import nltk
import numpy as np
import random
import string
import bs4 as bs
import urllib.request
import re
from urllib.parse import quote
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
q = "Mustafa_Kemal_Atatürk"
source = urllib.request.urlopen('https://en.wikipedia.org/wiki/'+ quote(q)).read()
soup = bs.BeautifulSoup(source,'lxml')
text = ""
for paragraph in soup.find_all('p'):
text += paragraph.text
raw=text.lower()
nltk.download('punkt')
nltk.download('wordnet')
sent_tokens = nltk.sent_tokenize(raw)
word_tokens = nltk.word_tokenize(raw)
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)
def LemNormalize(text):
return LemTokens(nltk.word_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)
def response(user_response):
robo_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"
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:
sent_tokens.append(user_response)
word_tokens=word_tokens+nltk.word_tokenize(user_response)
final_words=list(set(word_tokens))
print("ROBO: ",end="")
print(response(user_response))
sent_tokens.remove(user_response)
else:
flag=False
print("ROBO: Bye! take care..")
aldığım hata:C:\Users\Efe(Muhtar)\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\feature_extraction\text.py:388: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [‘ha’, ‘le’, ‘u’, ‘wa’] not in stop_words.
