ChatBot.py

# -*- coding: utf-8 -*-
"""
Created on Thu Mar 12 00:01:25 2020

@author: Rahul
"""
import nltk

#import libraries
from newspaper import Article
import random
import string 
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
import numpy as np
import warnings
warnings.filterwarnings('ignore')


nltk.download('punkt', quiet=True) # Download the punkt package
nltk.download('wordnet', quiet=True) # Download the wordnet package

#Get the article URL
article = Article('https://www.mayoclinic.org/diseases-conditions/chronic-kidney-disease/symptoms-causes/syc-20354521')
article.download() #Download the article
article.parse() #Parse the article
article.nlp() #Apply Natural Language Processing (NLP)
corpus = article.text #Store the article text into corpus

print(corpus)


#Tokenization
text = corpus
sent_tokens = nltk.sent_tokenize(text)# txt to a list of sentences

#Print the list of sentences
print(sent_tokens)


# Keyword Matching
#Greeting input from the user
GREETING_INPUTS = ["hi", "hello",  "hola", "greetings",  "wassup","hey"] 
#Greeting responses back to the user
GREETING_RESPONSES = ["howdy","hi", "hey", "what's good",  "hello","hey there"]
#Function to return a random greeting response to a users greeting
def greeting(sentence):
   #If user's input is a greeting, return a randomly chosen greeting response
    for word in sentence.split():
        if word.lower() in GREETING_INPUTS:
            return random.choice(GREETING_RESPONSES)
        
        
#Create a dictionary (key:value pair) to remove punctuations  
remove_punct_dict = dict(  (ord(punct), None) for punct in string.punctuation
                         
#Create a function to return a list of lemmatized lower case words after removing punctuations 
def LemNormalize(text):
    return nltk.word_tokenize(text.lower().translate(remove_punct_dict))


# Generating response
def response(user_response):
    robo_response='' #Create an empty response for the bot
    sent_tokens.append(user_response) #Append the users response to the list of sentence tokens
    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english') 
    tfidf = TfidfVec.fit_transform(sent_tokens)
    vals = cosine_similarity(tfidf[-1], tfidf)
    idx=vals.argsort()[0][-2]
    flat = vals.flatten()
    flat.sort()
    score = flat[-2]
    if(score==0):
        robo_response=robo_response+"I apologize, I don't understand."
    else:
        robo_response = robo_response+sent_tokens[idx]
    sent_tokens.remove(user_response) 
       
    return robo_response


    flag=True
    print("DOCBot: I am DOCTOR BOT or DOCBot for short. I will answer your queries about Chronic Kidney Disease. If you want to exit, type Bye!")
    while(flag==True):
        user_response = input()
        user_response=user_response.lower()
        if(user_response!='bye'):
            if(user_response=='thanks' or user_response=='thank you' ):
                flag=False
                print("DOCBot: You're welcome !")
                else:
                    if(greeting(user_response)!=None):
                        print("DOCBot: "+greeting(user_response))
                        else:
                            print("DOCBot: "+response(user_response))
                            else:
                                flag=False
                                print("DOCBot: Chat with you later !")