Sai A Sai A
Updated date Mar 06, 2024
In this blog, we will learn how to create a basic chatbot in Python. This blog covers setting up the environment, preprocessing data, building the chatbot, and interacting with it.


In today's world, chatbots have become everywhere, assisting us in various tasks, from customer service to personal assistants. But have you ever wondered how these chatbots are created? In this blog, we will guide you through the process of building a simple chatbot in Python

Setting Up Environment

Before we move into coding our chatbot, let's ensure we have the necessary tools installed. Firstly, make sure you have Python installed on your system. You can download it from the official Python website and follow the installation instructions.

Once Python is installed, we will need to install a library called nltk, which stands for Natural Language Toolkit. Open your terminal or command prompt and type the following command:

pip install nltk

Importing Libraries and Preprocessing

Now, let's start coding our chatbot. Open your favorite text editor or integrated development environment (IDE) and create a new Python file. We will begin by importing the necessary libraries and initializing some variables:

import nltk
import numpy as np
import random
import string

# Importing NLTK libraries
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

Preprocessing the Data

Before we can train our chatbot, we need to preprocess the data. This involves tokenization, removing stopwords, and lemmatization. Here's how we can do it:

# Download NLTK resources'punkt')'stopwords')'wordnet')

# Tokenization
def tokenize(sentence):
    return word_tokenize(sentence)

# Removing stopwords
def remove_stopwords(tokens):
    stop_words = set(stopwords.words('english'))
    return [word for word in tokens if word.lower() not in stop_words]

# Lemmatization
def lemmatize(tokens):
    lemmatizer = WordNetLemmatizer()
    return [lemmatizer.lemmatize(word) for word in tokens]

Building the Chatbot

Now, let's define our chatbot function. We will use a simple rule-based approach where the chatbot will respond based on predefined patterns.

# Defining chatbot function
def chatbot_response(user_input):
    responses = {
        "hi": "Hello! How can I assist you today?",
        "how are you": "I'm doing well, thank you!",
        "bye": "Goodbye! Have a great day!",
        # Add more responses as needed
    # Tokenizing and preprocessing user input
    tokens = tokenize(user_input)
    tokens = remove_stopwords(tokens)
    tokens = lemmatize(tokens)
    # Checking for predefined patterns
    for key in responses.keys():
        if key in tokens:
            return responses[key]
    # If no predefined pattern matches, return a default response
    return "I'm sorry, I didn't understand that. Can you please rephrase?"

Interacting with the Chatbot

Now that we have defined our chatbot function, let's create a simple loop to interact with it:

# Main loop
while True:
    user_input = input("You: ")
    response = chatbot_response(user_input)
    print("Chatbot:", response)


You: hi
Chatbot: Hello! How can I assist you today?
You: How are you?
Chatbot: I'm doing well, thank you!
You: Can you help me with a programming problem?
Chatbot: I'm sorry, I didn't understand that. Can you please rephrase?
You: bye
Chatbot: Goodbye! Have a great day!


You have successfully built a simple chatbot in Python. Although this chatbot is basic and rule-based, it explains the basic principles behind building conversational agents. From here, you can explore more advanced techniques such as machine learning-based approaches to create more sophisticated chatbots.

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