How to develop a chatbot in Python (by programming)?

Develop a chatbot

You are a company and you want to communicate effectively with your customers. The chatbot is the tool you should choose. Indeed, most people prefer to conduct a conversation via web chat rather than having to call service centers. That’s why there are more than 2 billion messages sent per month through chatbots. If you want to have your own Botnation robot, here’s everything you need to know to successfully develop it from the Python interface.

➜ Discover Botnation and launch your chabot easily!

The chatbot: what is it about?

A chatbot is a computer program designed to simulate a conversation with human users, especially on the Internet. The creation of this programmed bot is perfectly in line with the principles of artificial intelligence.

Still called conversational agent, the chatbot characterizes a discussion robot imitating human conversations. It is possible for users to interact with this web tool via a chat platform: messenger, whatsapp, Twitter, etc. The interaction can also be done from Google Assistant on your cell phone.

Moreover, this programmed bot is increasingly becoming part of the marketing policy of many companies. Chatbots have many advantages, especially in communication and customer relations. As long as customer satisfaction remains the priority of any company, adopting a tool like a chatbot will be very beneficial in this sense.

On the other hand, the chatbot can also help the company with some simple tasks. Also, this bot can be a quick way for customers to access information related to the company. For example, a customer who wishes to have information on the company’s products or services can have automatic responses following the launch of his request.

Moreover, this programmed bot allows the company to improve its quality of service to customers. Chatbots are also known to improve the user experience. The customer can therefore use the services of the company without difficulty.

How does a chatbot work?

To program a chatbot in Pyython, it is essential to first understand how this web service works. In reality, it is a software that can interact and communicate with customers just like a human being. You wonder how this is possible.

You should know that this is because the chatbot uses Natural Language Processing (NLP). This concept has two components. The first is Natural Language Understanding (NLU). This is the chatbot ‘s ability to understand theuser‘s language. The second is Natural Language Generation (NLG). This is the ability of the robot to produce text similar to what humans say.

In practice, when a customer asks questions, Botnation breaks the sentence into two elements: intent and entity. The first is what the user is looking for. As for the second one, it gives details about the entity. It is therefore thanks to this automatic study that the chatbot can understand the queries and provide different response proposals.

What do you need to create a chatbot in Python?

Having a project to create a chatbot can be beneficial in the long run. To develop a chatbot, you will need a few things. Among these is the NLTK Deep Learning library. Without it, you cannot proceed with chatbot creation. If this is the first time you’ve heard of it, you should know that it’s a Natural Language Processing toolkit.

Several models are available. You can opt for Keras. Using the Python programming language, this library is able to do the natural language processing you need.

What is the structure of a chatbot creation project?

You want to program a chatbot messenger on your company website. Be aware that this is a project involving many operations. To help you, here is the list of files that you will have to edit along the development of your chatbot. Among these, we distinguish :


This is the file that will allow the creation of the user interface.


This is the file in which you will create and train the deep learning model.

  • Intents.json

This file stores all the data needed to train the model.

  • Words.pkl

This directory contains all the unique words that make up the vocabulary of the model.

  • Classes.pkl

This is the file that saves the names of the tags to be classified for a better prediction of the answers.

  • Chatbot_model.h5

This is the hierarchical file in which the weights and the architecture of the trained model will be stored at the end.

What are the steps to develop a Botnation chatbot?

Now that you have an idea of how the project is structured, you might be wondering how you actually go about programming a Botnation chatbot.

Importing data

The first thing you need to do is to import the modules required for the development of your chatbot onto the platform. To do this, you will have to go to the Python interface and create the file Train_chatbot. Only then can you proceed with the import.

Data pre-processing

Once this is done, you need to do some preliminary processing of the loaded data. Several techniques can be used. Tokenization and lemmatization are the most common. The first is to divide the sentences into words and add them to the intent file. The second is to gather the lemma words into a class.

Data programming

You can now program the data. To do this, you will need to convert each model into numbers. Create a list of 0’s of the same length as the number of all the words. Finally, set the value 1 for indexes containing the word in the templates.

The formation of the model

The next step is to train the model. Start by adjusting the data using the “SGD” optimizer. Then, proceed to the formation of the epochs. Finally, save the model using the Keras model save function.

Interaction with the robot

The template is ready, now we have to create the graphical interface of the chat. You can save it in the file All you have to do is capture a user message, pre-process it and enter it into the model to see if it generates the expected responses.

Here is the procedure to follow to realize your project of designing a chatbot in Python.

➜ Discover Botnation and launch your chabot easily!

Amandine Carpentier
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