Merge branch 'IT20201364_Thathsarani_R.P.H.S.R'

IT20201364/voice_assistant.py

+import os
+import numpy as np
+import keras.models
+import speech_recognition as sr
+import transformers
+import threading
+from utils import wake_up, action_time, calculate_emotion_percentages, calculate_stress_level
+from record_analysis import record_and_analyze_user_input
+from text_to_speech import text_to_speech
+
+loaded_model = keras.models.load_model('C:/Users/dell/Desktop/F_Project/Virtual_Assistant/my_model.h5')
+
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
+
+conversation = []  # Initialize an empty list to store the conversation
+
+class ChatBot:
+    def __init__(self, name):
+        self.res = None
+        self.text = None
+        print("----- Starting up", name, "-----")
+        self.name = name
+        self.user_input_counter = 0
+        self.user_inputs = []
+        self.sentiment_analysis = []
+        self.record_thread = None
+
+    def speech_to_text(self):
+        recognizer = sr.Recognizer()
+        sr.Microphone()
+
+        with sr.Microphone() as mic:
+            print("Listening...")
+            try:
+                audio = recognizer.listen(mic)
+                self.text = recognizer.recognize_google(audio)
+                print("Me  -->", self.text)
+            except sr.UnknownValueError:
+                print("Me  --> Could not understand audio")
+            except sr.RequestError as e:
+                print(f"Me  --> Speech recognition request failed: {e}")
+            except Exception as e:
+                print(f"Me  --> An error occurred: {e}")
+
+    def run(self):
+
+        nlp = transformers.pipeline("conversational", model="microsoft/DialoGPT-medium")
+        tokenizer = nlp.tokenizer
+        tokenizer.padding_side = "left"  # Set padding side to left
+        os.environ["TOKENIZERS_PARALLELISM"] = "true"
+
+        # Initial AI response
+        initial_response = "Hello, I am zenBot. How can I help you?"
+        #conversation.append({'AI': initial_response})  # Add to conversation list
+        conversation.append('zenBot: ' + initial_response)
+
+        text_to_speech(initial_response)
+
+        while True:
+            self.speech_to_text()
+
+
+            if self.text:
+                print(f'User Input: {self.text}')
+                #conversation.append({'User': self.text})
+                conversation.append('User: ' + self.text)
+
+            if wake_up(self.text, self.name):
+                self.res = "Hello, I am an AI counselor. How can I help you?"
+            elif "time" in self.text:
+                self.res = action_time()
+            elif any(i in self.text for i in ["thank", "thanks"]):
+                self.res = np.random.choice(
+                    ["You're welcome!", "Anytime!", "No problem!", "Cool!", "I'm here if you need me!",
+                     "You're welcome!"]
+                )
+            elif any(i in self.text for i in ["exit", "close"]):
+                self.res = np.random.choice(
+                    ["Tata", "Have a good day", "Bye", "Goodbye", "Hope to meet soon", "Peace out!"]
+                )
+                text_to_speech(self.res)
+                break
+            else:
+                if self.text == "ERROR":
+                    self.res = "Sorry, come again?"
+                else:
+                    if self.record_thread and self.record_thread.is_alive():
+                        self.record_thread.join()  # Wait for the previous thread to finish if it's still running
+
+                    # Start a new thread to run record_and_analyze_user_input()
+                    self.record_thread = threading.Thread(target=record_and_analyze_user_input, args=(self,))
+                    self.record_thread.start()
+
+                    chat = nlp(transformers.Conversation(self.text), pad_token_id=50256)
+                    self.res = str(chat)
+                    self.res = self.res[self.res.find("bot >> ") + 7:].strip()
+
+                # Store the conversation in a list
+                #conversation.append({'AI': self.res})
+                conversation.append('zenBot: ' + self.res)
+
+                # Print the AI response immediately if it was added to the conversation
+                if self.res:
+                    print(f'AI Response: {self.res}')
+
+            text_to_speech(self.res)
+
+        overall_emotion = self.sentiment_analysis
+        stress_level = calculate_stress_level(self.sentiment_analysis)
+        print("Overall Emotion of the conversation:", overall_emotion)
+        print("stress level:", stress_level)
+        print("Conversation:", conversation)
+
+        # Print the last AI response after the conversation ends if it was added to the conversation
+        if self.res:
+            print(f'AI Response: {self.res}')
+
+        print("----- Closing down Dev -----")
+
+if __name__ == "__main__":
+    chatbot = ChatBot("AI Counselor")
+    chatbot.run()