chatbot errors

[francisco_neves2020]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74

import nltk import numpy as np import random import string # to process standard python strings   f=open('chatbot.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   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']   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)))   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..")

---

I found this code online to create a bot, but i don't get the error, it works for first sentence and then dies.

---

Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>>
================= RESTART: C:\Users\Nelson\Desktop\robo.pyw =================
[nltk_data] Downloading package punkt to
[nltk_data] C:\Users\Nelson\AppData\Roaming\nltk_data...
[nltk_data] Package punkt is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data] C:\Users\Nelson\AppData\Roaming\nltk_data...
[nltk_data] Package wordnet is already up-to-date!
ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!
hi
ROBO: hello
how are you
ROBO:
Warning (from warnings module):
File "C:\Users\Nelson\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\feature_extraction\text.py", line 301
'stop_words.' % sorted(inconsistent))
UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.
Traceback (most recent call last):
File "C:\Users\Nelson\Desktop\robo.pyw", line 70, in <module>
print(response(user_response))
File "C:\Users\Nelson\Desktop\robo.pyw", line 43, in response
tfidf = TfidfVec.fit_transform(sent_tokens)
File "C:\Users\Nelson\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\feature_extraction\text.py", line 1613, in fit_transform
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
File "C:\Users\Nelson\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\feature_extraction\text.py", line 1031, in fit_transform
self.fixed_vocabulary_)
File "C:\Users\Nelson\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\feature_extraction\text.py", line 962, in _count_vocab
raise ValueError("empty vocabulary; perhaps the documents only"
ValueError: empty vocabulary; perhaps the documents only contain stop words
>>>