Create Your Own Chatbot with Python: Python Automation Series #3

Table of Contents

1. Introduction
2. What is a Chatbot?
3. History of Chatbots
4. Types of Chatbots
   1. Rule-Based Chatbots
   2. Self-Learning Chatbots
5. Creating a Rule-Based Chatbot in Python
   1. Importing the NLTK Library
   2. Defining Pairs of Questions and Answers
   3. Creating the Chatbot
   4. Using Regular Expressions
   5. Adding Reflections
6. Creating Eliza, the Psychotherapist Chatbot
   1. Understanding the Source Code
   2. Running the Program
7. Conclusion

Creating a Chatbot from Scratch in Python

Chatbots have become increasingly popular in recent years, with many businesses and organizations using them to Interact with customers and provide assistance. In this article, we will explore the world of chatbots and learn how to Create our own chatbot from scratch in Python.

What is a Chatbot?

A chatbot is an artificial intelligence-powered piece of software that can simulate conversation with human users. Chatbots can be used in a variety of applications, including customer service, information retrieval, and entertainment.

History of Chatbots

The history of chatbots dates back to 1966 when a computer program called ELIZA was invented by Joseph Weizenbaum. ELIZA was a psychotherapist chatbot that imitated the language of a psychotherapist from only 200 lines of code. Since then, chatbots have come a long way and are now used in a variety of applications.

Types of Chatbots

There are two broad categories of chatbots: rule-based and self-learning.

Rule-Based Chatbots

Rule-based chatbots answer questions based on some rules on which they are trained. The rules defined can be very simple to very complex. Rule-based chatbots can handle simple queries but fail to manage complex ones.

Self-Learning Chatbots

Self-learning chatbots, on the other HAND, are the ones that use some machine learning-based approaches. Even these bots have two subcategories: retrieval-based and generative.

Retrieval-Based

Retrieval-based chatbots use some heuristic to select a response from a library of predefined responses.

Generative

Generative chatbots can generate the answers and not always reply with one of the answers from a set of answers, which makes them more intelligent.

Creating a Rule-Based Chatbot in Python

In this section, we will learn how to create a rule-based chatbot in Python. We will be using the NLTK library, which stands for Natural Language Toolkit.

Importing the NLTK Library

The first thing we need to do is import the NLTK library. We will import chat from nltk.chat.util.

Defining Pairs of Questions and Answers

Next, we need to define a variable called pairs, which is a list that contains the questions and corresponding answers. We will predefine them so that the program can answer them.

Creating the Chatbot

After defining the pairs, we need to create another variable called chat, which equals the module chat and inside here, we will pass the pairs.

Using Regular Expressions

In order to solve the problem of typing your name and getting a reply with your name, we will use regular expressions. We will need to work a little bit on our code. Instead of making it static by saying "my name is backpraise," we will now delete "backpraise," open parentheses, and use .* (dot asterisk). Likewise, in the answer, we will delete "backpraise" with an exclamation mark and %1.

Adding Reflections

We need to import something called reflections from the chat library. Reflections are a predefined set of data and answers or a form of addressing and answers to that. For example, "I am" - "You are," "I have" - "you have," and so on.

Creating Eliza, the Psychotherapist Chatbot

In this section, we will create Eliza, the psychotherapist chatbot. We will be using the source code provided in the video.

Understanding the Source Code

The source code for Eliza is more than 200 lines of code. It defines a variable called pairs, which is no longer in the form of a list but rather in a tuple. Inside that tuple, we have predefined questions and answers, and we have the r for row STRING. The program continues by asking more questions and getting more answers. We have quit at the end when you Type quit.

Running the Program

To run the program, we need to open it through the Git Bash and type python eliza.py. The program will ask you to talk to it by typing in plain English. It defines the language should be English using normal upper and lowercase letters and punctuation. If you want to quit the program, simply enter quit.

Conclusion

In this article, we learned about chatbots, their history, and the types of chatbots. We also learned how to create a rule-based chatbot in Python using the NLTK library and how to create Eliza, the psychotherapist chatbot. Chatbots have become an essential part of many businesses and organizations, and learning how to create them can be a valuable skill.