11

AiChatterbot.py

@@ -8,21 +8,17 @@ import pyttsx3
 from tkinter import *
 from tkinter import messagebox
 import speech_recognition as sr
-from tkinter import Text, Scrollbar, Entry, Button
+from tkinter import Text, Entry, Button
-import nltk
+
-from nltk.tokenize import word_tokenize
+framer = 16000
-from nltk.corpus import stopwords
+num_samples = 2000
-from nltk.stem import WordNetLemmatizer
-
-# Constants for audio recording
-framer = 16000  # Sample rate
-num_samples = 2000  # Sample points
 channels = 1
 sampwidth = 2
-audio_file = 'myvoices.wav'  # Specify the path to save the recorded audio file
+APP_ID = '78542969'
-APP_ID = '73927317'
+API_KEY = 'B37LWVxLfoHixHx52OrNCPij'
-API_KEY = '3jGcj5fLma64CtB3tTEuLcei'
+SECRET_KEY = 'bkNnbC37K3Ac6gI8kQ8phG1h5RIivMwj'
-SECRET_KEY = 'qm8gPCF7DSKpqatx5ZQ8e4OvNLmgdYcG'  # Add your own API credentials here
+client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
+audio_file = '../pythonProject6/myvoices.wav'
 
 # Class for handling audio recording and saving
 class Speak():
@@ -39,16 +35,14 @@ class Speak():
         stream = pa.open(format=paInt16, channels=channels, rate=framer, input=True, frames_per_buffer=num_samples)
         my_buf = []
         t_end = time.time() + 5
-        print('Speak now...')
+        print('识别中...')
         while time.time() < t_end:
             string_audio_data = stream.read(num_samples)
             my_buf.append(string_audio_data)
-        print('Recording ended')
+        print('识别完成')
         self.save_wave_file(audio_file, my_buf)
         stream.close()
 
-client = AipSpeech('73927317', '3jGcj5fLma64CtB3tTEuLcei', 'qm8gPCF7DSKpqatx5ZQ8e4OvNLmgdYcG')
-
 class RobotSay():
     def __init__(self):
         self.engine = pyttsx3.init()
@@ -58,12 +52,17 @@ class RobotSay():
         self.engine.runAndWait()
 
 class ReadWav():
+    def __init__(self):
+        self.APP_ID = '78542969'
+        self.API_KEY = 'B37LWVxLfoHixHx52OrNCPij'
+        self.SECRET_KEY = 'bkNnbC37K3Ac6gI8kQ8phG1h5RIivMwj'
+        self.client = AipSpeech(self.APP_ID, self.API_KEY, self.SECRET_KEY)
     def get_file_content(self, filePath):
         with open(filePath, 'rb') as fp:
             return fp.read()
 
     def predict(self):
-        return client.asr(self.get_file_content('myvoices.wav'), 'wav', 16000, {
+        return client.asr(self.get_file_content('../pythonProject6/myvoices.wav'), 'wav', 16000, {
             'dev_pid': 1537,
         })
 
@@ -144,16 +143,38 @@ class ChatWindow:
                                           port=3306)
         self.cursor = self.connection.cursor()
         self.chat_history = []
+
+        self.voice_button = Button(self.master, text="语音对话", command=self.start_voice_conversation)
+        self.voice_button.pack()
+
+        self.APP_ID = APP_ID
+        self.API_KEY = API_KEY
+        self.SECRET_KEY = SECRET_KEY
+        self.client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
+        '''
         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("你: " + user_input + "\n")
         self.update_chat("菲菲: " + response + "\n")
         self.speak(response)
+        self.save_to_database(user_input, response)
 
+    def start_voice_conversation(self):
+        # Implement voice conversation functionality
+        self.speak("开始语音对话，请说话...")
+        speak_instance = Speak()
+        speak_instance.my_record()
+        text = self.transcribe_audio('../pythonProject6/myvoices.wav')
+        self.update_chat("你说: " + text + "\n")
+        response = self.get_qingyunke_response(text)
+        self.update_chat("菲菲: " + response + "\n")
+        self.speak(response)
+        self.save_to_database(text, response)
     def get_qingyunke_response(self, message):
         api_url = "http://api.qingyunke.com/api.php?key=free&appid=0&msg=" + message
         response = requests.get(api_url)
@@ -172,10 +193,15 @@ class ChatWindow:
         self.engine.runAndWait()
 
     def transcribe_audio(self, audio_file):
-        with sr.AudioFile(audio_file) as source:
+        with open(audio_file, 'rb') as f:
-            audio_data = self.recognizer.record(source)
+            audio_data = f.read()
-            text = self.recognizer.recognize_sphinx(audio_data)
+        result = self.client.asr(audio_data, 'wav', 16000, {
-        return text
+            'dev_pid': 1537,  # Use the model for Mandarin
+        })
+        if 'result' in result:
+            return result['result'][0]
+        else:
+            return "Baidu speech recognition failed"
     #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)"
@@ -214,20 +240,25 @@ class ChatWindow:
         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
+    #知识图谱模块
-        tokens = word_tokenize(user_input)
+    def fetch_knowledge_graph_info(self, query):
-        # 去除停用词
+        # Assuming you are using Google Knowledge Graph API
-        stop_words = set(stopwords.words('english'))
+        api_key = 'YOUR_API_KEY'
-        filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
+        base_url = 'https://kgsearch.googleapis.com/v1/entities:search'
-        # 词形还原
+
-        lemmatizer = WordNetLemmatizer()
+        params = {
-        lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]
+            'query': query,
-        # 将处理后的文本重新组合为句子
+            'key': api_key,
-        processed_input = ' '.join(lemmatized_tokens)
+            'limit': 1  # You can adjust the limit based on response requirements
-        # 返回处理后的文本
+        }
-        return processed_input
+
-user_input = "Can you tell me about machine learning?"
+        response = requests.get(base_url, params=params).json()
+
+        if 'itemListElement' in response:
+            item = response['itemListElement'][0]
+            if 'result' in item:
+                return item['result']['name'], item['result']['detailedDescription']['articleBody']
 def main():
     root = Tk()
     login_window = LoginWindow(root)