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.
Read on!
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.
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
Here are type of bots:
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.
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:
First, we will ask Username
print("BOT: What is your name?")
user_name = input()
Output
What is your name?
Testing
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"] }
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
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
def send_message(message):
print((message))
response = res(message)
print((response))
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
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.
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
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')
Here are the benefits of Bots:
Also read: Top 30+ Python Projects: Beginner to Advanced 2024
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
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
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?"]
]
]
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()
Finally, add this code at the end of your file to start the chatbot:
if __name__ == "__main__":
chatbot()
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()
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!
Ans. Yes, Python is commonly used for chatbots. Its versatility, extensive libraries like NLTK and spaCy for natural language processing, and frameworks like ChatterBot make it an excellent choice. Python’s simplicity, readability, and strong community support contribute to its popularity in developing effective and interactive chatbot applications.
Ans. Popular Python libraries for chatbot development include NLTK, spaCy for natural language processing, TensorFlow, PyTorch for machine learning, and ChatterBot for simple implementations. Flask or Django can handle web integration. Rasa offers a comprehensive framework. Choose based on your project’s complexity, requirements, and library familiarity.
Ans. To create a chatbot in Python using the ChatterBot module, install ChatterBot, create a ChatBot instance, train it with a dataset or pre-existing data, and interact using the chatbot’s logic. Implement conversation flow, handle user input, and integrate with your application. Deploy the chatbot for real-time interactions.
Ans. To craft a generative chatbot in Python, leverage a natural language processing library like NLTK or spaCy for text analysis. Utilize chatgpt or OpenAI GPT-3, a powerful language model, to implement a recurrent neural network (RNN) or transformer-based model using frameworks such as TensorFlow or PyTorch. Train the model on a dataset and integrate it into a chat interface for interactive responses.
Ans. The time to create a chatbot in Python varies based on complexity and features. A simple one might take a few hours, while a sophisticated one could take weeks or months. It depends on the developer’s experience, the chosen framework, and the desired functionality and integration with other systems.
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