getting tiles from OpenStreetMap source
new ol.layer.Tile({
    source: new ol.source.OSM()
}),
vectorLayer
],
// Create a view centered on the specified location and zoom level
view: new ol.View({
    center: ol.proj.transform([2.1833, 41.3833], 'EPSG:4326', 'EPSG:3857'),
    zoom: 2
})
});

 

Sample Output which displays map:

The data set for the locations of tweets containing emoji in them are tracked using search API of LokLak, which is in the form of simplified extract as JSON file. The file contains a list of coordinates named as location_point, the coordinate consists of lat and long values. With the coordinates, we will create a circle point i.e.,marker on the map showing where the emoji have been recently used from the tweets posted.

In the callback of the AJAX request we loop through the list of coordinates. The coordinate of the resulting line string are in EPSG:4326. Usually, when loading vector data with a different projection, OpenLayers will automatically re-project the geometries to the projection of the map. Because we are loading loading the data ourself, we manually have to transform the line to EPSG:3857. Then we could add the feature to the vector source.

for(var i = 0; i < tweets.statuses.length; i++) {
        if(tweets.statuses[i].location_point !== undefined){
            // Creation of the point with the tweet's coordinates
            //  Coords system swap is required: OpenLayers uses by default
            //  EPSG:3857, while loklak's output is EPSG:4326
            var point = new ol.geom.Point(ol.proj.transform(tweets.statuses[i].location_point, 'EPSG:4326', 'EPSG:3857'));
            vectorSource.addFeature(new ol.Feature({  // Add the point to the data vector
                geometry: point
            }));
        }
    }
});

 

Markers on the Map:

We can also style the markers which gets rendered onto the map using the feature ol.style.Style provided by OpenLayers.

var style = new ol.style.Style({
    stroke: new ol.style.Stroke({
        color: [64, 200, 200, 0.5],
        width: 5
    }),
    text: new ol.style.Text({
        font: '30px sans-serif',
        text: document.getElementById('searchField').value !== '' ? document.getElementById('searchField').value : '', //any text can be given here
        fill: new ol.style.Fill({
            color: [64, 64, 64, 0.75]
        })
    })
});

 

Styled Markers on the Map:

So these were a few tips and tricks to use the interactive OpenLayers3 Maps.

The full code of the example is available here.

Resources:

Continue ReadingOpenLayers 3 Map that Animates Emojis Using LokLak API