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README.md

chatbot

building a chatbot

import nltk import numpy as np import random import string

import bs4 as bs import urllib.request import re

import pandas as pd import nltk

print ("You have successfully imported pandas version "+pd.version) print ("You have successfully imported nltk version "+nltk.version)

load the csv file that contains the tweets on sexual harassments into a pandas dataframe

df_raw = pd.read_csv('./analysis-public-place-assaults-sexual-assaults-and-robberies-2015-csv.csv', encoding='latin-1')

print ("You have successfully loaded your csv file")

df_raw.head(5)

len(df_raw)

df_raw['Territorial_authority_area_2013_label'].head(8)

sample_tweet = df_raw.iloc[100] print('Before tokenization:', sample_tweet)

sample_tweet = df_raw.iloc[100] print('After tokenization:', sample_tweet)

stop = nltk.corpus.stopwords.words('english')

Add a few more stop words we would like to remove here

stop.append('@') stop.append('#') stop.append('http') stop.append(':') stop

Let us take two sentences to be the knowledge base, and one more which is a question.

Sentence_1='Workplace harassment is the belittling or threatening behavior directed at an individual worker or a group of workers' Sentence_2='Physical harassment in the workplace takes many forms' Question='In an informal survey among 50 employees in Singapore'

Vocabulary = ['workplace', 'is', 'among', 'informal', 'forms', 'individual', 'group']

bag_of_words = np.array([[1,1,1,1,0,0,0], [0,1,1,0,1,1,0], [0,1,1,1,0,0,1]])

bag_of_words[:3,:]

print (bag_of_words[-1,:].reshape(1, -1))

import matplotlib.pyplot as plt mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000)

the histogram of the data

n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75) plt.xlabel('workplace') plt.ylabel('gender ratio') plt.title('Histogram of sexual harassments') plt.text(60, .025, r'$\mu=100,\ \sigma=15$') plt.axis([40, 160, 0, 0.03]) plt.grid(True) plt.show()

import io 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 nltk.download('popular', quiet=True) # for downloading packages #nltk.download('punkt') # first-time use only #nltk.download('wordnet') # first-time use only

raw_html = urllib.request.urlopen('https://en.wikipedia.org/wiki/Workplace_harassment') raw_html = raw_html.read()

article_html = bs.BeautifulSoup(raw_html, 'lxml')

article_paragraphs = article_html.find_all('p')

article_text = ''

for para in article_paragraphs: article_text += para.text

article_text = article_text.lower()

f=open('workplace harassment.txt','r',errors = 'ignore') raw=f.read() raw = raw.lower()# converts to lowercase

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

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)))

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))) #see previous section 1.2.5 lemmatization

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)

article_text = re.sub(r'[[0-9]*]', ' ', article_text) article_text = re.sub(r'\s+', ' ', article_text)

article_sentences = nltk.sent_tokenize(article_text) article_words = nltk.word_tokenize(article_text)

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

def generate_response(user_input): cookie_response = '' article_sentences.append(user_input)

word_vectorizer = TfidfVectorizer(tokenizer=get_processed_text, stop_words='english')
all_word_vectors = word_vectorizer.fit_transform(article_sentences)
similar_vector_values = cosine_similarity(all_word_vectors[-1], all_word_vectors)
similar_sentence_number = similar_vector_values.argsort()[0][-2]

matched_vector = similar_vector_values.flatten()
matched_vector.sort()
vector_matched = matched_vector[-2]

if vector_matched == 0:
    cookie_response = cookie_response + "I am sorry, I could not understand you"
    return cookie_response
else:
    cookie_response = cookie_response + article_sentences[similar_sentence_number]
    return cookie_response

def greeting(sentence): for word in sentence.split(): # Looks at each word in your sentence if word.lower() in GREETING_INPUTS: # checks if the word matches a GREETING_INPUT return random.choice(GREETING_RESPONSES) # replies with a GREETING_RESPONSE

def response(user_response):

cookie_response='' # initialize a variable to contain string
sent_tokens.append(user_response) #add user response to sent_tokens
TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english') 
tfidf = TfidfVec.fit_transform(sent_tokens) #get tfidf value
vals = cosine_similarity(tfidf[-1], tfidf) #get cosine similarity value
idx=vals.argsort()[0][-2] 
flat = vals.flatten() 
flat.sort() #sort in ascending order
req_tfidf = flat[-2] 

if(req_tfidf==0):
    cookie_response=cookie_response+"I am sorry! I don't understand you"
    return cookie_response
else:
    cookie_response = cookie_response+sent_tokens[idx]
    return cookie_response

import datetime def tell_time(sentence): for word in sentence.split(): if word.lower() -- 'time': currentdt - datetime.datetime.now() return currentdt.strftime("%Y-%n-%d %H:%M:%S")

flag=True print("cookie: My name is cookie. I will answer your queries about Workplace Harassment. 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("cookie: You are welcome..") else: if(greeting(user_response)!=None): print("cookie: "+greeting(user_response)) else: print("cookie: ",end="") print(response(user_response)) sent_tokens.remove(user_response) else: flag=False print("cookie: Bye! take care..") Binder .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/raghavi5980/chatbot/HEAD

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