Chatbot Project using Python

Published on 18 May 2022. Written by Utkarsh Agarwal

Introduction

Chatbots are very useful for business associations and furthermore clients. Most of the individuals want to talk legitimately from a chatbot as opposed to calling service centres. Facebook discharged information that demonstrated the value of bots. More than 2 billion messages are sent among individuals and organizations month to month. The HubSpot explore tells that 71% of the individuals need to get client service from informing applications. It is a fast method to get their issues understood so chatbots have a splendid future in associations.

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SLNOTE
Today we are going to construct an exciting project on Chatbot. We will execute a chatbot without any preparation that will have the option to comprehend what the user is discussing and give a proper reaction.

Prerequisites

To actualize the chatbot we will utilize Keras which is a Deep Learning library, NLTK which is a Natural Language Processing Toolkit and some supportive libraries. Run the below command to ensure all the libraries are introduced.

SLLATEST
How do Chatbots work?

Chatbots are only a smart bit of software that can cooperate and speak with individuals simply like people. Fascinating isn't it! So now let us see how they really work. All the chatbot goes under the NLP (Natural Language Processing) ideas. NLP is made out of two things:

NLU (Natural Language Understanding): The capacity of machines to comprehend human language like English.

NLG (Natural Language Generation): The capacity of a machine to produce content like human composed sentences.

Picture a user posing an inquiry to a chatbot "Hello, What's on the news today? " The chatbot will separate the user sentence into two things: Intent and an Entity. The expectation for this sentence could be get news as it alludes to an activity the user needs to perform. The element educates explicit details regarding the plan, so here 'today' will be the substance. So along these lines an AI model is utilized to perceive the expectations and substances of the talk.

Project file structure

After the venture is finished, you will be left with every one of these records. Lets rapidly experience every one of them, it will give you a thought of how the venture will be implemented.

1.Train_chatbot.py - In this document, we will build and train the deep learning model that can order and recognize what the user is asking to the bot.

2.Gui_Chatbot.py - This record is the place we will build a graphical user interface to talk with our trained chatbot.

3.Intents.json - The goals document has all the information that we will use to train the model. It contains an assortment of labels with their relating examples and reactions.

4. Chatbot_model.h5 - This is a progressive information format document in which we have put away the loads and the architecture of our trained model.
5. Classes.pkl - The pickle document can be utilized to store all the label names to group when we are foreseeing the message.
6. Words.pkl - The words.pkl pickle document contains all the remarkable words that are the jargon of our model. 

How to build your own chatbot?

Step 1.  Import libraries and load the data

Step 2. Preprocessing the data

Step 3. Create training and testing data

Step 4. Training the model

Step 5. Interacting with the chatbot

Step 6. Running the chatbot

SLDYK
Kit required to develop Chatbot Project using Python:

• No kit required

Technologies you will learn by working on Chatbot Project using Python:

• Python
• Machine Learning
• Deep Learning

Any Questions?