chatbot errors

[francisco_neves2020]

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..")

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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
>>>