如何将Tkinter聊天窗口与Chatbot文件的I/O进行关联？

核心实现步骤 #

• 解耦聊天逻辑与控制台交互：移除chatbot.py中用于控制台交互的while循环和print语句，保留核心的响应生成逻辑（greeting、response函数）及初始化代码。
• GUI调用聊天机器人逻辑：在chatbotGUI.py中导入chatbot模块，通过调用其函数处理用户输入并获取回复。
• 修改发送函数：更新send函数，在插入用户输入后，调用聊天机器人的逻辑生成回复，再将回复插入聊天窗口。
• 添加初始问候：GUI启动时自动显示机器人的初始欢迎语。

修改后的代码 #

1. chatbot.py（保留核心逻辑，移除控制台循环）

#Meet Reach: your friend

#import necessary libraries
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

# uncomment the following only the first time
#nltk.download('punkt') # first-time use only
#nltk.download('wordnet') # first-time use only


#Reading in the corpus
with open('chatbot.txt','r', encoding='utf8', errors ='ignore') as fin:
    raw = fin.read().lower()

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

# Preprocessing
lemmer = 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)))


# Keyword Matching
GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey", "whats up", "hi reach", "hello reach")
GREETING_RESPONSES = ["Hi", "Hey", "Hi there", "Hello"]

def greeting(sentence):
    """If user's input is a greeting, return a greeting response"""
    for word in sentence.split():
        if word.lower() in GREETING_INPUTS:
            return random.choice(GREETING_RESPONSES)


# Generating response
def response(user_response):
    reach_response=''
    user_response = user_response.lower()
    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 (user_response=="what is military reach?" or user_response=="what is military reach"):
        reach_response = "Military REACH is an organization that works to bridge the gap between military family research and practice."
        reach_response = reach_response + "\nOur goal is to make research more accessible for military families."
        reach_response = reach_response + "\n\nIs there anything else that I can help you with?"
        return reach_response
    elif (user_response=="how many documents does military reach have?" or user_response=="how many documents does military reach have"):
        reach_response = "Military REACH has a database of over 6,000 documents containing research summaries, reports, newsletters, and more!"
        reach_response = reach_response + "\n\nIs there anything else that I can help you with?"
        return reach_response
    elif(req_tfidf==0):
        reach_response=reach_response+"I'm not quite sure.  Let me look and get back to you!"
        return reach_response
    else:
        reach_response = reach_response+sent_tokens[idx]
        sent_tokens.remove(user_response)
        return reach_response

def get_initial_greeting():
    return "Hello, my name is Reach. I will answer your questions about Military REACH. If you want to exit, type Bye!"

2. chatbotGUI.py（集成聊天机器人逻辑）

#Description: This is a chat bot GUI

#Import the library
from tkinter import *
import chatbot

root = Tk()

root.title("Military REACH Chat Bot")
root.geometry("600x800")
root.resizable(width=FALSE, height=FALSE)

main_menu = Menu(root)

# Create the submenu 
file_menu = Menu(root)

# Add commands to submenu
file_menu.add_command(label="New..")
file_menu.add_command(label="Save As..")
file_menu.add_command(label="Exit", command=root.quit)
main_menu.add_cascade(label="File", menu=file_menu)
#Add the rest of the menu options to the main menu
main_menu.add_command(label="Edit")
main_menu.add_command(label="Quit", command=root.quit)
root.config(menu=main_menu)

messageWindow = Entry(root, bd=0, bg="white",width="30", font=("Arial", 16), foreground="black")
messageWindow.place(x=128, y=710, height=88, width=450)

chatWindow = Text(root, bd=1, bg="white",  width="550", height="100", font=("Arial", 16), foreground="black")
chatWindow.place(x=6,y=6, height=700, width=570)
# 设置文本框不可编辑，仅用于展示
chatWindow.config(state=DISABLED)

scrollbar = Scrollbar(root, command=chatWindow.yview, cursor="arrow")
scrollbar.place(x=575,y=6, height=700)

def insert_message(sender, message):
    """统一插入消息到聊天窗口的函数"""
    chatWindow.config(state=NORMAL)
    chatWindow.insert(END, f"{sender}: {message}\n\n")
    chatWindow.config(state=DISABLED)
    # 自动滚动到底部
    chatWindow.see(END)

def send(event=None):
    user_input = messageWindow.get().strip()
    if not user_input:
        return
    # 插入用户输入
    insert_message("You", user_input)
    messageWindow.delete(0, END)
    
    # 处理退出逻辑
    if user_input.lower() == 'bye':
        insert_message("REACH", "Bye! Take care.")
        root.after(1000, root.quit)
        return
    
    # 获取机器人回复
    bot_reply = ""
    if user_input.lower() in ['thanks', 'thank you']:
        bot_reply = "You are welcome. Do you need anything else?"
    else:
        greet_reply = chatbot.greeting(user_input)
        if greet_reply:
            bot_reply = greet_reply
        else:
            bot_reply = chatbot.response(user_input)
    
    # 插入机器人回复
    insert_message("REACH", bot_reply)

# 显示初始问候语
insert_message("REACH", chatbot.get_initial_greeting())

Button= Button(root, text="Send",  width="12", height=3,
                    bd=0, bg="green", activebackground="green",foreground='#000000',font=("Arial", 12), command=send)
Button.place(x=6, y=710, height=88)
root.bind('<Return>', send)

root.mainloop()

关键说明 #

• chatbot.py中新增了get_initial_greeting函数，用于返回初始欢迎语，同时在response函数中提前处理了输入的小写转换，避免重复操作。
• chatbotGUI.py中新增了insert_message函数，统一处理消息插入逻辑，同时设置聊天窗口为不可编辑状态，防止用户修改历史消息。
• 完善了退出逻辑：用户输入bye时，机器人回复后自动退出GUI；处理感谢类输入的专属回复。

内容的提问来源于stack exchange，提问作者Thomas Murphy