MainBot.ipynb

#!/usr/bin/env python3
# -*- coding: utf-8 -*-


# Meet Robo: your friend

# import necessary libraries
import io
import os
import random
import string  # to process standard python strings
import warnings

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import warnings

warnings.filterwarnings('ignore')

import nltk
from nltk.stem import WordNetLemmatizer
from pathlib import Path


nltk.download('popular', quiet=True)  # for downloading packages

# uncomment the following only the first time
# nltk.download('punkt') # first-time use only
# nltk.download('wordnet') # first-time use only

# data_dir = "D:/CodeGrind/Data/Formatted_Data/"
filename ="demofile2.txt"
# Reading in the corpus
total_str = ''
# for file in os.listdir(data_dir):
    # filename = data_dir + "sample.txt"
    # print(filename)
data = open(filename, 'rt', encoding="utf-8", errors="ignore")
text = data.read()
data.close()
total_str = total_str + "\n" + text

raw = text.lower()

# print(raw)

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

# Preprocessing
lemmer = WordNetLemmatizer()


def LemTokens(tokens):
    return [lemmer.lemmatize(token) for token in tokens]


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


def LemNormalize(text):
    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))


# Keyword Matching
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):
    """If user's input is a greeting, return a greeting response"""
    for word in sentence.split():
        if word.lower() in GREETING_INPUTS:
            return random.choice(GREETING_RESPONSES)


# Generating response
def response(user_response):
    robo_response = ''

    sent_filt_tokens.append(user_response)
    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')
    tfidf = TfidfVec.fit_transform(sent_filt_tokens)
    # feature_names = tfidf.get_feature_names()
    # print(tfidf)
    vals = cosine_similarity(tfidf[-1], tfidf)
    # print(vals)
    # for a in vals[0]:
    # print(sent_tokens[int(a)])
    idx = vals.argsort()[0][-2]
    # print(idx)

    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_filt_tokens[idx]
        return robo_response


flag = True
print(" My name is BOT. 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()
    tokens_user = LemNormalize(user_response)
    # print(tokens_user)

    # print(token_data)
    if (user_response != 'bye'):
        if (user_response == 'thanks' or user_response == 'thank you'):
            flag = False
            print("BOT: You are welcome..")
        else:
            if (greeting(user_response) != None):
                print("BOT: " + greeting(user_response))
            else:

                sent_filt_tokens = []
                new_list = []
                sent_list = []
                Counter = 0
                # print(raw)
                for line in raw.splitlines():
                    # print(line)
                    # print(line.split(":"))
                    key_words = line.split(":")[0]
                    sent = line.split(":")[1]
                    words = key_words.split(" ")
                    x = set(words) & set(tokens_user)
                    new_list.append(x)
                    sent_list.append(sent)

                m = max(new_list)
                # print(new_list)
                # print(m)
                match_index = [i for i, j in enumerate(new_list) if j == m]

                for x in match_index:
                    sent_filt_tokens.append(sent_list[x])

                    # print(sent_filt_tokens)

                print("BOT: ", end="")
                print(response(user_response))
                sent_filt_tokens.remove(user_response)

    else:
        flag = False
        print("BOT: Bye! take care..")