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import os
from dotenv import load_dotenv

import wave
import pyaudio
from scipy.io import wavfile
import numpy as np

import whisper

from langchain.chains.llm import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_groq import ChatGroq

from gtts import gTTS
import pygame
from groq import Groq


load_dotenv()

groq_api_key = os.getenv("GROQ_API_KEY")


def is_silence(data, max_amplitude_threshold=3000):
    """Check if audio data contains silence."""
    # Find the maximum absolute amplitude in the audio data
    max_amplitude = np.max(np.abs(data))
    return max_amplitude <= max_amplitude_threshold


def record_audio_chunk(audio, stream, chunk_length=5):
    print("Recording...")
    frames = []
    # Calculate the number of chunks needed for the specified length of recording
    # 16000 Hertz -> sufficient for capturing the human voice
    # 1024 frames -> the higher, the higher the latency
    num_chunks = int(16000 / 1024 * chunk_length)

    # Record the audio data in chunks
    for _ in range(num_chunks):
        data = stream.read(1024)
        frames.append(data)

    temp_file_path = os.path.dirname(__file__) + "/temp_audio_chunk.wav"
    # print(temp_file_path)
    print("Writing...")
    with wave.open(temp_file_path, 'wb') as wf:
        wf.setnchannels(1)  # Mono channel
        wf.setsampwidth(audio.get_sample_size(pyaudio.paInt16))  # Sample width
        wf.setframerate(16000)  # Sample rate
        wf.writeframes(b''.join(frames))  # Write audio frames

    # Check if the recorded chunk contains silence
    try:
        samplerate, data = wavfile.read(temp_file_path)
        if is_silence(data):
            # os.remove(temp_file_path)
            return True
        else:
            return False
    except Exception as e:
        print(f"Error while reading audio file: {e}")


def load_whisper():
    model = whisper.load_model("base")
    return model


def transcribe_audio(api_key, filename):
    print("Transcribing...")
    # Print all files in the current directory
    print("Current directory files:", os.listdir())
    if os.path.dirname(__file__) + "/"+ file_path:
        results = model.transcribe(file_path) # , fp16=False
        return results['text']
    else:
        return None

def load_prompt():
    input_prompt = """
    You are a friendly conversational chatbot
    Previous conversation:
    {chat_history}
    New human question: {question}
    Response:
    """
    return input_prompt


def load_llm():
    chat_groq = ChatGroq(temperature=0, model_name="llama3-8b-8192",
                         groq_api_key=groq_api_key)
    return chat_groq


def get_response_llm(user_question, memory):
    input_prompt = load_prompt()

    chat_groq = load_llm()

    # Look how "chat_history" is an input variable to the prompt template
    prompt = PromptTemplate.from_template(input_prompt)

    chain = LLMChain(
        llm=chat_groq,
        prompt=prompt,
        verbose=True,
        memory=memory
    )

    response = chain.invoke({"question": user_question})

    return response['text']


def play_text_to_speech(text, language='en', slow=False):
    # Generate text-to-speech audio from the provided text
    tts = gTTS(text=text, lang=language, slow=slow)

    # Save the generated audio to a temporary file
    temp_audio_file = "temp_audio.mp3"
    tts.save(temp_audio_file)

    # Initialize the pygame mixer for audio playback
    pygame.mixer.init()

    # Load the temporary audio file into the mixer
    pygame.mixer.music.load(temp_audio_file)

    # Start playing the audio
    pygame.mixer.music.play()

    # Wait until the audio playback finishes
    while pygame.mixer.music.get_busy():
        pygame.time.Clock().tick(10)  # Control the playback speed

    # Stop the audio playback
    pygame.mixer.music.stop()

    # Clean up: Quit the pygame mixer and remove the temporary audio file
    pygame.mixer.quit()
    os.remove(temp_audio_file)

	import os
	from dotenv import load_dotenv

	import wave
	import pyaudio
	from scipy.io import wavfile
	import numpy as np

	import whisper

	from langchain.chains.llm import LLMChain
	from langchain_core.prompts import PromptTemplate
	from langchain_groq import ChatGroq

	from gtts import gTTS
	import pygame
	from groq import Groq


	load_dotenv()

	groq_api_key = os.getenv("GROQ_API_KEY")


	def is_silence(data, max_amplitude_threshold=3000):
	"""Check if audio data contains silence."""
	# Find the maximum absolute amplitude in the audio data
	max_amplitude = np.max(np.abs(data))
	return max_amplitude <= max_amplitude_threshold


	def record_audio_chunk(audio, stream, chunk_length=5):
	print("Recording...")
	frames = []
	# Calculate the number of chunks needed for the specified length of recording
	# 16000 Hertz -> sufficient for capturing the human voice
	# 1024 frames -> the higher, the higher the latency
	num_chunks = int(16000 / 1024 * chunk_length)

	# Record the audio data in chunks
	for _ in range(num_chunks):
	data = stream.read(1024)
	frames.append(data)

	temp_file_path = os.path.dirname(__file__) + "/temp_audio_chunk.wav"
	# print(temp_file_path)
	print("Writing...")
	with wave.open(temp_file_path, 'wb') as wf:
	wf.setnchannels(1) # Mono channel
	wf.setsampwidth(audio.get_sample_size(pyaudio.paInt16)) # Sample width
	wf.setframerate(16000) # Sample rate
	wf.writeframes(b''.join(frames)) # Write audio frames

	# Check if the recorded chunk contains silence
	try:
	samplerate, data = wavfile.read(temp_file_path)
	if is_silence(data):
	# os.remove(temp_file_path)
	return True
	else:
	return False
	except Exception as e:
	print(f"Error while reading audio file: {e}")


	def load_whisper():
	model = whisper.load_model("base")
	return model


	def transcribe_audio(api_key, filename):
	print("Transcribing...")
	# Print all files in the current directory
	print("Current directory files:", os.listdir())
	if os.path.dirname(__file__) + "/"+ file_path:
	results = model.transcribe(file_path) # , fp16=False
	return results['text']
	else:
	return None

	def load_prompt():
	input_prompt = """

	You are a friendly conversational chatbot

	Previous conversation:
	{chat_history}

	New human question: {question}
	Response:
	"""
	return input_prompt


	def load_llm():
	chat_groq = ChatGroq(temperature=0, model_name="llama3-8b-8192",
	groq_api_key=groq_api_key)
	return chat_groq


	def get_response_llm(user_question, memory):
	input_prompt = load_prompt()

	chat_groq = load_llm()

	# Look how "chat_history" is an input variable to the prompt template
	prompt = PromptTemplate.from_template(input_prompt)

	chain = LLMChain(
	llm=chat_groq,
	prompt=prompt,
	verbose=True,
	memory=memory
	)

	response = chain.invoke({"question": user_question})

	return response['text']


	def play_text_to_speech(text, language='en', slow=False):
	# Generate text-to-speech audio from the provided text
	tts = gTTS(text=text, lang=language, slow=slow)

	# Save the generated audio to a temporary file
	temp_audio_file = "temp_audio.mp3"
	tts.save(temp_audio_file)

	# Initialize the pygame mixer for audio playback
	pygame.mixer.init()

	# Load the temporary audio file into the mixer
	pygame.mixer.music.load(temp_audio_file)

	# Start playing the audio
	pygame.mixer.music.play()

	# Wait until the audio playback finishes
	while pygame.mixer.music.get_busy():
	pygame.time.Clock().tick(10) # Control the playback speed

	# Stop the audio playback
	pygame.mixer.music.stop()

	# Clean up: Quit the pygame mixer and remove the temporary audio file
	pygame.mixer.quit()
	os.remove(temp_audio_file)