Category: Chat

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https://yugasa.com/mobile-apps/apps-for-healthcare-professionals-amazing-concepts-for-2021/ “Health is a man’s true wealth,” according to an old saying. We’ve heard it so many times from our professors, parents, and doctors that we’ve completely forgotten what it means. But it’s the same source that people criticize these days for damaging the health that reminds us of its importance: smartphones. As a result, healthcare software becomes ingrained in your everyday routine. We’ve compiled a list of some of the top healthcare app ideas that you may employ. App for Making Appointments Appointment scheduling applications are incredibly efficient and are typically created for the benefit of doctors. Various doctors and health specialists created their own doctor appointment scheduling app. Patients can use this app to verify the doctor’s availability and schedule an appointment. Creating a social platform for both doctors and patients is another excellent idea. Patients can contact doctors here and request a consultation, make a phone call, or book a home visit, depending on their health concerns. Women’s Health and Wellness App If you’re worried about women’s health, all you need for your startup is an app that focuses solely on keeping women’s health at its finest. You can create a healthcare app to help women track their menstrual cycle, treat a medical problem, or discover inner peace. In the medical field, there are many creative concepts for women’s health. Consider an app that performs the functions of a digital clinic, but solely for women. Your app might include a comprehensive list of dietitians, therapists, gynecologists, and other professionals. It could be the finest place for women to ask professional doctors questions and obtain treatment. You may also integrate text and video chat, where ladies can seek advice on any health concern. App for Lifestyle Diseases in Healthcare One of the top healthcare app ideas is Chronic Disease Management, often known as Lifestyle Disease Management. Chronic disorders, such as high blood pressure, kidney disease, obesity, and others, necessitate ongoing medical care and regular exams. As a result, any startup should consider developing lifestyle disease management software. You can allow users to track their health at regular intervals and provide a list of medical specialists for a specific ailment with this type of software. You may also create a database by collaborating with local service providers like clinics and doctors. The patient will be able to respond immediately as a result of this. Finally, you can integrate IoT into your healthcare app to take your app to the next level. Doctors will be able to keep in touch with their patients as well as perform remote monitoring and virtual meetings as a result of this. submitted by /u/YugasaSoftwareLabs [link] [comments]

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Includes instructions and a link to use it!

This nasty virus has taken something from everyone. For myself and my partner, it was fawning over strangers’ dogs. To help fill that void, I made a bot in ~20 minutes with Zapier that sends dog pictures with a custom message every morning.

Although I use it for sending dog pics, you can use it to send any files from Google Drive through text, as often as you want. You can use the bot yourself with this link.

What is Zapier?

Zapier is a Software as a Service (SaaS) company that allows anyone to connect software and websites to automate workflows. It has thousands of templates that you can customize, from lead management to automating your calendar. They have a free plan that includes up to 5 Zaps (automations) a month, so anybody can use this bot for free.

You can use this link to see the many apps that Zapier can automate.

What do you need?

To use this bot, you need these accounts:

• Google Drive
• Zapier
• A phone number to send the files to (you’re not sending them to my girlfriend)

Zapier says that you need a phone number from the US or UK, but Canadian phone numbers also worked for me.

How does it work?

Collect the files

First, you need files to send. I downloaded dog pictures from stock image sites, one for each day of the year. When the files were downloaded, I numbered them based on the date (using keyboard shortcuts, this only took a minute).

Set a schedule

Using the ‘Schedule’ module, I set the bot to trigger at 6am each morning and run 7 days a week. Make sure to change your time zone!

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Find the file for today

Using the ‘Google Drive’ module, you can choose to ‘Find a File’ on your desired schedule. Connecting to your Google Drive account is as simple as logging in and selecting your desired folder.

The bot fuses the current day and month to find the name of the file that corresponds to today in your Google Drive.

Connect to the phone

The ‘Send SMS’ module sends a message of your choosing after the person validates their phone number. There is no easy way to directly send the picture in the text message, so I opted to send the ‘Web Content Link’ from Google Drive. This sends a link to the image right under the custom text that you choose.

And voila!

In only three steps, you have a custom bot that sends files from Google Drive at your desired frequency.

Feel free to reach out if you need help automating your business’ workflows, or to show off what you made with the bot.

The link below will lead you to a copy of the bot that you can customize. Happy automating!

Schedule by Zapier → Google Drive & SMS

Don’t forget to give us your 👏 !

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Building a Bot that Sends Dog Pictures in 20 Minutes was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Welcome to the concluding part of our Machine learning integrated chatbot series. I’d recommend you read the first part using this link you haven’t.

In Part 1 of this series, we covered model training and integrating the Dialogflow chatbot into Telegram.

We will continue building the backend using the Flask web framework.

Flask

Flask is a python web framework used to build a lightweight, scalable webhook service to communicate with our chatbot. When the chatbot gets input from the user, it communicates to the machine learning model using Flask to make a prediction.

Create the flask webhook

Before creating our Flask webhook, we need to create a virtual environment to install Flask and its dependencies.

#Navigate to a folder on your computer 

cd abdulquadri/oshoare/desktop/loanbot

#install the virtual environment 

pip install venv myflaskbot

#myflaskbot is my virtual environment name

Install the Dependencies

Using the requirement.txt file found on my Github page, install the dependencies.

pip install -r requirement.txt

Create the app.py file

#import required libraries
import numpy as np
from flask import Flask, request, make_response,render_template,jsonify
import json
import pickle
from flask_cors import cross_origin

#instantiate flask
app = Flask(__name__)
model = pickle.load(open('rfc.pkl', 'rb'))

@app.route('/')
def hello():
    return render_template('home.html')

if __name__ == '__main__':
    app.run()

After creating the app.py file, create an optional folder called template. A simple HTML file with the tag H1 “Hello World” can be added to see the output in the local server.

Run the app.py

On the terminal, run python app.py

python app.py 

Verify that Flask has started on port 5000, as shown in the image above.

Create the predict & Webhook method

Flask uses the predict & webhook method to return JSON information of the prediction to the Dialogflow chatbot.

Would you please copy the code below and paste it into your app.py file?

@app.route('/predict', methods=['POST'])
def predict():
     json_ = request.json
     query = (json_)
     prediction = model.predict(query)
     return jsonify({'prediction': list(prediction)})

# geting and sending response to dialogflow
@app.route('/webhook/', methods=['GET','POST'])
@cross_origin()
def webhook():

req = request.get_json(silent=True, force=True)

print("req")

res = mlprediction(req)

res = json.dumps(res, indent=4)
    #print(res)
    r = make_response(res)
    r.headers['Content-Type'] = 'application/json'
    return r

# processing the request from dialogflow
def mlprediction(req):

#sessionID=req.get('responseId')
    result = req.get("queryResult")
    #print(result)

parameters = result.get("parameters")
    ApplicantIncome=parameters.get("Income")
    CoapplicantIncome = parameters.get("CoapplicantIncome")
    LoanAmount=parameters.get("LoanAmount")
    Credit_History=parameters.get("Credit_History")

if str.lower(Credit_History) == 'yes':
        Credit_History = int(1)
    elif str.lower(Credit_History) == 'no':
        Credit_History = int(0)
    else:
        return {
            "fulfillmentText": "Error please start again and enter the correct information"

        }

try:
        int_features = [ApplicantIncome, CoapplicantIncome, LoanAmount,Credit_History]
        final_features = [np.array(int_features)]

    except ValueError:
        return {
            "fulfillmentText": "Incorrect information supplied"
        }

print(final_features)

intent = result.get("intent").get('displayName')

    if (intent=='Default Welcome Intent - yes'):
        prediction = model.predict(final_features)
        print(prediction)


        if(prediction=='Y'):
            status = 'Congratulations you are eligible for a loan 😀'
        else:
            status = 'We are sorry you are not eligible for a loan at the moment'

fulfillmentText= status

        print(fulfillmentText)
        print(prediction)
        return {
            "fulfillmentText": fulfillmentText
        }

if __name__ == '__main__':
    app.run()

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Test the webhook using a Webtunnel

To test the webhook, we are going to use ngrok. ngrok is a web tunnel tool that exposes local servers behind NATs and firewalls to the public internet over secure tunnels. ngrok makes it easy to test a live application without deploying it to a cloud service.

Procced to https://ngrok.com/download to download ngrok. extract the .zip folder and run ngrok.exe HTTP 5000 to expose port 5000 on your computer to the internet

#expose http 5000 to the internet

ngrok.exe HTTP 5000

Make sure to copy the URL https://0931-105-112-38-173.ngrok.io and keep it handy. Dialogflow will use the URL to communicate with the flask app.

Test that the URL is exposed to the internet by pasting https://0931-105-112-38-173.ngrok.io in your browser. You should get the same welcome page as the one on the local server.

Connecting the backend and the frontend

Now that the Telegram bot and the flask app are live, we connect our bot to the backend.

Remember the URL field we left blank in part 1? Kindly go to the Dialogflow console and click on the fulfilment tab. We will add the URL gotten from ngrok to the webhook URL.

Click save and test your chatbot with Telegram.

Conclusion

Hooray!, we have successfully built our first Machine learning-enabled chatbot.

You can interact with the chatbot using the links below

Link to the chatbot hosted on a website: https://loan-predictor-bot.herokuapp.com/

Link to the Telegram bot: https://t.me/LoanPredictionBot

I hope you found this tutorial interesting. Please share and remember to comment with your suggestions or feedback.

Don’t forget to clap and follow me on LinkedIn for posts on Data Science and AI.

Don’t forget to give us your 👏 !

---

Deploying a Machine learning model as a Chatbot (Part 2) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

“What use is a machine learning model if you don’t deploy to production.”

– Anonymous

Are you tired of waiting for a software developer to integrate your machine learning model into a website for testing after training your model for days?

Are you bored of the usual model deployment to a website?

Are you looking for a fun way to deploy your machine learning model?

Worry no more because, with your python skills, we can take an unusual approach and deploy the machine learning model as a chatbot instead.

Chatbot has been around the corner and is becoming increasingly popular post-COVID-19. What’s more, chatbots are easy to access, easy to build, and can be integrated on almost any platform.

You can check out my previous article on how chatbots can turbocharge your business here.

Grab a seat while I walk you through this beautiful adventure.

The Flow

This project would be divided into two sections, the frontend and the backend.

* The frontend

The frontend would consist of our chatbot, which would be built with Dialogflow and integrated with Telegram

* The backend

The backend Would consist of the ML model deployed as a flask API hosted on a server.

The Dataset.

The Dataset we are going to use is the Loan prediction dataset. The loan prediction dataset is a unique dataset that contains 12 columns. The data was gathered to predict if a customer is eligible for a loan.

The Dataset is publicly available on Kaggle and can be accessed using this link.

Cleaning and creating & saving the machine learning models

Let’s Start with the bottom-up approach and build a simple Machine learning model. For this project, we would not be concerned with achieving high accuracy on our models. We only need enough accuracy so that our chatbot can make decent predictions on the fly.

Loading and Analyzing the Data

We start by importing the necessary libraries like Pandas, NumPy, Matplotlib, and seaborn. We then use pandas read_csv() to read the data.

#import neccessary library

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#loading the dataset

df_train = pd.read_csv("train.csv")
df_test = pd.read_csv('test.csv')

Data preparation and cleaning

Checking the summary of the Dataset, we can see that we have a few missing data. We will take care of the missing data by filling it with the mean and mode.
Please note: that there are more advanced techniques to fill missing data; for the sake of this project, we will prioritize simplicity over accuracy.

df_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 614 entries, 0 to 613
Data columns (total 13 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Loan_ID            614 non-null    object 
 1   Gender             601 non-null    object 
 2   Married            611 non-null    object 
 3   Dependents         599 non-null    object 
 4   Education          614 non-null    object 
 5   Self_Employed      582 non-null    object 
 6   ApplicantIncome    614 non-null    int64  
 7   CoapplicantIncome  614 non-null    float64
 8   LoanAmount         592 non-null    float64
 9   Loan_Amount_Term   600 non-null    float64
 10  Credit_History     564 non-null    float64
 11  Property_Area      614 non-null    object 
 12  Loan_Status        614 non-null    object 
dtypes: float64(4), int64(1), object(8)
memory usage: 62.5+ KB

Filling missing Data

#Imputing Missing values with mean for continuous variable
df_train['LoanAmount'].fillna(df_train['LoanAmount'].mean(), inplace=True)

#Imputing Missing values with mode for categorical variables
df_train['Gender'].fillna(df_train['Gender'].mode()[0], inplace=True)
df_train['Married'].fillna(df_train['Married'].mode()[0], inplace=True)
df_train['Dependents'].fillna(df_train['Dependents'].mode()[0], inplace=True)
df_train['Loan_Amount_Term'].fillna(df_train['Loan_Amount_Term'].mode()[0], inplace=True)
df_train['Credit_History'].fillna(df_train['Credit_History'].mode()[0], inplace=True)
df_train['Self_Employed'].fillna(df_train['Self_Employed'].mode()[0], inplace=True)

Feature engineering

Converting categorical variables into dummy/indicator variables.

cat_cols = ['Gender','Married','Education','Self_Employed','Property_Area']

train_df = pd.get_dummies(df_train,columns=cat_cols,drop_first=True)

Training the Model

#split the data into train and test for training

from sklearn.model_selection import train_test_split

X = train_df.drop(columns='Loan_Status')
y = train_df['Loan_Status']

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=42)

#import the randomForest algorithm to train the datasets

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_score

sel = SelectFromModel(RandomForestClassifier(n_estimators=100,random_state=0,n_jobs=-1))
sel.fit(X_train,y_train)
sel.get_support()

X_train_rfc = sel.transform(X_train)
X_test_rfc =sel.transform(X_test)

#Using Recursive Feature Eliminination

def run_randomForest(X_train,X_test,y_train,y_test):
    rfc = RandomForestClassifier(n_estimators=100,random_state=0,n_jobs=-1)
    rfc.fit(X_train,y_train)
    y_pred = rfc.predict(X_test)
    print("Accuracy: ", accuracy_score(y_test,y_pred))
    print('n')
    print("Confusion Matrix")
    print('n')
    print(confusion_matrix(y_test,y_pred))

#Save our model as pickle so it can be reused to make predictions
import pickle
with open(r'rfc.pkl','wb') as model_pkl:
    pickle.dump(rfc, model_pkl, protocol=2)

After preparing the Dataset, I used recursive feature elimination to select features with the highest predicting factor. I found that using four features, we got an accuracy of 76%, while 11 features got an accuracy of 78%.

You can find the whole notebook containing the code on GitHub using this link.

Building the Frontend

The front end would consist of the Dialogflow bot and telegram integration.

What is Dialogflow?

Dialogflow is a natural language understanding platform designed by Google that makes it easy to design and build enterprise-grade chatbots.

Dialogflow makes creating chatbots easy, and It uses NLU Natural language understanding on pre-trained models to understand Users’ intent with little training data. One of the reasons I choose Dialogflow is its robustness and its easy Integration with another third-party app.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Creating the chatbots using Dialogflow

We will follow the steps below in creating our chatbot

• Create the chatbot
• Create intents
• Fulfilment
• Integration

Create the chatbot

To create our chatbot, kindly go to https://dialogflow.cloud.google.com/ to create an account. After completing the account, click on create an agent to create the chatbot, as shown in the image below.

Create Intents

When creating a chatbot, Dialogflow presents you with two default intents; welcome intent and fallback intent.

Intents are the actions users want to execute. Intents represent something a user might ask for. Intents help the chatbot agent determine what the users want. e.g. One Pizza, please (Intent: Order Pizza).

Welcome Intent

The welcome intent is triggered when a user interacts with the bot; it is usually a greeting, as its name suggests. e.g. Hello, I’m the loanPredictionBot. Would you like to make a prediction?

Click on the plus sign on the intent tab to Modify the welcome intent and add the response below.

After adding the response, kindly click save and allow the chatbot to train.

After it has finished training, click on the default intent and create a follow-up intent.

Follow-up Intent

Follow-up intents are used to guide the user into making a prediction.

After the welcome intent is triggered, the user would be asked to make a Prediction; if the user clicks yes, the yes-follow up intent is triggered, and the user is asked for parameters to make the prediction. If the user selects no, the No-follow up intent is triggered.

Follow-up intent

The yes follow-up intent is where the real magic happens. This is where the user inputs details to be used for making predictions.

Kindly add the parameters(features) that we selected using recursive feature elimination. These are the features that the user would be prompted for when he tries to make a prediction.

Dialogflow uses entities to extract information from the intent. Use the default sys.number entity so that the chatbot would only accept numbers. Don’t forget to click the save button after adding the parameters.

Using the same technique, let’s create the No follow up intent.

The No follow-up intent would be triggered when the user clicks on No.

The fallback intents would be triggered when the Dialogflow agent cannot understand the user’s input. It would be populated to return a response to guide users on how to use the bot.

For now, the fallback intent would be configured as described below.

Fulfillments

Fulfilment is a feature in Dialogflow that allows the agent to communicate with external applications using a webhook. With fulfilment, you can connect the chatbot to a database, map API or backend service.

To use fulfilment, we must turn it on in the Dialogflow console, go to the fulfilment tab and turn on webhook. Leave the URL field empty for now.

Goto the default yes follow-up intent and turn on fulfilments. This will allow the yes follow-up intent to send input received from users to the backend flask service.

Testing the Agent

Using the Dialogflow console, test the chatbot by typing “hello”. This would trigger the Welcome intent.

Telegram Integration

To Integrate with Telegram, We need to create a Telegram bot.

Kindly go to Telegram and search for botFather. Using the on-screen guide, create a bot and copy the access token. We will use the access token to link Dialogflow with the telegram bot.

Kindly go to Dialogflow and click on the Integration>> Telegram.

Paste the access token as shown in the image below and click start.

Go back to the Telegram bot and interact. You should get the same response.

Conclusion

We have come to the end of the first part of creating a Machine learning model as a chatbot. In part two of this series (link here), we will deploy the machine learning model as a Flask API and link it with our chatbot.

I hope you found this tutorial interesting. Please share and remember to comment with your suggestions or feedback.

Don’t forget to clap and follow me on LinkedIn for posts on Data Science and AI.

Cheers!!!

Don’t forget to give us your 👏 !

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Deploying a Machine learning model as a Chatbot (Part 1) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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