Chat Bot in Python with ChatterBot Module

Together, these technologies create the smart voice assistants and chatbots we use daily. Generative chatbots powered by Python revolutionise natural (NLP) and human-computer interaction. ChatterBot is a Python library used to create chatbots that generate automated responses to users’ input by using machine learning algorithms. Using the ChatterBot library and the right strategy, you can create chatbots for consumers that are natural and relevant.

When a user inputs a query, or in the case of chatbots with speech-to-text conversion modules, speaks a query, the chatbot replies according to the predefined script within its library. This makes it challenging to integrate these chatbots with NLP-supported speech-to-text conversion modules, and they are rarely suitable for conversion into intelligent virtual assistants. A chatbot is a computer program that aims to make a conversation between both humans and machines. The chatbot can be utilized in a variety of platforms, including messaging apps and virtual assistants. The chatbot has evolved a lot in decades starting from amusement usage to performing serious tasks.

Indian Restaurant Chatbot- Project

AI chatbots have quickly become a valuable asset for many industries. Building a chatbot is not a complicated chore but definitely requires some understanding of the basics before one embarks on this journey. Once the basics are acquired, anyone can build an AI chatbot using a few Python code lines. TheChatterBot Corpus contains data that can be used to train chatbots to communicate. One of the major challenges with creating a chatbot is the accuracy of the responses generated from a user query, hence why many chatbots do not succeed.

In the following section, I will explain how to create a rule-based chatbot that will reply to simple user queries regarding the sport of tennis. For instance, a task-oriented chatbot can answer queries related to train reservation, pizza delivery; it can also work as a personal medical therapist or personal assistant. Depending on their application and intended usage, chatbots rely on various algorithms, including the rule-based system, TFIDF, cosine similarity, sequence-to-sequence model, and transformers. Congratulations, you’ve built a Python chatbot using the ChatterBot library! Your chatbot isn’t a smarty plant just yet, but everyone has to start somewhere. You already helped it grow by training the chatbot with preprocessed conversation data from a WhatsApp chat export.

Project Overview

They rely on predetermined rules and keywords to interpret the user’s input and provide a response. To build a chatbot, it is important to create a database where all words are stored and classified based on intent. The response will also be included in the JSON where the chatbot will respond to user queries. Whenever the user enters a query, it is compared with all words and the intent is determined, based upon which a response is generated.

You should be able to run the project on Ubuntu Linux with a variety of Python versions. However, if you bump into any issues, then you can try to install Python 3.7.9, for example using pyenv. You need to use a Python version below 3.8 to successfully work with the recommended version of ChatterBot in this tutorial. Firstly, we import the requests library so that we can make the HTTP requests and work with them. In the next line, you must replace the your_api_key with the API key generated for your account.

Just modify intents.json with possible patterns and responses and re-run the training. Paste the code in your IDE and replace your_api_key with the API key generated for your account. Chatbots can perform various tasks like booking a railway ticket, providing information about a particular topic, finding restaurants near you, etc. Chatbots are created to accomplish these tasks for users providing them relief from searching for these pieces of information themselves.

It is important to mention that the idea of this article is not to develop a perfect chatbot but to explain the working principle of rule-based chatbots. On the other hand, if the input text is not equal to “bye”, it is checked if the input contains words like “thanks”, “thank you”, etc. or not. Otherwise, if the user input is not equal to None, the generate_response method is called which fetches the user response based on the cosine similarity as explained in the last section. First we need a corpus that contains lots of information about the sport of tennis.

Step2: Create patterns and responses file

The purpose of this project is to build a ChatBot that utilises NLP (Natural Language Processing) and assists customers. A ChatBot is an automated conversation system that replies to users’ queries by analysing them using NLP and assists them in every way it can. In this project, we are trying to implement a customer service chatbot that tries to converse and assist the user in some simple scenarios. This chat bot can take simple user queries as input, process them, classify them into one of the existing tags, and respond to them with an appropriate response.

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