Real Python: ChatterBot: Build a Chatbot With Python

Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.

You’ll get the basic chatbot up and running right away in step one, but the most interesting part is the learning phase, when you get to train your chatbot. The quality and preparation of your training data will make a big difference in your chatbot’s performance.

To simulate a real-world process that you might go through to create an industry-relevant chatbot, you’ll learn how to customize the chatbot’s responses. You’ll do this by preparing WhatsApp chat data to train the chatbot. You can apply a similar process to train your bot from different conversational data in any domain-specific topic.

In this tutorial, you’ll learn how to:

• Build a command-line chatbot with ChatterBot
• Train the chatbot to customize its responses
• Export your WhatsApp chat history
• Perform data cleaning on the chat export using regular expressions
• Retrain the chatbot with industry-specific data

You’ll also learn how ChatterBot stores your training data, and you’ll find suggestions and pointers for next steps, so you can start collecting real user data and let the chatbot learn from it.

Overall, in this tutorial, you’ll quickly run through the basics of creating a chatbot with ChatterBot and learn how Python allows you to get fun and useful results without needing to write a lot of code.

Demo

At the end of this tutorial, you’ll have a command-line chatbot that can respond to your inputs with semi-meaningful replies:

You’ll achieve that by preparing WhatsApp chat data and using it to train the chatbot. Beyond learning from your automated training, the chatbot will improve over time as it gets more exposure to questions and replies from user interactions.

Project Overview

The ChatterBot library combines language corpora, text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots.

You can build an industry-specific chatbot by training it with relevant data. Additionally, the chatbot will remember user responses and continue building its internal graph structure to improve the responses that it can give.

In this tutorial, you’ll start with an untrained chatbot that’ll showcase how quickly you can create an interactive chatbot using Python’s ChatterBot. You’ll also notice how small the vocabulary of an untrained chatbot is.

Next, you’ll learn how you can train such a chatbot and check on the slightly improved results. The more plentiful and high-quality your training data is, the better your chatbot’s responses will be.

Therefore, you’ll either fetch the conversation history of one of your WhatsApp chats or use the provided chat.txt file that you can download here:

It’s rare that input data comes exactly in the form that you need it, so you’ll clean the chat export data to get it into a useful input format. This process will show you some tools you can use for data cleaning, which may help you prepare other input data to feed to your chatbot.

After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance.

When you work through this process from start to finish, you’ll get a good idea of how you can build and train a Python chatbot with the ChatterBot library so that it can provide an interactive experience with relevant replies.

Prerequisites

Before you get started, make sure that you have a Python version available that works for this ChatterBot project. What version of Python you need depends on your operating system: