import os
import io
import wave
import audioop
import numpy as np

from openai import AsyncOpenAI
import chainlit as cl
import requests


url = "http://127.0.0.1:8888/api/v1/run/03d6265d-87cc-48af-9457-709f7bb288d8"  # Replace with the link to your flow

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

openai_client = AsyncOpenAI(api_key=OPENAI_API_KEY)
CHUNK_SIZE = 1024

if not OPENAI_API_KEY:
    raise ValueError(
        "OPENAI_API_KEY must be set"
    )

# Define a threshold for detecting silence and a timeout for ending a turn
SILENCE_THRESHOLD = (
    3500  # Adjust based on your audio level (e.g., lower for quieter audio)
)
SILENCE_TIMEOUT = 1300.0  # Seconds of silence to consider the turn finished


@cl.step(type="tool")
async def speech_to_text(audio_file):
    response = await openai_client.audio.transcriptions.create(
        model="whisper-1", file=audio_file
    )

    return response.text


@cl.step(type="tool")
async def text_to_speech(text: str, mime_type: str):
    async with openai_client.audio.speech.with_streaming_response.create(
        model="gpt-4o-mini-tts",
        voice="coral",
        input=text,
        instructions="Speak in a cheerful and positive tone.",
    ) as response:
        buffer = io.BytesIO()
        buffer.name = "output.wav"

        async for chunk in response.iter_bytes(chunk_size=CHUNK_SIZE):
            if chunk:
                buffer.write(chunk)

        buffer.seek(0)
        return buffer.name, buffer.read()


@cl.step(type="tool")
async def generate_text_answer(transcription):
    
    # Request payload configuration
    payload = {
        "input_value": transcription,  
        "output_type": "chat",  
        "input_type": "chat"  
    }

    # Request headers
    headers = {
        "Content-Type": "application/json"
    }
    text ="" #Add this line
    try:
        # Send API request
        response = requests.request("POST", url, json=payload, headers=headers)
        response.raise_for_status()  # Raise exception for bad status codes

        text= response.json()["outputs"][0]["outputs"][0]["results"]["message"]["text"] #Add this line
        
    except requests.exceptions.RequestException as e:
        print(f"Error making API request: {e}")
        
    except ValueError as e:
        print(f"Error parsing response: {e}")

    return text


@cl.on_chat_start
async def start():
    cl.user_session.set("message_history", [])
    await cl.Message(
        content="Welcome to Chainlit x Whisper example! Press `p` to talk!",
    ).send()


@cl.on_audio_start
async def on_audio_start():
    cl.user_session.set("silent_duration_ms", 0)
    cl.user_session.set("is_speaking", False)
    cl.user_session.set("audio_chunks", [])
    return True


@cl.on_audio_chunk
async def on_audio_chunk(chunk: cl.InputAudioChunk):
    audio_chunks = cl.user_session.get("audio_chunks")

    if audio_chunks is not None:
        audio_chunk = np.frombuffer(chunk.data, dtype=np.int16)
        audio_chunks.append(audio_chunk)

    # If this is the first chunk, initialize timers and state
    if chunk.isStart:
        cl.user_session.set("last_elapsed_time", chunk.elapsedTime)
        cl.user_session.set("is_speaking", True)
        return

    audio_chunks = cl.user_session.get("audio_chunks")
    last_elapsed_time = cl.user_session.get("last_elapsed_time")
    silent_duration_ms = cl.user_session.get("silent_duration_ms")
    is_speaking = cl.user_session.get("is_speaking")

    # Calculate the time difference between this chunk and the previous one
    time_diff_ms = chunk.elapsedTime - last_elapsed_time
    cl.user_session.set("last_elapsed_time", chunk.elapsedTime)

    # Compute the RMS (root mean square) energy of the audio chunk
    audio_energy = audioop.rms(
        chunk.data, 2
    )  # Assumes 16-bit audio (2 bytes per sample)

    if audio_energy < SILENCE_THRESHOLD:
        # Audio is considered silent
        silent_duration_ms += time_diff_ms
        cl.user_session.set("silent_duration_ms", silent_duration_ms)
        if silent_duration_ms >= SILENCE_TIMEOUT and is_speaking:
            cl.user_session.set("is_speaking", False)
            await process_audio()
    else:
        # Audio is not silent, reset silence timer and mark as speaking
        cl.user_session.set("silent_duration_ms", 0)
        if not is_speaking:
            cl.user_session.set("is_speaking", True)


async def process_audio():
    # Get the audio buffer from the session
    if audio_chunks := cl.user_session.get("audio_chunks"):
        # Concatenate all chunks
        concatenated = np.concatenate(list(audio_chunks))

        # Create an in-memory binary stream
        wav_buffer = io.BytesIO()

        # Create WAV file with proper parameters
        with wave.open(wav_buffer, "wb") as wav_file:
            wav_file.setnchannels(1)  # mono
            wav_file.setsampwidth(2)  # 2 bytes per sample (16-bit)
            wav_file.setframerate(24000)  # sample rate (24kHz PCM)
            wav_file.writeframes(concatenated.tobytes())

        # Reset buffer position
        wav_buffer.seek(0)

        cl.user_session.set("audio_chunks", [])

    frames = wav_file.getnframes()
    rate = wav_file.getframerate()

    duration = frames / float(rate)
    if duration <= 1.71:
        print("The audio is too short, please try again.")
        return

    audio_buffer = wav_buffer.getvalue()

    input_audio_el = cl.Audio(content=audio_buffer, mime="audio/wav")

    whisper_input = ("audio.wav", audio_buffer, "audio/wav")
    transcription = await speech_to_text(whisper_input)

    await cl.Message(
        author="You",
        type="user_message",
        content=transcription,
        elements=[input_audio_el],
    ).send()

    answer = await generate_text_answer(transcription)

    output_name, output_audio = await text_to_speech(answer, "audio/wav")

    output_audio_el = cl.Audio(
        auto_play=True,
        mime="audio/wav",
        content=output_audio,
    )

    await cl.Message(content=answer, elements=[output_audio_el]).send()


@cl.on_message
async def on_message(message: cl.Message):
    await cl.Message(content="This is a voice demo, press P to start!").send()