chatbot.py

#from https://medium.com/analytics-vidhya/building-a-simple-chatbot-in-python-using-nltk-7c8c8215ac6e

import nltk
import numpy as np
import random
import string

# preprocess

data = open('chatbot.txt', 'r', errors = 'ignore')
raw = data.read()
raw = raw.lower()
nltk.download('punkt') # first-time use only
nltk.download('wordnet')
nltk.download('words')

sent_tokens = nltk.sent_tokenize(raw)  # converts to list of sentences
word_tokens = nltk.word_tokenize(raw)  # converts to list of words

#print(sentence_tokens)

lemmer = nltk.stem.WordNetLemmatizer()

def LemTokens(tokens):
    return [lemmer.lemmatize(token) for token in tokens]
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
#print(remove_punct_dict)
def LemNormalize(text):
    return LemTokens(nltk.corpus.words.words(nltk.tokenize(text.lower().translate(remove_punct_dict))))
greeting_inputs = ("hello", "hi", "greetings", "sup", "what's up", "hey")
greeting_responses = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad you are talking to me"]

def greeting(sentence):
    for word in sentence.split():
        if word.lower() in greeting_inputs:
            return random.choice(greeting_responses)

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity


def response(user_response):
    robo_response=''
    sent_tokens.append(user_response)
    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()
    req_tfidf = flat[-2]
    if(req_tfidf==0):
        robo_response=robo_response+"I am sorry! I don't understand you"
        return robo_response
    else:
        robo_response = robo_response+sent_tokens[idx]
        return robo_response


flag=True
print("ROBO: My name is Robo. I will answer your queries about Chatbots. 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("ROBO: You are welcome..")
        else:
            if(greeting(user_response)!=None):
                print("ROBO: "+greeting(user_response))
            else:
                print("ROBO: ",end="")
                print(response(user_response))
                sent_tokens.remove(user_response)
    else:
        flag=False
        print("ROBO: Bye! take care..")