How to Make a Chatbot in Python?
In the past few years, chatbots in Python 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. In this article, we will learn about chatbot using Python and how to make chatbot in python.
Learn Machine Learning online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.
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.
1. Retrieval-based Chatbots
A retrieval-based chatbot is one that functions on predefined input patterns and set responses. Once the question/pattern is entered, the chatbot uses a heuristic approach to deliver the appropriate response. The retrieval-based model is extensively used to design goal-oriented chatbots with customized features like the flow and tone of the bot to enhance the customer experience.
2. Generative Chatbots
Unlike retrieval-based chatbots, generative chatbots are not based on predefined responses – they leverage seq2seq neural networks. This is based on the concept of machine translation where the source code is translated from one language to another language. In seq2seq approach, the input is transformed into an output.
Must Read: Free deep learning course!
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 to chatbots in python today.
Here you will know about python online course free!
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. Chatbots have become a staple customer interaction tool for companies and brands that have an active online presence (website and social network platforms).
Chatbots using python are a nifty tool since they facilitate instant messaging between the brand and the customer. Think about Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana. Aren’t these just wonderful? Aren’t you already curious to learn how to make a chatbot in Python?
Essentially, the chatbot using Python 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.
Although chatbot in python has 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 make a simple chatbot in Python using the ChatterBot Python library. So, let’s get started!
ChatterBot is a Python library that is designed to deliver automated responses to user inputs. It makes use of a combination of ML algorithms to generate many different types of responses. This feature allows developers to build chatbots using python that can converse with humans and deliver appropriate and relevant responses. Not just that, the ML algorithms help the bot to improve its performance with experience.
Another excellent feature of ChatterBot is its language independence. The library is designed in a way that makes it possible to train your bot in multiple programming languages.
How does ChatterBot function?
When a user enters a specific input in the chatbot (developed on ChatterBot), the bot saves the input along with the response, for future use. This data (of collected experiences) allows the chatbot to generate automated responses each time a new input is fed into it.
The program chooses the most-fitting response from the closest statement that matches the input, and then delivers a response from the already known selection of statements and responses. Over time, as the chatbot engages in more interactions, the accuracy of response improves.
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.
To build a chatbot in Python, you have to import all the necessary packages and initialize the variables you want to use in your chatbot project. Also, remember that when working with text data, you need to perform data preprocessing on your dataset before designing an ML model.
This is where tokenizing helps with text data – it helps fragment the large text dataset into smaller, readable chunks (like words). Once that is done, you can also go for lemmatization that transforms a word into its lemma form. Then it creates a pickle file to store the python objects that are used for predicting the responses of the bot.
Another vital part of the chatbot development process is creating the training and testing datasets.
Now that we’ve covered the basics of chatbot development in Python, let’s dive deeper into the actual process!
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:
You can also install ChatterBot’s latest development version directly from GitHub. For this, you will have to write and execute the following command:
pip install git+git://github.com/gunthercox/ChatterBot.git@master
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 to create chatbot using python.
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
This is the third step on creating chatbot in python. The chatbot you are creating will be an instance of the class “ChatBot.” After creating a new ChatterBot instance, you can train the bot to improve its performance. Training ensures that the bot has enough knowledge to get started with specific responses to specific inputs. 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 using python 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 using python 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
In this last step of how to make a chatbot in Python, for training your python 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:
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. This is how we create chatbot in Python.
What we’ve illustrated here is just one among the many ways of how 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 in Python 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 using python fully well, you can experiment with it using different tools and commands to make it even smarter.
Also, If you’re interested to learn more about Machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
What is a Chatbot?
A chatbot is a piece of AI-based software that can converse with humans in their own language. These chatbots often connect with humans through audio or written means, and they can easily mimic human languages to speak with them in a human-like manner. The Rule-based approach teaches a chatbot to answer queries based on a set of pre-determined rules that it was taught when it was first created. These predetermined rules can be simple or complex. Self-learning bots, as the name implies, are bots that can train on their own. These take advantage of cutting-edge technology like Artificial Intelligence and Machine Learning to learn from examples and behaviors.
What skills do I need to build a chatbot?
Developers of chatbots must possess a diverse range of skills. They must have a thorough understanding of platforms and programming languages in order to efficiently work on Chatbot development. Developers of chatbots should be well-versed in Learning Algorithms, Artificial Intelligence, and Natural Language Processing. Multilingual background with programming experience in languages such as Java, PHP, Python, Ruby, and others. The programmers must be conversant with the platforms in order to improve the quality of the chatbot.
What is a rule based chatbot?
Instead of using AI, a rule-based bot utilizes a tree-like flow to assist guests with their questions. This indicates that the bot will lead the guest through a series of follow-up questions in order to arrive at the proper solution. You have complete control over the dialogue because the structures and responses are all pre-defined. So, why should you use a chatbot with rules? Smaller numbers and simple enquiries, such as booking a table at a restaurant or inquiring about operating hours, are ideal for rule-based chatbots.