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In this tutorial, you will learn how to create an attractive voice-controlled python chatbot application with a small amount of coding. To build our application we’ll first create a good-looking user interface through the built-in Tkinter library in Python and then we will create some small functions to achieve our task. 


Here is a sneak peek of what we are going to create. 


Voice controlled chatbot
Voice controlled chatbot using coding in Python – Data Science Dojo

Before kicking off, I hope you already have a brief idea about web scraping, if not then read the following article talking about Python web scraping 


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Pre-requirements for building a voice python chatbot

Make sure that you are using Python 3.8+ and the following libraries are installed on it 

  • Pyttsx3 (pyttsx3 is a text-to-speech conversion library in Python) 
  • SpeechRecognition (Library for performing speech recognition) 
  • Requests (The requests module allows you to send HTTP requests using Python) 
  • Bs4 (Beautiful Soup is a library that is used to scrape information from web pages) 
  • pyAudio (With PyAudio, you can easily use Python to play and record audio) 


If you are still facing installation errors or incompatibility errors, then you can try downloading specific versions of the above libraries as they are tested and working currently in the application. 


  • Python 3.10 
  • pyttsx3==2.90 
  • SpeechRecognition==3.8.1 
  • requests==2.28.1
  • beautifulsoup4==4.11.1 
  • beautifulsoup4==4.11.1 


Now that we have set everything it is time to get started. Open a fresh new py file and name it Import the following relevant libraries on the top of the file. 


  • from tkinter import * 
  • import time
  • import datetime
  • import pyttsx3
  • import speech_recognition as sr
  • from threading import Thread
  • import requests
  • from bs4 import BeautifulSoup 


The code is divided into the GUI section, which uses the Tkinter library of python and 7 different functions. We will start by declaring some global variables and initializing instances for text-to-speech and Tkinter. Then we start creating the windows and frames of the user interface. 


The user interface 

This part of the code loads images initializes global variables, and instances and then it creates a root window that displays different frames. The program starts when the user clicks the first window bearing the background image. 


if __name__ == “__main__”: 


#Global Variables 

loading = None
query = None
flag = True
flag2 = True


#initalizng text to speech and setting properties 

engine = pyttsx3.init() # Windows voices = engine.getProperty('voices') engine.setProperty('voice', voices[1].id) rate = engine.getProperty('rate') engine.setProperty('rate', rate-10) 


#loading images 

    img1= PhotoImage(file='chatbot-image.png') 
    img2= PhotoImage(file='button-green.png') 
    img3= PhotoImage(file='icon.png') 
    img4= PhotoImage(file='terminal.png') 
    front_image = PhotoImage(file="front2.png") 


#creating root window 

    root.title("Intelligent Chatbot") 


#Placing frame on root window and placing widgets on the frame 

    f = Frame(root,width = 1360, height = 690),y=0) 


#first window which acts as a button containing the background image 

    okVar = IntVar() 
    btnOK = Button(f, image=front_image,command=lambda: okVar.set(1)),y=0) 
    background_label = Label(root, image=background_image), y=0) 


#Frame that displays gif image 

    frames = [PhotoImage(file='chatgif.gif',format = 'gif -index %i' %(i)) for i in range(20)] 
    canvas = Canvas(root, width = 800, height = 596),y=10) 
    canvas.create_image(0, 0, image=img1, anchor=NW) 


#Question button which calls ‘takecommand’ function 

    question_button = Button(root,image=img2, bd=0, command=takecommand),y=625) 


#Right Terminal with vertical scroll 

    canvas2.config(width=500,height=596, background="black") 
    canvas2.create_image(0,0, image=img4, anchor="nw") 
    task = Thread(target=main_window) 


The main window functions 

This is the first function that is called inside a thread. It first calls the wishme function to wish the user. Then it checks whether the query variable is empty or not. If the query variable is empty, then it checks the contents of the query variable. If there is a shutdown or quit or stop word in query, then it calls the shutdown function, and the program exits. Else, it calls the web_scraping function. This function calls another function with the name wishme. 


def main_window(): 
    global query 
    while True: 
        if query != None: 
            if 'shutdown' in query or 'quit' in query or 'stop' in query or 'goodbye' in query: 
                query = None 


The wish me function 

This function checks the current time and greets users according to the hour of the day and it also updates the canvas. The contents in the text variable are passed to the ‘speak’ function. The ‘transition’ function is also invoked at the same time in order to show the movement effect of the bot image, while the bot is speaking. This synchronization is achieved through threads, which is why these functions are called inside threads. 


def wishme(): 
    hour = 
    if 0 <= hour < 12: 
        text = "Good Morning sir. I am Jarvis. How can I Serve you?" 
    elif 12 <= hour < 18: 
        text = "Good Afternoon sir. I am Jarvis. How can I Serve you?" 
        text = "Good Evening sir. I am Jarvis. How can I Serve you?" 
    canvas2.create_text(10,10,anchor =NW , text=text,font=('Candara Light', -25,'bold italic'), fill="white",width=350) 
    p2 = Thread(target=transition) 


The speak function 

This function converts text to speech using pyttsx3 engine. 

def speak(text): 
    global flag 


The transition functions 

The transition function is used to create the GIF image effect, by looping over images and updating them on canvas. The frames variable contains a list of ordered image names.  


def transition(): 
    global img1 
    global flag 
    global flag2 
    global frames 
    global canvas 
    local_flag = False 
    for k in range(0,5000): 
        for frame in frames: 
            if flag == False: 
                canvas.create_image(0, 0, image=img1, anchor=NW) 
                flag = True 
                canvas.create_image(0, 0, image=frame, anchor=NW) 


The web scraping function 

This function is the heart of this application. The question asked by the user is then searched on google using the ‘requests’ library of python. The ‘beautifulsoap’ library extracts the HTML content of the page and checks for answers in four particular divs. If the webpage does not contain any of the four divs, then it searches for answers on Wikipedia links, however, if that is also not successful, then the bot apologizes.  


def web_scraping(qs): 
    global flag2 
    global loading 
    URL = '' + qs 
    page = requests.get(URL) 
    soup = BeautifulSoup(page.content, 'html.parser') 
    div0 = soup.find_all('div',class_="kvKEAb") 
    div1 = soup.find_all("div", class_="Ap5OSd") 
    div2 = soup.find_all("div", class_="nGphre") 
    div3  = soup.find_all("div", class_="BNeawe iBp4i AP7Wnd") 

    links = soup.findAll("a") 
    all_links = [] 
    for link in links: 
       link_href = link.get('href') 
       if "url?q=" in link_href and not "webcache" in link_href: 

    flag= False 
    for link in all_links: 
       if '' in link: 
           wiki = link 
           flag = True 
    if len(div0)!=0: 
        answer = div0[0].text 
    elif len(div1) != 0: 
       answer = div1[0].text+"\n"+div1[0].find_next_sibling("div").text 
    elif len(div2) != 0: 
       answer = div2[0].find_next("span").text+"\n"+div2[0].find_next("div",class_="kCrYT").text 
    elif len(div3)!=0: 
        answer = div3[1].text 
    elif flag==True: 
       page2 = requests.get(wiki) 
       soup = BeautifulSoup(page2.text, 'html.parser') 
       title ="#firstHeading")[0].text
       paragraphs ="p") 
       for para in paragraphs: 
           if bool(para.text.strip()): 
               answer = title + "\n" + para.text 
        answer = "Sorry. I could not find the desired results"
    canvas2.create_text(10, 225, anchor=NW, text=answer, font=('Candara Light', -25,'bold italic'),fill="white", width=350) 
    flag2 = False 
    p2 = Thread(target=transition) 


The take command function 

This function is invoked when the user clicks the green button to ask any question. The speech recognition library listens for 5 seconds and converts the audio input to text using google recognize API. 


def takecommand(): 
    global loading 
    global flag 
    global flag2 
    global canvas2 
    global query 
    global img4 
    if flag2 == False: 
        canvas2.create_image(0,0, image=img4, anchor="nw")  
    speak("I am listening.") 
    flag= True 
    r = sr.Recognizer() 
    r.dynamic_energy_threshold = True 
    r.dynamic_energy_adjustment_ratio = 1.5 
    #r.energy_threshold = 4000 
    with sr.Microphone() as source: 
        #r.pause_threshold = 1 
        audio = r.listen(source,timeout=5,phrase_time_limit=5) 
        #audio = r.listen(source) 
        query = r.recognize_google(audio, language='en-in') 
        print(f"user Said :{query}\n") 
        query = query.lower() 
        canvas2.create_text(490, 120, anchor=NE, justify = RIGHT ,text=query, font=('fixedsys', -30),fill="white", width=350) 
        global img3 
        loading = Label(root, image=img3, bd=0), y=622) 
    except Exception as e: 
        speak("Say that again please") 
        return "None"


The shutdown function 

This function farewells the user and destroys the root window in order to exit the program. 

def shut_down(): 
    p1=Thread(target=speak,args=("Shutting down. Thankyou For Using Our Sevice. Take Care, Good Bye.",)) 
    p2 = Thread(target=transition) 



It is time to wrap up, I hope you enjoyed our little application. This is the power of Python, you can create small attractive applications in no time with a little amount of code. Keep following us for more cool python projects! 


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September 27, 2022

What is web scraping?

Web scraping is the act of extracting the content and data from a website. The vast amount of data available on the internet is not open and available to download. As a result, ethical web scraping is the most effective technique to collect this data. There is also a debate about the legality of web scraping as the content may get stolen or the website can crash as a result of web scraping.

Ethical Web Scraping is the act of harvesting data legally by following ethical rules about web scraping. There are certain rules in ethical web scraping that when followed ensure trust between the website owner and web scraper.

Web scraping using Python

In Python, a learner can write a small piece of code to do large tasks. Since web scraping is used to save time, a small code written in Python can save a lot of time. Also, Python is simple and easy to understand and provides an extensive set of libraries for web scraping and further manipulation required on extracted data.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your web scraping skills.

Challenges for individuals

Individuals who are new to web scraping and wish to flourish in their field usually lack the necessary computing and learning resources to obtain hands-on expertise. Also, they may face compatibility issues when installing libraries.

What we provide

With just a single click, Jupyter Hub for Ethical Web Scraping using Python comes with pre-installed Web Scraping python libraries, which gives the learner an effortless coding environment in the Azure cloud and reduces the burden of installation. Moreover, this offer provides the learner with a repository of the famous book on web scraping which contains chapter-wise notebooks which serve as a learning resource for a user in gaining hands-on experience with web scraping.

Through this offer, a learner can collect data from various sources legally by following the best practices for ethical web scraping mentioned in the latter section of this blog. Once the data is collected, it can be further analyzed to get valuable insights into almost everything while all the heavy computations are performed on Microsoft Azure hence saving the user from the trouble of running high computations on the local machine.

Python libraries:

Listed below are the pre-installed web scraping python libraries and the sources of repositories of web scraping book provided by this offer:

  •          Pandas
  •          NumPy
  •          Scikit-learn
  •          Beautifulsoup4
  •          lxml
  •          MechanicalSoup
  •          Requests
  •          Scrapy
  •          Selenium
  •          urllib


  •          GitHub repository of book Web Scraping with Python 2nd Edition,
    by author Ryan Mitchell.

Best practices for ethical web scraping

Globally, there is a debate about whether web scraping is an ethical concept or not. The reason it is unethical is that when a website is queried repeatedly by the same user (in this case bot), too many requests land on the server simultaneously and all resources of the server may be consumed in generating responses for each request, preventing it from responding to other legitimate users.

In this way, the server denies responses to any further users, commonly known as a Denial of Service (DoS) attack.

Below are the best practices for ethical web scraping, and compliance with these will allow a web scraper to work ethically.

1.   Check out for ROBOTS.TXT

Robots.txt file, also known as the Robots Exclusion Standard, is used to inform the web scrapers if the website can be crawled or not, if yes then how to index the website. A legitimate web scraper is expected to respect the instructions in this file and not disobey the website owner’s allowed instructions.

2.   Check for website APIs

An ethical web scraper is expected to first look for the public API of the website in question instead of scraping it all together. Many website owners provide public API access which can be used by anyone looking to gain from the information available on the website. Provision of public API works in the best interests of both the ethical scrapper as well as the website owner, avoiding web scraping altogether.

3.   Avoid repeated requests

Vigorous scraping can occasionally cause functionality issues, resulting in a poor user experience for humans. As a result, it is always advised to scrape during off-peak hours. An ethical web scraper is expected to delay recurrent requests to avoid a DoS attack.

4.   Provide your identity

It is always a good idea to take responsibility for one’s actions. An ethical web scraper never hides his or her identity and provides it in a user-agent string. Not only does this make the intentions of the scraper clear but also provides a means of contact for any questions or concerns of the website owner.

5.   Avoid fake ownership

The content scraped through web scraper should always be respected and never passed on under the fake information of scraper as the author. This act can be regarded as highly unethical as well as illegal since the website owner may file a copyright claim. It also damages the reputation of genuine web scrapers and hurts the trust of the website owner.

6.  Ask for permission

Since the website information belongs to the owner, one should never presume it to be free and ask politely to use it for their means. An ethical web scraper always seeks permission from the website owner to avoid any future problems. The website owner should be given the choice of whether she agrees to scrape the data.

 7.  Give due credit

To encourage the website owner as a token of thanks, the web scraper should give due credit wherever possible. This can be done in many ways such as providing a link to the original website on any blog, article, or social media post by generating traffic for the original website.

Ethical web scraping


Ethical web scraping is a two-way street in which the website owner should be mindful of the global availability of the data, similarly, the scraper should not harm the website in any way and also first seek permission from the website owner. If a web scraper abides by the above-mentioned practices, I.e., he/she works ethically, the web owner may not only allow scraping his or her website but also provide helpful means to the scraper in the form of Meta data or a public API.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Jupyter Notebook Environment dedicated specifically for Ethical Web Scraping using Python. Install the Jupyter Hub offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science!

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September 16, 2022

Use Python and BeautifulSoup to web scrape. Web scraping is a very powerful tool to learn for any data professional. Make the entire internet your database.

Web scraping tutorial using Python and BeautifulSoup

With web scraping, the entire internet becomes your database. In this tutorial, we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup.

web scraping

There are many services out there that augment their business data or even build out their entire business by using web scraping. For example there is a steam sales website that tracks and ranks steam sales, updated hourly. Companies can also scrape product reviews from places like Amazon to stay up-to-date with what customers are saying about their products.

The code

from bs4 import BeautifulSoup as soup  # HTML data structure
from urllib.request import urlopen as uReq  # Web client
#URl to web scrap from.
#in this example we web scrap graphics cards from
page_url = ""
#opens the connection and downloads html page from url
uClient = uReq(page_url)
#parses html into a soup data structure to traverse html
#as if it were a json data type.
page_soup = soup(, "html.parser")
#finds each product from the store page
containers = page_soup.findAll("div", {"class": "item-container"})
#name the output file to write to local disk
out_filename = "graphics_cards.csv"
#header of csv file to be written
headers = "brand,product_name,shippingn"
#opens file, and writes headers
f = open(out_filename, "w")
#loops over each product and grabs attributes about
#each product
for container in containers:
# Finds all link tags "a" from within the first div.
make_rating_sp ="a")
# Grabs the title from the image title attribute
# Then does proper casing using .title()
brand = make_rating_sp[0].img["title"].title()
# Grabs the text within the second "(a)" tag from within
# the list of queries.
product_name ="a")[2].text
# Grabs the product shipping information by searching
# all lists with the class "price-ship".
# Then cleans the text of white space with strip()
# Cleans the strip of "Shipping $" if it exists to just get number
shipping = container.findAll("li", {"class": "price-ship"})[0].text.strip().replace("$", "").replace(" Shipping", "")
# prints the dataset to console
print("brand: " + brand + "n")
print("product_name: " + product_name + "n")
print("shipping: " + shipping + "n")
# writes the dataset to file
f.write(brand + ", " + product_name.replace(",", "|") + ", " + shipping + "n")
f.close()  # Close the file

The video (enjoy!)

For more info, there’s a script that does the same thing in R

Want to learn more data science techniques in Python? Take a look at this introduction to Python for Data Science

August 18, 2022

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