How Python Suddenly Became a Superhero of IT industry

Programming drives the IT industry in the 21st century. Whether it is a mobile phone or complex software, everything in the world of IT is made possible with the help of programming. Information technology industry upgrades itself often and at this time artificial intelligence and machine learning are the buzz words in the industry. But the real hero is Python, like we said programming is the main force driving the IT sector.

We say it again that Python occupies the top spot on the list of preferred programming languages in 2020.

Developed by Guido Van Rossum in 1989, and named after Monty Python, Python is a programming language which is the hero behind Artificial Intelligence, Machine Learning and chatbots.

In fact, Python is greatly used for many other applications. This post will shed some light on the story behind the elevation of Python as the Superhero in the information technology industry and how you can use it to create a chatbot.

Why Python is Getting Popular in the IT industry

At the time of its inception, Python had Java as its formidable competitor. Despite facing tough competition, it has managed to cement its position as a leading choice among programmers until now. Due to its ability to innovate, most app development professionals consider it as the best programming language for application development.

For a long time, the programming language has been in the sidelines on the pretext that it is not compatible with the operating systems of most devices. Some experts cite the need for a compiler for the interpretation of its codes as the reason behind it. Even with that said, no one can overlook the fact that it is also the best when it comes to innovation.

Because Python fits into the scheme of things of most companies that make the IT industry, Python’s popularity is in ascendence now in the industry than ever before. This is specifically the reason why Python is getting popular in the IT industry.

It is due to ability of Python to adapt to the changing dynamics in technology in the IT industry that it comes in handy in the creation of modern tools such as chatbots – a virtual interactive human for simulation purposes.

Apart from chatbots using Python, there are a number of other applications in which Python has a significant utility. In fact, Python is the default option for app developers due to its ability to work in conjunction with artificial intelligence (AI).

What is a Chatbot?

A chatbot is an AI-based computer program which uses textual and auditory methods to conduct a conversation. Chatbots are used for simulation to determine the behaviour of human as a conversational partner. A developer develops with the aim of clearing the Turing test.

Chatbots, that exemplify one of the important applications of Python, are popular in the domain of customer service. They typically constitute an important component of dialogue systems.

These days, more companies are showing a tendency of using chatbots for maintaining conversational exchanges with customers simply because chatbots are efficient at performing these tasks than humans. Unlike human beings, they do not tire of while undertaking these tasks.

Depending on whether or not chatbots are based on predefined responses and inputs, they can be classified into the following categories:

● Retrieval based chatbots

● Generative based chatbots

The very fact that Python codes come in handy in creating chatbots is one of the popular reasons behind the popularity of Python among application developers.

Given the unavailability due to huge demand of Python developers, are you looking for options to create a chatbot with Python codes? Here’s how you can go about it.

Making chatbot in Python

The journey of making chatbots with advanced features began in the 1960s. Innovations in the approach of making chatbots continue until now. Still, developers believe more needs to be done to realize the potential of chatbots powered by AI.

Typically, chatbots based on Python programming languages have the following features:

● They understand who the target audience is.

● Chatbots have an amazing ability to discern the natural language of communication.

● A Python code-based chatbot understands user intent and makes decisions based on it.

● A chatbot is good at providing an appropriate response by analysing the needs of a user.

With these features of chatbots on the mind, you can proceed with your intent of creating a chatbot based on Python codes. Before you go any further, make sure you have sound knowledge of Natural language processing along with Python and Keras. The knowledge of these aspects is of paramount importance to succeed in the creation of chatbots.

As is the case with the other developers, you will also use the following files in the creation of your chatbot:

● Intents.json – it consists of predefined patterns and the outcomes based on them.

● – You can use this file to build the basic model of a chatbot and also for training it.

● Words.pkl – This is the file for storing the vocabulary of the language which your chatbot will use for communicating with its human partner.

● Classes.pkl: This file involves a list of all categories.

● Chatbot_model.h5 – It involves information about the weights of neurons and weights of it.

● – You can interact with a chatbot by implementing this script.

Steps to create a chatbot by using Python codes

Now that you have come to know about the important files and scripts involved in the creation of chatbots, follow these steps for making chatbot in Python.

Import the data file and load it: Create a file by the name of Make sure you import the requisite packages in order to initialize the variable that you are likely to use along with the Python codes. Keep the file in JSON format so you can parse it into Python without changing the format.

Preprocess the data: Prepare deep learning or machine learning model by preprocessing the data. Also, tokenize the text to break it down into small words. You will need to store Python objects after creating a pickle file. In order to do it, you will need to lemmatize the words. Remove the words that are used for more than have been used more than once.

Create data for training as well as testing: You will need to provide information to the file in order to get output from it. Whether you will get the desired level of output or not will depend on how well you input the requisite information. An important thing to remember here is that you need to convert text into numbers for computer to understand it.

Develop the model: Create an in-depth mural model with three layers. You can do this by using Keras sequential API. For best results, make sure it is in line with the file you use for training the chatbot. This will help you achieve a desirable level of accuracy.

Predict the response: You need to get an insight into the type of response you will receive based on a specific pattern of interaction with the chatbot. Once the trained model is loaded, think about predicting the responses from the bot using a graphical interface. The model only helps to find information about the class. You can identify it by implementing the functions. This way, you will be able to retrieve random responses from the list of responses you have loaded in the file for storing information while training the chatbot. Emulate the manner of providing input during the training phase while trying to predict the class. Code the graphical user interface (GUI) by using the Tkinter library.

Operate the chatbot: You are almost done with the creation of a chatbot. The only thing you need to do is put the finishing touches by using the command python No error at the time of training ins an indicator that you have created the model successfully. Sit tight for a few seconds after running the chatbot. You will see a window for communication on the screen.

Final thoughts

Over the last few years, Python has exceeded the expectations in the IT industry. One of the important applications of Python is chatbots – a virtual interacting partner.

It is the growing influence of chatbots that has not only put Python in the driving seat but also increased the demand of Python developers. If you wish to create your own chatbot, follow the steps listed above.