使用 Python 创建一个基于规则的聊天机器人

while True: 
  AI = input('我：') 
  print(AI.replace("吗", " ").replace('?','!').replace('？','！')) 

import bs4 as bs 
import urllib.request#Open the cat WEB data page 
cat_data = urllib.request.urlopen('https://simple.wikipedia.org/wiki/Cat').read() 
#Find all the paragraph html from the web page 
cat_data_paragraphs = bs.BeautifulSoup(cat_data,'lxml').find_all('p') 
#Creating the corpus of all the web page paragraphs 
cat_text = '' 
#Creating lower text corpus of cat paragraphs 
for p in cat_data_paragraphs: 
cat_text += p.text.lower() 
print(cat_text) 

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

import nltk 
cat_sentences = nltk.sent_tokenize(cat_text) 

from sklearn.metrics.pairwise import cosine_similarity 
from sklearn.feature_extraction.text import TfidfVectorizer 
def chatbot_answer(user_query): 
 
#Append the query to the sentences list 
cat_sentences.append(user_query)  
#Create the sentences vector based on the list 
vectorizer = TfidfVectorizer() 
sentences_vectors = vectorizer.fit_transfORM(cat_sentences) 
 
#Measure the cosine similarity and take the second closest index because the first index is the user query 
vector_values = cosine_similarity(sentences_vectors[-1], sentences_vectors) 
answer = cat_sentences[vector_values.argsort()[0][-2]]  
#Final check to make sure there are result present. If all the result are 0, means the text input by us are not captured in the corpus 
input_check = vector_values.flatten() 
input_check.sort() 
 
if input_check[-2] == 0: 
return "Please Try again" 
else:  
return answer 

print("Hello, I am the Cat Chatbot. What is your meow questions?:") 
while(True): 
query = input().lower() 
if query not in ['bye', 'Good bye', 'take care']: 
print("Cat Chatbot: ", end="") 
print(chatbot_answer(query)) 
cat_sentences.remove(query) 
else: 
print("See You Again") 
break