Python chatbot

[Pankajcharak1999]

updated chatbot

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

#ifnore any warning messages warnings.filterwarnings('ignore')

#download the packages from nltk nltk.download('punkt',quiet=True) nltk.download('wordnet',quiet=True)

article = Article('https://www.mayoclinic.org/diseases-conditions/chronic-kidney-disease/symptoms-causes/syc-20354521') article.download() article.parse() article.nlp() corpus = article.text

#print the corpus/text

print(corpus)

#Tokenization text = corpus #convert the text into a list of sentence sent_tokens = nltk.sent_tokenize(text)

#print the list of sentence

print(sent_tokens)

create a doictionary(key:value) pair to remove punctuations

remove_punct_dict = dict( (punct,None) for punct in string.punctuation)

#print the punctuations

print(string.punctuation)

print(remove_punct_dict)

#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))

#print the tokenizaation text

print(LemNormalize(text))

#keyword matching #Greetin inputs greeting_inputs = ["hi","hello","greetings","wassup","hey"] #greetind response back to the user greeting_response = ["howdy","hi","hey","what's good","hello","hey there"] #function to return a random greeting response to a user greeting def greeting(sentence): for word in sentence.split(): if word.lower() in greeting_inputs: return random.choice(greeting_response)

#generate the response def response(user_response): #The users response/query user_response = 'what is a chronic disease' #Make the response lower case user_response = user_response.lower() #print the user response # print(user_response)

#set the chatbot response to an empty string
robo_response = ""

#append the user response to the sentense list
sent_tokens.append(user_response)

 # print(sent_tokens)

#create a TfidfVectorizer object
TfidfVec = TfidfVectorizer(tokenizer = LemNormalize, stop_words='english')

#convert the text to a matrix of Tf-IDF features
tfidf = TfidfVec.fit_transform(sent_tokens)

#print the TFIDF features
# print(tfidf)

#get the measure of similarity
vals = cosine_similarity(tfidf[-1], tfidf)

#print the similarity scores
# print(vals)

#get the index of the most similar text/sentence to the users response
idx = vals.argsort()[0][-2]

#reduce the dimensionality off vlas
flat = vals.flatten()

#sort the list in ascending order
flat.sort()

#get the most similar score to the users response
score = flat[-2]
#print the similarity score
# print(score)

#if the variable score is 0 then their is no text similar to the user response
if(score == 0):
    robo_response = robo_response+"I apologize, I don't understand."
else:
    robo_response = robo_response+sent_tokens[idx]

#print the chat bot response
#print(robo_response)

#remove the users reponse form the sentence token list
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 the chronic disease.If you need any help we here to response") 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 are 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 !")