EMOTIONAL_CHANGE_DIFFICULTY_IN_CONCENTRATION.py

#!/usr/bin/env python
# coding: utf-8

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import nltk
import numpy as np
import random
import string # to process standard python strings
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity


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f=open(r'/home/aakash/Documents/DASM/solutions/Emotional/diificulty in conc.txt','r',errors = 'ignore')

raw=f.read()

raw=raw.lower()# converts to lowercase

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

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


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sent_tokens[:2]
['a chatbot (also known as a talkbot, chatterbot, bot, im bot, interactive agent, or artificial conversational entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.',
 'such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the turing test.']

word_tokens[:2]
['a', 'chatbot', '(', 'also', 'known']


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lemmer = nltk.stem.WordNetLemmatizer()
#WordNet is a semantically-oriented dictionary of English included in NLTK.

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)))


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def difficult_in_concentration(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


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