aibotsacademy / covidcenter-bot Goto Github PK

In this tutorial, we will be creating an API-based bot that give information covid to your account. The app will be able to process the user's text and respond to the user data about covid that they want. The key things we will explore is how to:

• Design the user interaction and Architecture
• Create and train a Wit app to do natural language processing (NLP)
• Integrate Wit with your Messenger Bot

• Create a Wit.ai account
• Clone this repository Wit.ai Covid Center Demo from GitHub
• Create Facebook Page | Link
• Create Facebook App | Tutorial
• Download & Install Ngrok Link

Ngrok is reverse proxy, it allow you deploy at local with random link.

When designing applications with interactions, it's important to understand the various ways that a user may interact with your app. Some techniques that can help with modeling the conversation is writing a script or creating a flow diagram. For our covid app, let's write a script to outline it.

Let's consider the following conversation as the happy path:

User: "Selamat Pagi, Ini bot buat apa ya"

Wit:  "Maaf kak, cocid tidak mengerti :(. Jika ingin mengetahui info terbaru covid silahkan mengetik jumlah covid di kota kakak. contoh : jumlah covid di Jakarta"

User: "jumlah covid di Jakarta"

Wit:  "jumlah covid di jakarta adalah 2 kasus, 1 konfirmasi, 1 meninggal.\n
jika anda mengalami gejala berikut indra perasa hilang, Susah bernafas, demam tinggi, batuk kering, kelelahan
segera lakukan pengecekkan lebih lanjut di rumah sakit rujukan dan setelah lakukan test, jika positif
di sarankan untuk melakukan karantina mandiri selama 14 hari dirumah anda. \n\n berikut artikel cara isolasi mandiri
yang baik dan benar : https://kumparan.com/kumparannews/cara-karantina-mandiri-protokol-corona-1tCEvhtt8LG
berikut rumah sakit rujukan di jakarta :\n
1. rumah sakit Umum Fatmawati (https://goo.gl/maps/GV6fZRxhEgg2PPjK7)\n
2. rumah sakit Jakarta Medical Centre (https://goo.gl/maps/oPnpyw2edFJcg3ha7)\n
3. rumah sakit Umum Andhika (https://g.page/rsuandhika?share)`?"

User: "Alhamdulillah" / (send a location)

Wit: "Mantap, kalau kamu merasa baik !! tetap social distancing yaaa :D"

There are many other scenarios to consider as well, but for the tutorial let's just focus on these.

Before we train our Wit app, we should learn about intents, entities, traits, and utterances. If you already learn those terms, you can go to Next section

Case Study: We want to understand what our end-user wants to perform. For example:

• Ask about the weather
• Book a restaurant
• Open the garage door

The problem is that there a millions of different ways to express a given intent. For instance, all the following expressions should be mapped to the same intent:

"What is the weather in Paris?" "Give me tomorrow weather in Paris." "Is it sunny or rainy in Paris now?"

Those expressions are asking about the weather intent. How about entities ? Entities is object that referred in the intent of sentence.

"What is the weather in Paris ?" Paris is city where we ask about the weather for.

"Give me tomorrow weather in Paris." Tomorrow is time when we ask about the weather for.

"Is it sunny or rainy in Paris now?" And sunny and rainy are options what we ask about he weather.

The entities make machine understand what object that related with the intent. example: "Give me tomorrow weather in Paris."

Intent: Ask about the weather , Entities: City: Paris Time: Tomorrow Machine could query to the database in table weather(intent) with paris city and tomorrow queries (entities)

So what is trait ? Trait is tendency of an intent. We could give an example of this like sentiment on reaction_intent.

"Sad" (negative) "OMG :(" (negative) "I can't believe this. I'm crying" (negative) "Superb" (positive)

Utterances is sample data which define a sentence to be categorized to an intent and have entities and traits. This term will be used to train data, for example:

Now that we are understand, let’s train our Wit app to process the user’s response to the app.

Wit AI has two method for training the NLP. The first is inserting utterances with web interface. The second one is inserting utterances with API.

We would like to introduce to you all for two methods. But, because data is supossed to be large, we emphasize the API method more than Web Interface method in this tutorial.

We are going to create intents to define what the user's utterance for our wit.AI application will understand. On the dashboard click on intents, click +Intents to add a new intents.

How to produce sentiment

1. Select Understanding from the left nav and add a sentence you want to do sentiment analysis on.

This is amazing!

2. Click Add Trait. Enter the name of the trait, such as "sentiment".
3. Click the value selector to create a value "positive".
4. Validate your sentence.
5. Select Traits from the left nav and select the trait you just created.
6. In the values section, add more values such as "negative" and "neutral".
7. Annotate a few more examples to get more accurate results!

Update: for English apps, you can use our wit/sentiment built-in! It should already appear in your traits dropdown when you click Add Trait.

Your "sentiment" is a trait, which means that it depends on the utterance as a whole (as opposed to a particular word or sequence of words appearing in the sentence). If it is inferred from a keyword or a specific phrase, use an entity instead. Your "sentiment" trait is trained by and for you. The good news is, it will be completely customized to your case. The bad news is, you need to provide several examples for each value :).

For more information on this, see the Quick Start guide.

Before we implement, we should read Wit.AI API Documentation first.

After we understand the API, open the Wit.ai Covid Center init data script

Update the init-data/sentiment.tsv and add

Alhamdulillah    sentiment   positif
Kabar buruk   sentiment   negatif
Tidak menyenangkan   sentiment   negatif
Sedih akutu   sentiment   negatif
Huhuhuhu   sentiment   negatif

Get Your Seed Token

In order to start using the Wit.ai API, we need to start with some identification. Make sure you have signed up for Wit.ai if you haven't already.

Once you have:

1. Go to the Settings page of the Wit console
2. Copy the Server Access Token

This will be the base token we will use to create other apps. In the code this will be under the variable NEW_ACCESS_TOKEN.

Next update NEW_ACCESS_TOKEN and APP_ID in shared.js variable to run the as follows:

const NEW_ACCESS_TOKEN = '' // TODO: fill this in 
const APP_ID = ''; // TODO: fill this in

The script is reading data from tsv and hit Utterances API. In this script, we use doubletab to enable data with tab and node fetch to hit api. We could change utterances constractor and the map for another needs if we want to train another data.

// read data with `\n` splitting
const data = fs
  .readFileSync(fileName, 'utf-8')
  .split('\n')
  .map((row) => row.split(DOUBLETAB))

// mapping 3 column into construct function
const samples = data.map(([text, trait, value]) => {
  // utterances constractor
  return {
    text: text,
    intent: intentName,
    entities: [],
    traits: [
      {
        trait: trait,
        value: value,
      },
    ],
  }
});

// hit and log the response
validateUtterances(samples).then((res) => console.log(res))

// hit utterances API https://wit.ai/docs/http/20200513/#post__utterances_link
function validateUtterances(samples) {
  console.log(JSON.stringify(samples))
  return fetch(`https://api.wit.ai/utterances?v=${APP_ID}`, {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${NEW_ACCESS_TOKEN}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(samples),
    })
    .then(res => res.json())
}

After you understand the pre-script Run the file with:

  node init-data/index.js

Open the Wit.ai Covid Center init data script

Update the test.tsv and add

Alhamdulillah, banyak yg sembuh    sentiment   positif
Kabar jelek   sentiment   negatif
Tidak senang   sentiment   negatif
Sedih bgt akutu   sentiment   negatif
Huhuhuuhuuuuu   sentiment   negatif

Run the file with:

  node init-data/test.js

When you download the Tutorial from the init data branch, the app will be capable of doing text and answer with sentiment intent. In this part, we will add how retrieve covid

Before you begin, you will need to create a few things. Please ensure you have all of the following:

• Facebook Page: A Facebook Page will be used as the identity of your Messenger experience. When people chat with your app, they will see the Page name and the Page profile picture. To create a new Page, visit https://www.facebook.com/pages/create
• Facebook Developer Account: Your developer account is required to create new apps, which are the core of any Facebook integration. You can create a new developer account by going to the Facebook Developers website and clicking the 'Get Started' button.
• Facebook App: The Facebook app contains the settings for your Messenger experience, including access tokens. To create a new app, visit your app dashboard.

Add the Messenger Platform to your Facebook app

1. In the sidebar of your app settings under 'PRODUCTS', click '+ Add Product'.
2. Hover over 'Messenger' to display options.
3. Click the 'Set Up' button. The Messenger Platform will be added to your app, and the Messenger settings console will be displayed.

Open the Wit.ai Covid Center bot demo int and run

Get ACCESS_TOKEN and VERIFY_TOKEN from Your App. See Webhook Setup for further reference.

Next update ACCESS_TOKEN and VERIFY_TOKEN variable to get webhook as follows:

const ACCESS_TOKEN = '' // line 11
let VERIFY_TOKEN = '' // line 74

Now that you have all the code in place for a basic webhook, it is time to test it by sending a couple sample requests to your webhook running on localhost.

1. Run the following on the command line to start your webhook on localhost:

node bot/index.js

2. From a separate command line prompt, test your webhook verification by substituting your verify token into this cURL request:

curl -X GET "localhost:1337/webhook?hub.verify_token=<YOUR_VERIFY_TOKEN>&hub.challenge=CHALLENGE_ACCEPTED&hub.mode=subscribe"

If your webhook verification is working as expected, you should see the following:

• WEBHOOK_VERIFIED logged to the command line where your node process is running.
• CHALLENGE_ACCEPTED logged to the command line where you sent the cURL request.

3. Test your webhook by sending this cURL request:

curl -H "Content-Type: application/json" -X POST "localhost:1337/webhook" -d '{"object": "page", "entry": [{"messaging": [{"message": "TEST_MESSAGE"}]}]}'

If your webhook is working as expected, you should see the following:

• TEST_MESSAGE logged to the command line where your node process is running.
• EVENT RECEIVED logged to the command line where you sent the cURL request.

Ngrok is reverse proxy, it allow you deploy at local with random link.

Download & Install Ngrok | Link

Run ngrok:

ngrok http 1337

We could access your API from this example is : https://3c37b05d146e.ngrok.io (this is random url from ngrok, you will generate another link)

After we get https webhook url, and working Wit.API, we change our facebook app webhook and connect our working wit api.

Configure the webhook for your app

1. In the 'Webhooks' section of the Messenger settings console, click the 'Setup Webhooks' button.
2. In the 'Callback URL' field, enter the ngrok URL for your webhook. (example: https://3c37b05d146e.ngrok.io)
3. In the 'Verify Token' field, enter the verify token for your webhook. See Step 4 of Webhook Setup for further reference.
4. Click 'Verify and Save' to confirm you callback URL.

The Built-in NLP integration with the Page inbox lets you create a Wit.ai app automatically and bootstrap it with past conversations from your Page directly from the Facebook app console settings. These samples that are compiled into your Wit.ai app are based on real conversations your Page has had with your users.

To try the Built-in NLP Page Inbox feature with your Messenger experience, do the following:

1. In your app settings, go to Messenger > Settings.
2. Enable built-in NLP for your app.
3. In the 'Select a Model' dropdown, select 'Custom'.
4. Click 'Link to existing Wit App'
5. Choose your app
6. Insert your Wit Server Access Token See how to get Wit Access Token

Now, after you set your webhook and NLP, you could try your chatbot.

We are going to create intents to define what the user's utterances for our wit.AI application will understand. On the dashboard click on intents, click +Intents to add a new intents.

Next, we create training intents, entities and utterances which the user will likely do on the understanding menu. Add a utterance:

1. Make sure you are in Train Your App page by click Understanding on top right menu.
2. enter jumlah covid di jakarta into Utterance text box.
3. Label it your entitites into utterence by highlight covid and enter covid_intents, klik Create Intents as a entitites dan highlight again jakarta and choice wit/location.
4. Submit your first utterance by click Train and Validate. Training will be start a few seconds - you can check the status training on top right corner.

To find out whether our training has been successful, you can try to re-enter words related to the training we are doing, namely Covid and Jakarta and make sure the confidence reaches above 90% to test the validity of our intentions.

$ curl -XGET "https://api.wit.ai/utterances?v=$APPID&limit=10" -H "Authorization: Bearer $YOURTOKEN"

You may have heard that the most important part of machine learning is data training. At this step, we're only providing our Wit app with a single data point, so let's think about the natural variations the user might respond to and repeat steps # 4 through # 5.

Now We go to the next step: how to input automatically your utterances with large data training. You can check this step:

1. You can clone our github See how to input Utterance
2. Now you can see file in Dataset covid intent.tsv, that is file tsv dataset for training our apps.
3. Update and add your own data for example, you can see the init-data/datasets/covid_intent.tsv

your_covid_intent.tsv

rumah sakit rujukan di Bogor   location   23   27
gejala covid    covid   0   6

4. Next, we need init data script for training your data. you can check this script:

init-data/covid_intents.js

covid_intents.js

const fs = require('fs');
const fetch = require('node-fetch');
const { validateUtterances } = require('../shared')

const DOUBLETAB = '   ';
const fileName = 'init-data/covid_intent.tsv'
const intentName = 'covid_intents'
const entityName = 'covid:covid'
const data = fs
  .readFileSync(fileName, 'utf-8')
  .split('\r\n')
  .map((row) => row.split(DOUBLETAB))

const samples = data.map(([text, value, start, end]) => {
  return {
    text: text,
    intent: intentName,
    entities: [
        {
            entity: entityName,
            start: start,
            end: end,
            body: value,
            entities: [],
        }
    ],
    traits: [],
  }
});

validateUtterances(samples).then((res) => console.log(res))

we have text, value, start, and end that we can check again from our covid_intent.tsv. we can see these terms explanation below:

• text is an utterance how the user is likely to chat on Facebook messenger. In our tsv file, the first word of row is the utterance of user messages. ex: "covid di Jaksel"

• value is an entity how our application will learn the word that we highlighted. we are training it within the utterance. In covid_intent.tsv, the second word of row is entity. ex: "covid"

• start is the starting index highlighted entity within the text. We can see on third column.

• end is the ending index highlighted entity within the text. We can see on last column.

The first thing that you have to do is listing your bot behaviours and responses that come from entities that you have.

We have covid_intents which have two entities.

1. Covid entities
2. Location entities

In order to give covid result based on area, the intent should fulfill both entities. If one of them is not exist, we must asking user for the missing items.

But, in order to make this bot simpler, we avoid Finite State Machine. So we give response how user should send text to bot.

Therefore, this is code you must add to bot/index.js in your branch covid template.

bot/index.js

// default response
const DEFAULT_RESPONSE = 'Maaf kak, cocid tidak mengerti :(. Jika ingin mengetahui info terbaru covid silahkan mengetik jumlah covid di kota kakak. contoh : jumlah covid di Jakarta'

function getMessageFromNlp(nlp) {
  // intents checker
  if (nlp.intents.length == 0) {
    return DEFAULT_RESPONSE
  }

  // switch case the intent.
  switch (nlp.intents[0].name) {
    case 'covid_intents':
      return getCovidResponse(nlp.entities)
    case 'sentiment_intent':
      return getSentimentResponse(nlp.traits.sentiment)
    default:
      return DEFAULT_RESPONSE
  }
}

function getCovidResponse(entities) {
  console.log(entities["covid:covid"]);
  var city = ''
  var isCovid = false
  // iterate to find covid entities and location entities.
  entities['covid:covid'].forEach(function(c) {
    if (c.value == 'location') {
      city = c.body
    }
    if (c.value == "covid") {
      isCovid = true
    }
  })

  if (isCovid && city != '') {
    // covid response when covid and city is available.
    var totalCase = getRandomNumber(1,100)
    var confirmCase = getRandomNumber(1, totalCase)
    return `jumlah covid di ${city} adalah ${totalCase} kasus, ${confirmCase} konfirmasi, ${totalCase - confirmCase} meninggal.\n
jika anda mengalami gejala berikut indra perasa hilang, Susah bernafas, demam tinggi, batuk kering, kelelahan
segera lakukan pengecekkan lebih lanjut di rumah sakit rujukan dan setelah lakukan test, jika positif
di sarankan untuk melakukan karantina mandiri selama 14 hari dirumah anda. \n\n berikut artikel cara isolasi mandiri
yang baik dan benar : https://kumparan.com/kumparannews/cara-karantina-mandiri-protokol-corona-1tCEvhtt8LG
berikut rumah sakit rujukan di ${city}:\n
1. rumah sakit Umum Fatmawati (https://goo.gl/maps/GV6fZRxhEgg2PPjK7)\n
2. rumah sakit Jakarta Medical Centre (https://goo.gl/maps/oPnpyw2edFJcg3ha7)\n
3. rumah sakit Umum Andhika (https://g.page/rsuandhika?share)`
  } else if (isCovid) {
    // response when location is not provided (ask the location and give how they should give message)
    return 'Maaf kak, cocid ingin tahu covid di daerah apa ya kak? sebagai contoh kakak bisa mengetik kembali jumlah covid di kota kakak'
  }

  return DEFAULT_RESPONSE;
}

// random the total case 
function getRandomNumber(start, end) {
  return Math.floor(Math.random() * end-start) + start
}

and change the function getSentimentResponse to getMessageFromNlp on post webhook.

- text: getSentimentResponse(message.nlp.traits.sentiment),
+ text: getMessageFromNlp(message.nlp),

Now you can try your bot on facebook messenger, repeat step Deploy your webhook from start to finish your setup messenger.

Enjoy, and hack your bot !!! 🤖 📱

🏆🏆🏆

As you are testing the app, you might notice that certain utterances are not resolving to the proper intents. To address this, go to Wit.ai and on the Understanding page you should see utterances that have been sent to the API endpoint. You can review each utterance by expanding one and making sure that the entity is properly identified and resolving to the correct intent. If there are utterances not relevant to your use case (invalid utterances), you can mark them as Out of Scope.

For demonstration purposes, we’ve created a very simple covid bot, but you can create a much more engaging and interactive bot. Try sketching out a larger conversation flow with various scenarios and see Wit documentation to learn more about other Wit features e.g. other built-in entities, custom entities, and traits.

If you want to add features, we have recommendation feature to add:

1. Finite State Machine link.
2. Integrate with Covid API.
3. More data to train.
4. Use time entities.

We look forward to what you will develop! To stay connected, join the Wit Hackers Facebook Group and follow us on Twitter @FBPlatform.

• Wit Documentation
• Wit GitHub
• Wit Blog

Just make issue and PR if you want to contribute. we will review your PR.

Wit.ai Covid Center Bot is licensed, as found in the LICENSE file.