How to build a AI chatbot using NLTK and Deep Learning

The chatbots you interact with everyday are pretty smart because they use additional algorithms and libraries. We create the startup file as a separate entity so that we can add more aiml files

to the bot later without having to modify any of the programs source code. The chatbot we’ve built is relatively simple, but there are much more complex things you can try when building your own chatbot in Python. If it sparks your interest, then learn how deep learning works. You can build a chatbot that can provide answers to your customers’ queries, take payments, recommend products, or even direct incoming calls. An AI chatbot is built using NLP which deals with enabling computers to understand text and speech the way human beings can.

We will give you a full project code outlining every step and enabling you to start. This code can be modified to suit your unique requirements and used as the foundation for a chatbot. The right dependencies need to be established before we can create a chatbot. Python and a ChatterBot library must be installed on our machine. With Pip, the Chatbot Python package manager, we can install ChatterBot. Python is a popular choice for creating various types of bots due to its versatility and abundant libraries.

Improving the Chatbot

In this section, we will learn how to upgrade it to the latest version. In case you don’t know, Pip is the package manager for Python. Basically, it enables you to install thousands of Python libraries from the Terminal. To create an AI chatbot, you don’t need a powerful computer with a beefy CPU or GPU. The ‘temperature’ parameter controls the randomness of the model’s output. A low value like 0.3 will make the responses more focused and deterministic, while higher values produce more random outputs.

This is where the intelligent and not just a scripted bot that will be ready to handle any test thrown at them. The main package that we will be using in our code here is the Transformers package provided by HuggingFace. This tool is popular amongst developers as it provides tools that are pre-trained and ready to work with a variety of NLP tasks. NLP, or Natural Language Processing, stands for teaching machines to understand human speech and spoken words. NLP combines computational linguistics, which involves rule-based modeling of human language, with intelligent algorithms like statistical, machine, and deep learning algorithms.

Create an Generative-AI chatbot using Python and Flask: A step by step guide

It is written in Cython and can perform a variety of tasks like tokenization, stemming, stop word removal, and finding similarities between two documents. NLP is used to summarize a corpus of data so that large bodies of text can be analyzed in a short period of time. Document summarization yields the most important and useful information. Let us consider the following snippet of code to understand the same. We will follow a step-by-step approach and break down the procedure of creating a Python chat.

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. As a cue, we give the chatbot the ability to recognize its name and use that as a marker to capture the following speech and respond to it accordingly. This is done to make sure that the chatbot doesn’t respond to everything that the humans are saying within its ‘hearing’ range. In simpler words, you wouldn’t want your chatbot to always listen in and partake in every single conversation. Hence, we create a function that allows the chatbot to recognize its name and respond to any speech that follows after its name is called.

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Your chatbot is now ready to engage in basic communication, and solve some maths problems. Training the chatbot will help to improve its performance, giving it the ability to respond with a wider range of more relevant phrases. Over 30% of people primarily view chatbots as a way to have a question answered, with other popular uses including paying a bill, resolving a complaint, or purchasing an item. For example, ChatGPT for Google Sheets can be used to automate processes and streamline workflows to save data input teams time and resources. I am excited to introduce myself as an AI python developer with years of experience transforming clients ideas into functional and intelligent applications.

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