# To be able to convert text to Speech
!pip install SpeechRecognition  #(3.8.1)
#To convey the Speech to text and also speak it out
!pip install gTTS  #(2.2.3)
# To install our language model
!pip install transformers  #(4.11.3)
!pip install tensorflow #(2.6.0, or pytorch)

import numpy as np
# Beginning of the AI
class ChatBot():
    def __init__(self, name):
        print("----- starting up", name, "-----")
        self.name = name
# Execute the AI
if __name__ == "__main__":
    ai = ChatBot(name="Dev")

import speech_recognition as sr
def speech_to_text(self):
    recognizer = sr.Recognizer()
    with sr.Microphone() as mic:
         print("listening...")
         audio = recognizer.listen(mic)
    try:
         self.text = recognizer.recognize_google(audio)
         print("me --> ", self.text)
    except:
         print("me -->  ERROR")
         # Execute the AI
if __name__ == "__main__":
     ai = ChatBot(name="Dev")
     while True:
         ai.speech_to_text()
         def wake_up(self, text):
    return True if self.name in text.lower() else False
    from gtts import gTTS
import os
@staticmethod
def text_to_speech(text):
    print("AI --> ", text)
    speaker = gTTS(text=text, lang="en", slow=False)
    speaker.save("res.mp3")
    os.system("start res.mp3")  #if you have a macbook->afplay or for windows use->start
    os.remove("res.mp3")
    #Those two functions can be used like this
# Execute the AI
if __name__ == "__main__":
     ai = ChatBot(name="Dev")
     while True:
         ai.speech_to_text()
         ## wake up
         if ai.wake_up(ai.text) is True:
             res = "Hello I am Dev the AI, what can I do for you?"
         ai.text_to_speech(res)
         import datetime
@staticmethod
def action_time():
    return datetime.datetime.now().time().strftime('%H:%M')
#and run the script after adding the above function to the AI class
# Run the AI
if __name__ == "__main__":
ai = ChatBot(name="Dev")
while True:
         ai.speech_to_text()
         ## waking up
         if ai.wake_up(ai.text) is True:
             res = "Hello I am Dev the AI, what can I do for you?"
         ## do any action
         elif "time" in ai.text:
            res = ai.action_time()
         ## respond politely
         elif any(i in ai.text for i in ["thank","thanks"]):
            res = np.random.choice(
                  ["you're welcome!","anytime!",
                   "no problem!","cool!",
                   "I'm here if you need me!","peace out!"])
         ai.text_to_speech(res)
         import transformers
nlp = transformers.pipeline("conversational", 
                            model="microsoft/DialoGPT-medium")
#Time to try it out
input_text = "hello!"
nlp(transformers.Conversation(input_text), pad_token_id=50256)
chat = nlp(transformers.Conversation(ai.text), pad_token_id=50256)
res = str(chat)
res = res[res.find("bot >> ")+6:].strip()
----- starting up Dev -----
listening...
me --> Hello!
AI --> Hello :D
listening...



# for speech-to-text
import speech_recognition as sr
# for text-to-speech
from gtts import gTTS
# for language model
import transformers
import os
import time
# for data
import os
import datetime
import numpy as np
# Building the AI
class ChatBot():
    def __init__(self, name):
        print("----- Starting up", name, "-----")
        self.name = name
    def speech_to_text(self):
        recognizer = sr.Recognizer()
        with sr.Microphone() as mic:
            print("Listening...")
            audio = recognizer.listen(mic)
            self.text="ERROR"
        try:
            self.text = recognizer.recognize_google(audio)
            print("Me  --> ", self.text)
        except:
            print("Me  -->  ERROR")
    @staticmethod
    def text_to_speech(text):
        print("Dev --> ", text)
        speaker = gTTS(text=text, lang="en", slow=False)
        speaker.save("res.mp3")
        statbuf = os.stat("res.mp3")
        mbytes = statbuf.st_size / 1024
        duration = mbytes / 200
        os.system('start res.mp3')  #if you are using mac->afplay or else for windows->start
        # os.system("close res.mp3")
        time.sleep(int(50*duration))
        os.remove("res.mp3")
    def wake_up(self, text):
        return True if self.name in text.lower() else False
    @staticmethod
    def action_time():
        return datetime.datetime.now().time().strftime('%H:%M')
# Running the AI
if __name__ == "__main__":
    ai = ChatBot(name="dev")
    nlp = transformers.pipeline("conversational", model="microsoft/DialoGPT-medium")
    os.environ["TOKENIZERS_PARALLELISM"] = "true"
    ex=True
    while ex:
        ai.speech_to_text()
        ## wake up
        if ai.wake_up(ai.text) is True:
            res = "Hello I am Dave the AI, what can I do for you?"
        ## action time
        elif "time" in ai.text:
            res = ai.action_time()
        ## respond politely
        elif any(i in ai.text for i in ["thank","thanks"]):
            res = np.random.choice(["you're welcome!","anytime!","no problem!","cool!","I'm here if you need me!","mention not"])
        elif any(i in ai.text for i in ["exit","close"]):
            res = np.random.choice(["Tata","Have a good day","Bye","Goodbye","Hope to meet soon","peace out!"])
            ex=False
        ## conversation
        else:   
            if ai.text=="ERROR":
                res="Sorry, come again?"
            else:
                chat = nlp(transformers.Conversation(ai.text), pad_token_id=50256)
                res = str(chat)
                res = res[res.find("bot >> ")+6:].strip()
        ai.text_to_speech(res)
    print("----- Closing down Dev -----")