How to Make a Chatbot in Python Step By Step [Python Chatterbox Guide]

In the past few years, chatbots have become wildly popular in the tech and business sectors. These intelligent bots are so adept at imitating natural human languages and conversing with humans, that companies across various industrial sectors are adopting them. From e-commerce firms to healthcare institutions, everyone seems to be leveraging this nifty tool to drive business benefits

What is a Chatbot?

A chatbot is an AI-based software designed to interact with humans in their natural languages. These chatbots are usually converse via auditory or textual methods, and they can effortlessly mimic human languages to communicate with human beings in a human-like manner. A chatbot is arguably one of the best applications of natural language processing.

Chatbots can be categorized into two primary variants – Rule-Based and Self-learning.

The Rule-based approach trains a chatbot to answer questions based on a set of pre-determined rules on which it was initially trained. These set rules can either be very simple or very complex. While rule-based chatbots can handle simple queries quite well, they usually fail to process more complicated queries/requests.

As the name suggests, self-learning bots are chatbots that can learn on their own. These leverage advanced technologies like Artificial Intelligence and Machine Learning to train themselves from instances and behaviours. Naturally, these chatbots are much smarter than rule-based bots. Self-learning bots can be further divided into two categories – Retrieval Based or Generative.

The first chatbot dates back to 1966 when Joseph Weizenbaum created ELIZA that could imitate the language of a psychotherapist in only 200 lines of code. However, thanks to the rapid advancement of technology, we’ve come a long way from scripted chatbots.

Chatbot in Today’s Generation

Today, we have smart AI-powered Chatbots that use natural language processing (NLP) to understand human commands (text and voice) and learn from experience. Think about Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana. Aren’t these just wonderful?

Essentially, these chatbots are programmed to take in the information you provide to it and then analyze it with the help of complex AI algorithms, and provide you with either a written or verbal response. Since these bots can learn from behaviour and experiences, they can respond to a wide range of queries and commands. 

Source 

Although chatbots have already begun to dominate the tech scene at present, Gartner predicts that by 2020, chatbots will handle nearly 85% of the customer-brand interactions.

In light of the increasing popularity and adoption of chatbots in the industry, you can increase your market value by learning how to make a chatbot in Python – one of the most extensively used programming languages in the world.

Today, we will teach you how to build a simple chatbot in Python using the ChatterBot Python library. So, let’s get started!

How To Make A Chatbot In Python?

We’ll take a step by step approach and break down the process of building a Python chatbot. 

1. Prepare the Dependencies

The first step in creating a chatbot in Python with the ChatterBot library is to install the library in your system. It is best if you create and use a new Python virtual environment for the installation. To do so, you have to write and execute this command in your Python terminal:

 

 

If you wish to upgrade the command, you can do so as well:

 

 

Now that your setup is ready, we can move on to the next step.

2. Import Classes

Importing classes is the second step in the Python chatbot creation process. All you need to do is import two classes – ChatBot from chatterbot and ListTrainer from chatterbot.trainers. To do this, you can execute the following command:

3. Create and Train the Chatbot

The chatbot you are creating will be an instance of the class “ChatBot.” You have to execute the following command now:

Here, the argument (that corresponds to the parameter name) represents the name of your Python chatbot. If you wish to disable the bot’s ability to learn after the training, you can include the “read_only=True” command. The command “logic_adapters” denotes the list of adapters used to train the chatbot.

While the “chatterbot.logic.MathematicalEvaluation” helps the bot to solve math problems, the “chatterbot.logic.BestMatch” helps it to choose the best match from the list of responses already provided.

Since you have to provide a list of responses, you can do it by specifying the lists of strings that can be later used to train your Python chatbot, and find the best match for each query. Here’s an example of responses you can train your chatbot to learn:

You can also create and train the bot by writing an instance of “ListTrainer” and supplying it with a list of strings like so:

Now, your Python chatbot is ready to communicate.

4. Communicate with the Python Chatbot

To interact with your Python chatbot, you can use the .get_response() function. This is how it should look while communicating:

However, it is essential to understand that the chatbot might not know how to answer all your questions. Since its knowledge and training is still very limited, you have to give it time and provide more training data to train it further.

5. Train your Python Chatbot with a Corpus of Data

For training your chatbot even further, you can use an existing corpus of data. Here’s an example of how to train your Python chatbot with a corpus of data provided by the bot itself:

Code snippet source

The good thing is that ChatterBot offers this functionality in many different languages. So, you can also specify a subset of a corpus in a language you would prefer. 

Conclusion

What we’ve illustrated here is just one among the many ways to make a chatbot in Python. You can also use NLTK, another resourceful Python library to create a Python chatbot. And although what you learned here is a very basic chatbot model having hardly any cognitive skills, it should be enough to help you understand the anatomy of chatbots.

Once you understand the design of a chatbot fully well, you can experiment with it using different tools and commands to make it even smarter.

If you would like to know more about the creation of chatbot (or how to make one!), and other Machine Learning applications like Gesture recognition and more, check out IIIT-B and upGrad’s PG Diploma in Machine Learning and AI Program.

Kechit Goyal

Lead the AI Driven Technological Revolution

PG DIPLOMA IN MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
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