11

AiChatterbot.py

@@ -1,3 +1,4 @@
+import pymysql
 import time
 import wave
 import requests
@@ -7,20 +8,18 @@ import pyttsx3
 from tkinter import *
 from tkinter import messagebox
 import speech_recognition as sr
+from tkinter import Text, Scrollbar, Entry, Button
 import nltk
 from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
-from nltk.tokenize import RegexpTokenizer
 from nltk.stem import WordNetLemmatizer
-from nltk.probability import FreqDist
-import string
 
 # Constants for audio recording
 framer = 16000  # Sample rate
 num_samples = 2000  # Sample points
 channels = 1
 sampwidth = 2
-FILEPATH = 'myvoices.wav'  # Specify the path to save the recorded audio file
+audio_file = 'myvoices.wav'  # Specify the path to save the recorded audio file
 APP_ID = '73927317'
 API_KEY = '3jGcj5fLma64CtB3tTEuLcei'
 SECRET_KEY = 'qm8gPCF7DSKpqatx5ZQ8e4OvNLmgdYcG'  # Add your own API credentials here
@@ -38,29 +37,26 @@ class Speak():
     def my_record(self):
         pa = PyAudio()
         stream = pa.open(format=paInt16, channels=channels, rate=framer, input=True, frames_per_buffer=num_samples)
-        my_buf = []  # Store recorded data
+        my_buf = []
-        t_end = time.time() + 5  # Record for 5 seconds
+        t_end = time.time() + 5
         print('Speak now...')
         while time.time() < t_end:
             string_audio_data = stream.read(num_samples)
             my_buf.append(string_audio_data)
         print('Recording ended')
-        self.save_wave_file(FILEPATH, my_buf)  # Save the recorded audio
+        self.save_wave_file(audio_file, my_buf)
         stream.close()
 
-# Initialize the AipSpeech client
+client = AipSpeech('73927317', '3jGcj5fLma64CtB3tTEuLcei', 'qm8gPCF7DSKpqatx5ZQ8e4OvNLmgdYcG')
-client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
 
-# Class for handling text-to-speech
 class RobotSay():
     def __init__(self):
-        self.engine = pyttsx3.init()  # Initialize TTS engine
+        self.engine = pyttsx3.init()
 
     def say(self, msg):
         self.engine.say(msg)
         self.engine.runAndWait()
 
-# Class for handling speech recognition
 class ReadWav():
     def get_file_content(self, filePath):
         with open(filePath, 'rb') as fp:
@@ -71,7 +67,7 @@ class ReadWav():
             'dev_pid': 1537,
         })
 
-# Define the login window
+# 定义登录窗口
 class LoginWindow:
     def __init__(self, master):
         self.master = master
@@ -97,11 +93,9 @@ class LoginWindow:
 
         self.button_login = Button(master, text="登录", command=self.login)
         self.button_login.pack()
-
     def login(self):
         username = self.entry_username.get()
         password = self.entry_password.get()
-        # You can add actual validation logic here by checking against a database or hardcoded values
         if username == "123" and password == "123":
             messagebox.showinfo("登陆成功", "恭喜你登陆成功")
             self.open_chat_window()
@@ -109,16 +103,22 @@ class LoginWindow:
             messagebox.showerror("登录失败", "无效的用户名或密码")
 
     def open_chat_window(self):
-        self.master.withdraw()  # Hide the login window
+        self.master.withdraw()
-        chat_root = Toplevel(self.master)  # Create a new window for chat
+        chat_root = Toplevel(self.master)
-        chat_window = ChatWindow(chat_root)  # Open the chat window
+        chat_window = ChatWindow(chat_root)
-        chat_root.protocol("WM_DELETE_WINDOW", self.master.quit)  # Close the entire application when chat window is closed
+        chat_root.protocol("WM_DELETE_WINDOW", self.master.quit)
 class ChatWindow:
     def __init__(self, master):
         self.master = master
         self.master.title("开始聊天")
         self.master.geometry("700x500")
 
+        self.screen_width = self.master.winfo_screenwidth()
+        self.screen_height = self.master.winfo_screenheight()
+        self.x_pos = (self.screen_width - 300) // 2
+        self.y_pos = (self.screen_height - 200) // 2
+        self.master.geometry(f"700x500+{self.x_pos}+{self.y_pos}")
+
         self.chat_text = Text(self.master)
         self.chat_text.pack()
 
@@ -128,15 +128,31 @@ class ChatWindow:
         self.send_button = Button(self.master, text="发送", command=self.send_message)
         self.send_button.pack()
 
+        self.view_button = Button(self.master, text="查看聊天记录", command=self.view_chat_history)
+        self.view_button.pack()
+
+        self.delete_button = Button(self.master, text="删除聊天记录", command=self.delete_chat_history)
+        self.delete_button.pack()
+
         self.recognizer = sr.Recognizer()
         self.engine = pyttsx3.init()
 
+        self.connection = pymysql.connect(host="localhost",
+                                          user="root",
+                                          password="myroot",
+                                          database="ai_chatter",
+                                          port=3306)
+        self.cursor = self.connection.cursor()
+        self.chat_history = []
+        nltk.download('punkt')
+        nltk.download('stopwords')
+        nltk.download('wordnet')
     def send_message(self):
         user_input = self.input_field.get()
         self.update_chat("你: " + user_input + "\n")
         response = self.get_qingyunke_response(user_input)
         self.update_chat("菲菲: " + response + "\n")
-        self.speak(response)  # Speaking the response
+        self.speak(response)
 
     def get_qingyunke_response(self, message):
         api_url = "http://api.qingyunke.com/api.php?key=free&appid=0&msg=" + message
@@ -149,36 +165,73 @@ class ChatWindow:
                 return "对不起，我不能理解."
         else:
             return "我目前无法处理您的请求."
-    # 下载NLTK数据
+    def update_chat(self, text):
-    nltk.download('stopwords')
+        self.chat_text.insert(END, text)
-    nltk.download('punkt')
+    def speak(self, text):
-    nltk.download('wordnet')
+        self.engine.say(text)
-    # 假设用户输入作为文本样本
+        self.engine.runAndWait()
-    user_input = "我想了解自然语言处理。你能帮助我吗？"
+
-    # 小写化
+    def transcribe_audio(self, audio_file):
-    user_input = user_input.lower()
+        with sr.AudioFile(audio_file) as source:
-    # 标点符号移除
+            audio_data = self.recognizer.record(source)
-    translator = str.maketrans('', '', string.punctuation)
+            text = self.recognizer.recognize_sphinx(audio_data)
-    user_input = user_input.translate(translator)
+        return text
-    # 分词
+    #使用mysql数据库来保存
+    def save_to_database(self, user_message, bot_response):
+        sql = "INSERT INTO chat_history (user_message, bot_response) VALUES (%s, %s)"
+        text = (user_message, bot_response)
+        self.connection.cursor().execute(sql, text)
+        self.connection.commit()
+        print("聊天记录保存与数据库中.")
+    def view_chat_history(self):
+        self.chat_text.delete('1.0', END)
+        sql = "SELECT * FROM ai_chatter.chat_history"
+        self.cursor.execute(sql)
+        records = self.cursor.fetchall()
+        for record in records:
+            self.update_chat("你: " + record[1] + "\n")
+            self.update_chat("菲菲: " + record[2] + "\n")
+        if self.chat_history:
+            self.update_chat("\n--- 聊天记录 ---\n")
+            for message in self.chat_history:
+                self.update_chat(message + "\n")
+        else:
+            self.update_chat("没有找到聊天记录\n")
+
+    def delete_chat_history(self):
+        sql = "DELETE FROM chat_history"
+        self.cursor.execute(sql)
+        self.connection.commit()
+        self.chat_text.delete('1.0', END)
+        self.update_chat("聊天记录已删除。\n")
+
+        if self.chat_history:
+            self.chat_history.clear()
+            self.update_chat("聊天记录已删除\n")
+        else:
+            self.update_chat("没有找到聊天记录\n")
+    def close_connection(self):
+        self.cursor.close()
+        self.connection.close()
+    #自然语言处理
+    def process_user_input(self, user_input):
+        # 对用户输入进行分词
         tokens = word_tokenize(user_input)
         # 去除停用词
         stop_words = set(stopwords.words('english'))
-    filtered_tokens = [word for word in tokens if word not in stop_words]
+        filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
         # 词形还原
         lemmatizer = WordNetLemmatizer()
         lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]
-    # 频率分布
+        # 将处理后的文本重新组合为句子
-    fdist = FreqDist(lemmatized_tokens)
+        processed_input = ' '.join(lemmatized_tokens)
-    print(fdist.most_common(5))
+        # 返回处理后的文本
-    def update_chat(self, text):
+        return processed_input
-        self.chat_text.insert(END, text)
+user_input = "Can you tell me about machine learning?"
-    def speak(self, text):
-        self.engine.say(text)
-        self.engine.runAndWait()
 def main():
     root = Tk()
     login_window = LoginWindow(root)
     root.mainloop()
 if __name__ == "__main__":
     main()
+