diff --git a/AiChatterbot.py b/AiChatterbot.py index 7e71780..37572e0 100644 --- a/AiChatterbot.py +++ b/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 - t_end = time.time() + 5 # Record for 5 seconds + my_buf = [] + 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(APP_ID, API_KEY, SECRET_KEY) +client = AipSpeech('73927317', '3jGcj5fLma64CtB3tTEuLcei', 'qm8gPCF7DSKpqatx5ZQ8e4OvNLmgdYcG') -# 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 - chat_root = Toplevel(self.master) # Create a new window for chat - chat_window = ChatWindow(chat_root) # Open the chat window - chat_root.protocol("WM_DELETE_WINDOW", self.master.quit) # Close the entire application when chat window is closed + self.master.withdraw() + chat_root = Toplevel(self.master) + chat_window = ChatWindow(chat_root) + 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 - nltk.download('stopwords') - nltk.download('punkt') - nltk.download('wordnet') - # 假设用户输入作为文本样本 - user_input = "我想了解自然语言处理。你能帮助我吗？" - # 小写化 - user_input = user_input.lower() - # 标点符号移除 - translator = str.maketrans('', '', string.punctuation) - user_input = user_input.translate(translator) - # 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] - # 词形还原 - lemmatizer = WordNetLemmatizer() - lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens] - # 频率分布 - fdist = FreqDist(lemmatized_tokens) - print(fdist.most_common(5)) def update_chat(self, text): self.chat_text.insert(END, text) def speak(self, text): self.engine.say(text) self.engine.runAndWait() + + def transcribe_audio(self, audio_file): + with sr.AudioFile(audio_file) as source: + audio_data = self.recognizer.record(source) + text = self.recognizer.recognize_sphinx(audio_data) + return text + #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.lower() not in stop_words] + # 词形还原 + lemmatizer = WordNetLemmatizer() + lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens] + # 将处理后的文本重新组合为句子 + processed_input = ' '.join(lemmatized_tokens) + # 返回处理后的文本 + return processed_input +user_input = "Can you tell me about machine learning?" def main(): root = Tk() login_window = LoginWindow(root) root.mainloop() if __name__ == "__main__": - main() \ No newline at end of file + main() +