How to Make a Chatbot in Python Step By Step [With Source Code]

Creating a chatbot in Python is an essential skill for modern developers looking to enhance user interaction and automate responses within applications. How to Make a Chatbot in Python serves as a comprehensive guide aimed at sharing expert knowledge and steps to craft a Python-based chatterbox from the ground up. This article will outline the fundamental steps to get you started on your chatbot journey, covering everything from the basic concept of what a chatbot is to the final implementation in Python. Whether you’re aiming to enhance customer service or create a more interactive user experience, the knowledge shared here will set a solid foundation for your endeavors in the exciting world of chatbot development. 

In this Article, you will learn about How to Make a Chatbot in Python Step By Step:

1. Prepare the Dependencies
2. Import Classes
3. Create and Train the Chatbot
4. Communicate with the Python Chatbot
5. Train your Python Chatbot with a Corpus of Data

Read more to know each in detail.

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. 

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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.

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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.

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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. 

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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? 

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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. 

Source 

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!

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ChatterBot Library

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.

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How does Python 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. You may create your own chatbot project to understand the details of this technology. 

Source 

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How To Make A Simple Chatbot In Python From Scratch?

You may have this question in your mind, how to create a chatbot? 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! It will help you understand how to create a 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:

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:

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. This is how we create chatbot in Python. 

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How Does Chatbot in Python Work? 

Now that we have learned how to make a chatbot in Python let us witness a mechanism of action for the implemented code. The chatbot incorporates a sophisticated chatting interface, which employs artificial intelligence and machine learning to interact with different individuals. The process of the workability of a Python chatbot like in the example using ChatterBot library, can be described as taking place over several successive stages. 

The essence of the process can be represented by developing a chatbot. With the help of machine learning algorithms, this bot learns from user inputs and responses over time to become smarter. It is also language-agnostic, which suggests that bots can be created in a range of programming languages, ensuring more flexibility. Therefore, the intricacies of chatbot-Python interaction point to the quality and type of results that it brings learning outcomes or adaptive outputs. 

What is ChatterBot Library? 

The ChatterBot library is one of the main processes in how to create a chatbot in Python. It is a Chatbot AI and significantly influences Python chatbots. ChatterBot is a type of chatbot created through the implementation of machine learning algorithms that are designed to process user input and provide responses automatically. The main aim of this is that its output responses need to be diverse but still context-appropriate to create better user interactions. 

 Of importance, ChatterBot has been a success in its ability to train chatbots in various programming languages that developers prefer. This AI architecture is designed in such a manner that it encodes all inputs and user responses creating an experience pool. In response to a new input, the ChatterBot-invented responds by using ML and determines which answer is appropriate from its saved information. 

Benefits of a Chatbot

We have covered how to make a chatbot in this article. Now we must understand its benefits to grasp its full utilization. Chatbots Programming is very useful, especially when it comes to building good relationships with customers. Strong connections can be built with the help of chatbots because it helps you to interact with the visitors of your website directly. With the help of chatbot programming, you not only achieve all the marketing goals but also increase sales and better customer service.

Chatbot programming comes with several benefits – 

1. Increase in customer interaction – Businesses may increase consumer interaction by using chatbots. Customer interaction may be pushed based on customer data and enhanced engagement by deploying conversational AI chatbots. This process is very important after you understand how to make a chatbot. Additionally, since bots may provide regular responses, you can avoid giving clients useless information. Additionally, clients are more likely to stay on your website more and continue the dialogue if they receive pertinent and prompt replies.
2. Generating more leads – The ability to engage consumers with individualized messaging during their journey is a strength of today’s highly evolved bots. In reality, your company may employ chatbots to help clients make rapid decisions by generating leads. In order to convince the consumers of lead generation and greater conversion rates. 
3. Consumer service costs are reduced – Chatbot implementation is an investment in reducing the cost of customer support. You may save extra money by investing in chatbots instead of employing more support staff. A firm may cut expenses in a variety of ways with chatbots:

Salaries

Infrastructure

Training

Similarly, using AI chatbots for customer care has various advantages;

• Reduce chat availability at peak times.
• Automate common business operations
• Allow for new customer service approaches

Additionally, AI-bots may be expanded without incurring any additional expenditures during business peaks. In addition, bots are cost-saving and improve the bottom line by ensuring that clients have an easier and more consistent brand experience.

1. Monitor data to get consumer insights – By tracking user data, a chatbot project is a great way to study customer behavior and follow purchase trends.  Using this information, businesses may sell their products in new ways and reach a wider audience.  Similar to this, bots may be employed to gather input via straightforward questions, improve services, or enhance websites.
2. It helps with a Conversational Marketing Strategy – Real-time customer interactions are at the heart of conversational marketing, which aims to assist and drive customers along the sales funnel. It may assist your company in developing genuine consumer interactions and real experiences. By engaging with customers through customized messaging and clever chatbots, firms using chatbots for marketing may increase conversion rates.
3. Enhance customer services and meet consumer expectations – Customers anticipate prompt replies to their inquiries or concerns. As chatbots become more intelligent, businesses may utilize them to satisfy customers’ expectations by automating some of the typical interactions. The goal remains the same, whether you employ an Android chatbot: to provide value to consumers through automation. Customers won’t have to wait for humans to communicate with them thanks to automation, which is one of the chatbot’s most important use cases. This will undoubtedly improve their experience with the business and increase retention.
4. Create a Smooth Customer Experience – No firm wants to lose prospects midway through a sales funnel since that is never a desirable scenario. Customers may choose not to buy for a variety of reasons, such as a lengthy checkout procedure or unstated fees. Chatbots can be useful in assisting businesses in lowering customer turnover and improving the customer experience. By writing the proper bot scripts, you may successfully interact with clients at various points in their journeys and respond to their questions

What are the Limitations of a Chatbot? 

Below are some limitations of chatbot: 

• Limited Understanding of Context: 

Rule-based chatbots, in particular, may lag behind in terms of context meaning during the conversation. Operating under predefined rules, they frequently misinterpret the intent of a user for complex or even ambiguous queries.  

• Dependency on Training Data: 

The success of chatbots largely depends on the type and quality of their training data. But if chatbots are not properly trained, they may offer wrong or irrelevant responses. Moreover, they may unknowingly reinforce biases embedded in the training data and raise ethical issues that could make users feel alienated. 

• Inability to Handle Unforeseen Scenarios: 

One of the main issues in chatbots implementation is to adapt to unpredictable user inputs. They can also produce general answers or simply state that they cannot understand which makes for an un-intuitive and nonuser friendly environment. 

Conclusion

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.

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