Chatbot using Python (Python Project )

Chatbot using Python

What is a Chatbot?

A Chatbot is an AI application that mimics human conversations. It is widely used by companies to solve the most common problems they receive from customers daily. For example, if you want to know the CRN of your bank account, a chatbot will assist you by asking for your bank details, then it will give you your CRN. But if you want to know something that is not that common, like asking how you can turn your account into a joint account, chances are the authorized employee will assist you.

So while creating a chatbot for any company you should know what that company deals in and what problems their customers get daily. In the section below, I will take you through how to create a chatbot using Python.

How Does a Chatbot Work?

Chatbots are nothing but software applications that have an application layer, a database, and APIs. To simplify the working of the chatbot, we can say it works on pattern matching to classify text and produce a suitable response for the questions/queries addressed by the user. The chatbot responds to the user as per the program that has been fed in it. Chatbots are of different types, depending on how they are used. Mainly there are three types of chatbots, and they are as follows:

Rule-Based Chatbot: This is the basic chatbot made, the user interacts with this kind of bot by using predefined options. To get answers from these bots, users need to click on certain options. These kinds of bots collect the user's request, analyze it, and then offer results in the form of buttons. These bots are commonly used to replace frequently asked questions when it comes to complex queries; they aren't always the best solution.

Independent(Keyword) Chatbots: These are machine learning bots, unlike rule-based chatbots, they analyze what the user wants and respond appropriately. These chatbots use customizable keywords and machine learning to determine how to respond to users' requests effectively and efficiently.

NLP (Contextual) Chatbots: These are so far the most advanced chatbots. They are a combination of best from rule-based and keyword chatbots. These chatbots use NLP to understand the context and intent in users' requests and thus act accordingly. These chatbots can handle multiple requests from the same user at ease.

Comparisons of Chatbot Platform

Many platforms offer customized chatbots with automation making seamless, always on-time, best in class support services available to customers whenever they need it without the box conversational capacities. Customers are also keen to purchase from a business that they can easily connect over messages.

Listing down the AI chatbot building platform in 2020:

Azure Bot Service: Azure bot service offers to build a chatbot from scratch, i.e., you can build, connect, test, and deploy. It allows developers to use the open-source SDK and tools. It also enables developers to create advanced bots such as virtual assistants to handle complex queries.

Botsify: This tool's uniqueness is that it enables non-technical users to build a chatbot with its intuitive interface. Bots are represented here as stories, i.e., you can create multiple stories or multiple chatbots and deploy them as per the requirement. Another great feature is the ability to save users' responses to a form that can be easily exported to a CSV.

Amazon Lex: Amazon lex allows developers to build conversational interfaces using text and voice. It comes up with advanced deep learning functionalities and NLP for understanding the context of the text. It provides an easy to use console for building chatbot in minutes.

Mobile Monkey: Mobile monkey offers developers to build chatbots specifically for marketing purposes. It allows developers to make Facebook ad bots, SMS bots, and native web chatbots all in a single platform. They also offer ready-made chatbot templates for every business possible, which can be directly embedded on a website.

ChatterOn: It claims to handle different types of rich content responses from the Bot as it allows developers to connect different APIs at each interaction with the user. ChatterOn offers more than 20 pre-built bolts that can be put to use in a single click. It doesn’t require much coding, which makes it easier for non-technical users to build chatbots.

TARS: It offers you to build a conversational landing page, which enables you to create an automated chatbot to greet you, customers, give them relevant information about their queries regarding your products, and ask for their contact simultaneously. Tars offers many pre-defined chatbot templates, which are classified into two parts - by industry and by use-case.

Building a Chatbot using Chatterbot

Let's begin by installing the chatterbot library. For creating chatbot also need to install chatterbot corpus. Corpus - literal meaning is a collection of words. This contains a corpus of data that is included in the chatterbot module. Each corpus is nothing but a prototype of different input statements and their responses. These corpus are used by bots to train themselves. The most recommended method for installing chatterbot and chatterbot_corpus is by using pip.

Installation commands for terminal:

pip install chatterbot

pip install chatterbot_corpus

Installation commmands for Jupyter Notebook:

!pip install chatterbot

!pip install chatterbot_corpus

Let's first import the Chatbot class of the chatterbot module.

# Importing chatterbot
from chatterbot import ChatBot

Create Chatbot Instance

Now, it's time for the most interesting part i.e., naming your chatbot by creating a Chatbot object. You can choose any name you want. This single line of code generates our very own new bot named Buddy. We need to specify some more parameters before running our first program.

# Create object of ChatBot class
bot = ChatBot('Buddy')

[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Unzipping corpora/wordnet.zip.

You can position the storage adapter with the chatbot object. Storage Adapters allows you to connect to a particular storage unit or network. For using a storage adapter, we need to specify it. We will position the storage adapter by assigning it to the import path of the storage we want to use. Here we are using SQL Storage Adapter, which permits chatbot to connect to databases in SQL. By using the database parameter, we will create a new SQLite Database. Please follow the code below, for creating a new database for chatbot.

# Create object of ChatBot class with Storage Adapter
bot = ChatBot(
    'Buddy',
    storage_adapter='chatterbot.storage.SQLStorageAdapter',
    database_uri='sqlite:///database.sqlite3'
)

You can also position the logical adapter with a chatbot object. As the name implies, Logical Adapter regulates the logic behind the chatterbot, i.e., it picks responses for any input provided to it. This parameter contains a list of logical operators. Chatterbot allows us to use a number of logical Adapters. When more than one logical adapter is put to use, the chatbot will calculate the confidence level, and the response with the highest calculated confidence will be returned as output. Here we have used two logical adapters: BestMatch and TimeLogicAdapter.

# Create object of ChatBot class with Logic Adapter
bot = ChatBot(
    'Buddy',  
    logic_adapters=[
        'chatterbot.logic.BestMatch',
        'chatterbot.logic.TimeLogicAdapter'],
)

Training the chatbot

Now the final step in making a chatbot is to train the chatbot using the modules available in chatterbot. Training a chatbot using chatterbot is as simple as providing a conversation into the chatbot database. As soon as the chatbot is given a dataset, it produces the essential entries in the chatbot's knowledge graph to represent the input and output in the right manner. Firstly, let's import the ListTrainer, create its object by passing the Chatbot object, and call the train() method by passing a list of sentences.

# Inport ListTrainer
from chatterbot.trainers import ListTrainer

trainer = ListTrainer(bot)

trainer.train([
'Hi',
'Hello',
'I need your assistance regarding my order',
'Please, Provide me with your order id',
'I have a complaint.',
'Please elaborate, your concern',
'How long it will take to receive an order ?',
'An order takes 3-5 Business days to get delivered.',
'Okay Thanks',
'No Problem! Have a Good Day!'
])

List Trainer: [####################] 100%

Chatbot Testing

The last step of this tutorial is to test the chatterbot’s conversational skills. For testing its responses, we will call the get_responses() method of Chatbot instance.

# Get a response to the input text 'I would like to book a flight.'
response = bot.get_response('I have a complaint.')

print("Bot Response:", response)

Bot Response: Please elaborate, your concern

We will create a while loop for our chatbot to run in. When statements are passed in the loop, we will get an appropriate response for it, as we have already entered data into our database. If we get "Bye" or "bye" statement from the user, we can put an end to the loop and stop the program.

name=input("Enter Your Name: ")
print("Welcome to the Bot Service! Let me know how can I help you?")
while True:
    request=input(name+':')
    if request=='Bye' or request =='bye':
        print('Bot: Bye')
        break
    else:
        response=bot.get_response(request)
        print('Bot:',response)

OUTPUT :

Enter Your Name: Avinash
Welcome to the Bot Service! Let me know how can I help you?
Avinash:I need your assistance regarding my order
Bot: Please, Provide me with your order id
Avinash:12345
Bot: No Problem! Have a Good Day!
Avinash:Bye
Bot: Bye