Creating ChatBot Using Natural Language Processing in Python

Are you fed up with waiting in long queues to speak with a customer support representative? Can you recall the last time you interacted with customer service? There’s a chance you were contacted by a bot rather than a human customer support professional. In our blog post-ChatBot Building Using Python, we will discuss how to build a simple Chatbot in Python programming and its benefits.

In this article, you will gain an understanding of how to make a chatbot in Python. We will explore creating a simple chatbot using Python and provide guidance on how to write a program to implement a basic chatbot effectively.

This article was published as a part of the Data Science Blogathon

Learning Objectives

1. Understanding Bots: Learn the meaning and types of bots, including chatbots, social media bots, Google bots, spam bots, transnational bots, and monitoring bots. Understand their significance in automating interactions and tasks on the internet.
2. Rule-Based Chatbot Development: Explore the concept of rule-based chatbots that operate on predefined rules and patterns. Understand how to implement rule-based responses using Python, gaining insights into structured and specific task execution.
3. Building a Simple ChatBot in Python: Learn step-by-step how to build a simple chatbot in Python. This includes user interaction, defining bot responses, creating functions for message processing, and breaking the loop for user termination. Gain hands-on experience with practical code examples.
4. Introduction to Chatterbot and NLP: Discover the role of Natural Language Processing (NLP) in chatbot development. Explore the Chatterbot library in Python and understand how to install and use it to create chatbots. Also, gain knowledge of essential NLP libraries like NLTK and Spacy.

What is the meaning of Bots?

Bots are specially built software that interacts with internet users automatically. Bots are made up of deep learning and machine learning algorithms that assist them in completing jobs. By auto-designed, we mean they run independently, follow instructions, and begin the conservation process without human intervention. With their ability to mimic human languages seamlessly and engage in natural conversations, these smart bots have the power of self-learning and gain widespread adoption among companies in diverse industries.

Bots are responsible for the majority of internet traffic. For e-commerce sites, traffic can be significantly higher, accounting for up to 90% of total traffic. They can communicate with people on social media accounts and websites.

Some were programmed and manufactured to transmit spam messages to wreak havoc.

Also read: Python Tutorial for Beginners Overview

Type of Bots

Here are type of bots:

1. Chatbot – An Artificial Intelligence (AI) programme that communicates with users through an app, message, or phone call. Twitter most commonly uses chatbots.
2. Social Media Bot- Created for social media sites to answer automatically all at once.
3. Google Bot commonly indexes and crawls the web. Spider Bots, developed to retrieve website data, widely index and crawl content.
4. Spam Bots – Spam Bots are programmed to send spam emails automatically to a list of addresses. 
5. Transnational Bots – Transnational Bots are bots designed to be used in transactions. 
6. Monitoring Bots – Creating bots to keep track of the system’s or website’s health. 

Rule-based chatbots operate on predefined rules and patterns, relying on instructions to respond to user inputs. These bots excel in structured and specific tasks, offering predictable interactions based on established rules.

Building libraries should be avoided if you want to understand how a chatbot operates in Python thoroughly. In 1994, Michael Mauldin was the first to coin the term “chatterbot” as Julia.

Also read: A Comprehensive Guide to Python Automation.

Simple ChatBot build by using Python

A ChatBot is essentially software that facilitates interaction between humans. When you train your chatbot with Python 3, extensive training data becomes crucial for enhancing its ability to respond effectively to user inputs.

ChatBot examples include Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana. Following is the step-by-step guide with regular expressions for building ChatBot using Python:

Interaction of User for asking the name

First, we will ask Username

print("BOT: What is your name?")
user_name = input()

Output

What is your name?
Testing

Response of ChatBot

To start, we assign questions and answers that the ChatBot must ask. For example, we assign three variables with different answers. It’s crucial to note that these variables can be used in code and automatically updated by simply changing their values. The format facilitates the easy passing of values.

name = "Bot Number 286" 
monsoon = "rainy" 
mood = "Smiley"
resp = { 
"what's your name?": [ 
"They call me {0}".format(name), 
"I usually go by {0}".format(name), 
"My name is the {0}".format(name) ],
"what's today's weather?": [ 
"The weather is {0}".format(monsoon), 
"It's {0} today".format(monsoon)], 
"how are you?": [ 
"I am feeling {0}".format(mood), 
"{0}! How about you?".format(mood), 
"I am {0}! How about yourself?".format(mood), ],
"": [ 
"Hey! Are you there?", 
"What do you mean by these?", 
 ],
"default": [
"This is a default message"] }

Creating a Function Response

Library random imported to choose the response. It will select the answer by bot randomly instead of the same act.

import random
def res(message):
if message in resp: 
        bot286_message = random.choice(resp[message])
else: 
        bot286_message = random.choice(resp["default"])
return bot286_message

Another Function

Sometimes, the questions added are not related to available questions, and sometimes, some letters are forgotten to write in the chat. The bot will not answer any questions then, but another function is forward.

def real(xtext): 
  if "name" in xtext: 
        ytext = "what's your name?"
elif "monsoon" in xtext: 
        ytext = "what's today's weather?"
elif "how are" in xtext: 
        ytext = "how are you?"
else: 
        ytext = ""
return ytext

Sending back the message function

def send_message(message): 
print((message)) 
response = res(message) 
print((response))

Final Step to break the loop

while 1: 
my_input = input() 
my_input = my_input.lower() 
related_text = real(my_input) 
send_message(related_text)
if my_input == "exit" or my_input == "stop": 
break

Another example is installing a library chatterbot

Chatterbot

Natural language Processing (NLP) is a necessary part of artificial intelligence that employs natural language to facilitate human-machine interaction.

Python uses many libraries such as NLTK, spacy, etc. A chatbot is a computer program that can communicate with humans in a natural language. They frequently rely on machine learning, particularly natural language processing (NLP).

Use the following commands in Terminal in Anaconda prompt.

pip install chatterbot
pip install chatterbot_corpus

You can also install chatterbot from its source: https://github.com/gunthercox/ChatterBot/archive/master.zip

After installing, unzip, the file and open Terminal, and type in:

cd chatter_bot_master_directory.

Finally, type python setup.py install.

For installing Spacy

pip install -U spacy

For downloading model “en_core_web_sm”

import spacy
from spacy.cli.download import download
download(model="en_core_web_sm")

or

pip install 
https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

Testing installation

import spacy
nlp = spacy.blank("en")
doc = nlp("This is a sentence.")

You will need two further classes ChatBot and ListTrainers

from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer

1st Example

from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
# Create a new chatbot named Charlie
chatbot = ChatBot('name')
trainer = ListTrainer(chatbot)
trainer.train([
    "Hi, can I help you?",
    "Sure, I'd like to book a flight to Iceland.",
    "Your flight has been booked."
])
# Get a response to the input text 'I would like to book a flight.'
response = chatbot.get_response('I would like to book a flight.')
print(response)

2nd Example by ChatBot

Bot286 = ChatBot(name='PyBot')
talk = ['hi buddy!',
              'hi!',
              'how do you do?',
              'how are you?',
              'i'm fine.',
              'fine, you?',
              'always smiling.']
list_trainer = ListTrainer(Bot286)for item in (talk):
     list_trainer.train(item)
>>>print(my_bot.get_response("hi"))
 how do you do?
from chatterbot.trainers import ChatterBotCorpusTrainercorpus_trainer = ChatterBotCorpusTrainer(Bot286)
 corpus_trainer.train('chatterbot.corpus.english')

Benefits of Bots

Here are the benefits of Bots:

1. Understandable information about the customer.
2. It can be called a selling partner by making and sending the product information.
3. They provide 24hrs services.
4. Satisfy clients’ needs, as they will not continue waiting for your call. They need action quickly, or they will turn to another brand.
5. Most customers prefer sending messages, texts, and SMS to the company for information. Marketing Bots can grow your business by increasing sales and satisfying needs. Facebook Messenger is one of the widely used messengers in the U.S.
6. Recently, chatbots were used by the World Health Organization to provide information by ChatBot on WhatsApp.
7. Facebook Messenger, Slack, Whatsapp, and Telegram make use of ChatBot.
8. The modern need is there for Bot Building for the growth of Business to make progress.
9. Another example of making use of ChatBo is Google Assistant and Siri.
10. Bots, for the most part, operate on a network. Bots that can communicate with one another will use internet-based services like IRC.

Also read: Top 30+ Python Projects: Beginner to Advanced 2024

Write a Program to implement a simple chatbot in Python ?

Here’s a simple guide to creating a basic chatbot in Python using easy-to-understand language.
What You Need
Python: Make sure you have Python installed on your computer.
Install NLTK: This is a library that helps with language processing. You can install it by running this command in your terminal or command prompt:

pip install nltk

Step-by-Step Guide

Step 1: Import the Library

Start by importing the necessary parts of the NLTK library. Open a new Python file and add this code:

import nltk
from nltk.chat.util import Chat, reflections

Step 2: Define Questions and Answers

Next, you need to create a list of questions (patterns) and answers (responses). Here’s an example:

pairs = [
    [
        r"my name is (.*)",
        ["Hello %1, how can I help you today?",]
    ],
    [
        r"hi|hello|hey",
        ["Hello!", "Hi there!", "Hey! How can I assist you?"]
    ],
    [
        r"how are you?",
        ["I'm just a computer program, but thanks for asking!", "I'm doing well, how about you?"]
    ],
    [
        r"what is your name?",
        ["I am a simple chatbot created to help you.", "You can call me ChatBot!"]
    ],
    [
        r"quit",
        ["Goodbye! Have a nice day!", "See you later!"]
    ],
    [
        r"(.*)",
        ["I'm sorry, I don't understand that.", "Can you say that differently?"]
    ]
]

Step 3: Create the Chatbot Function

Now, write a function that will run the chatbot:

def chatbot():
    print("Hi! I'm a simple chatbot. Type 'quit' to exit.")
    chat = Chat(pairs, reflections)
    chat.converse()

Step 4: Run the Chatbot

Finally, add this code at the end of your file to start the chatbot:

if __name__ == "__main__":
    chatbot()

Complete Code

Here’s the full code you should have in your Python file:

import nltk
from nltk.chat.util import Chat, reflections

# Define questions and answers
pairs = [
    [
        r"my name is (.*)",
        ["Hello %1, how can I help you today?",]
    ],
    [
        r"hi|hello|hey",
        ["Hello!", "Hi there!", "Hey! How can I assist you?"]
    ],
    [
        r"how are you?",
        ["I'm just a computer program, but thanks for asking!", "I'm doing well, how about you?"]
    ],
    [
        r"what is your name?",
        ["I am a simple chatbot created to help you.", "You can call me ChatBot!"]
    ],
    [
        r"quit",
        ["Goodbye! Have a nice day!", "See you later!"]
    ],
    [
        r"(.*)",
        ["I'm sorry, I don't understand that.", "Can you say that differently?"]
    ]
]

# Create the chatbot
def chatbot():
    print("Hi! I'm a simple chatbot. Type 'quit' to exit.")
    chat = Chat(pairs, reflections)
    chat.converse()

# Run the chatbot
if __name__ == "__main__":
    chatbot()

Conclusion

This article has delved into the fundamental definition of chatbots and underscored their pivotal role in business operations. By exploring the process of building your own chatbot using the Python programming language and the Chatterbot library, we’ve highlighted the critical aspects of handling text data, emphasizing the significance of preprocessing, such as tokenizing, for effective communication.

Moreover, including a practical use case with relevant parameters showcases the real-world application of chatbots, emphasizing their relevance and impact on enhancing user experiences. As businesses navigate the landscape of conversational AI, understanding the nuances of text data, mastering preprocessing techniques, and defining tailored use cases with specific parameters emerge as key considerations for successful chatbot integration.

Hope you like the article; and you will get an understanding of how to make a chatbot in Python. By utilizing simple libraries, you can write a program to implement a simple chatbot using Python. Follow guides to create your own chatbot using Python effortlessly! Feel free to let me know if you need any more adjustments!

Key Takeaways

• Bots are autonomous software that uses deep learning and machine learning to interact with users. They are responsible for significant internet traffic, including spam, transnational, and monitoring bots.
• Different types of bots include Chatbots, Social Media Bots, Google Bots for indexing, Spam Bots for automated spam emails, Transnational Bots for transactions, and Monitoring Bots for system health tracking.
• Rule-based chatbots operate on predefined rules and patterns. They excel in structured tasks. To gain a deeper understanding of chatbot operations, avoid relying solely on pre-built libraries.
• The article provides a step-by-step guide for building a simple chatbot in Python, emphasizing the importance of extensive training data for effective responses.
• An alternative approach involves using the Chatterbot library for Natural Language Processing (NLP) in Python, with examples of installing and using Chatterbot along with NLTK and Spacy libraries.
• Chatbots offer several advantages, including providing understandable information, acting as selling partners, offering 24/7 services, and satisfying client needs promptly. Examples include their use by the World Health Organization on WhatsApp and in popular messaging platforms like Facebook Messenger, Slack, Whatsapp, and Telegram.

Frequently Asked Questions

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Sonia Singla

I have done my Master Of Science in Biotechnology and Master of Science in Bioinformatics from reputed Universities. I have written a few research papers, reviewed them, and am currently an Advisory Editorial Board Member at IJPBS.
I Look forward to the opportunities in IT to utilize my skills gained during work and Internship.
https://aster28.github.io/SoniaSinglaBio/site/