import gradio as gr
from huggingface_hub import snapshot_download
from threading import Thread
import time
import base64
import numpy as np
import requests
import traceback
from dataclasses import dataclass
from pathlib import Path
import io
import wave
import tempfile
from pydub import AudioSegment
import librosa
from utils.vad import get_speech_timestamps, collect_chunks, VadOptions


from server import serve

repo_id = "gpt-omni/mini-omni"
snapshot_download(repo_id, local_dir="./checkpoint", revision="main")

IP = "0.0.0.0"
PORT = 60808

thread = Thread(target=serve, daemon=True)
thread.start()

API_URL = "http://0.0.0.0:60808/chat"

# recording parameters
IN_CHANNELS = 1
IN_RATE = 24000
IN_CHUNK = 1024
IN_SAMPLE_WIDTH = 2
VAD_STRIDE = 0.5

# playing parameters
OUT_CHANNELS = 1
OUT_RATE = 24000
OUT_SAMPLE_WIDTH = 2
OUT_CHUNK = 5760


OUT_CHUNK = 20 * 4096
OUT_RATE = 24000
OUT_CHANNELS = 1


def run_vad(ori_audio, sr):
    _st = time.time()
    try:
        audio = ori_audio
        audio = audio.astype(np.float32) / 32768.0
        sampling_rate = 16000
        if sr != sampling_rate:
            audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate)

        vad_parameters = {}
        vad_parameters = VadOptions(**vad_parameters)
        speech_chunks = get_speech_timestamps(audio, vad_parameters)
        audio = collect_chunks(audio, speech_chunks)
        duration_after_vad = audio.shape[0] / sampling_rate

        if sr != sampling_rate:
            # resample to original sampling rate
            vad_audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=sr)
        else:
            vad_audio = audio
        vad_audio = np.round(vad_audio * 32768.0).astype(np.int16)
        vad_audio_bytes = vad_audio.tobytes()

        return duration_after_vad, vad_audio_bytes, round(time.time() - _st, 4)
    except Exception as e:
        msg = f"[asr vad error] audio_len: {len(ori_audio)/(sr*2):.3f} s, trace: {traceback.format_exc()}"
        print(msg)
        return -1, ori_audio, round(time.time() - _st, 4)


def warm_up():
    frames = b"\x00\x00" * 1024 * 2  # 1024 frames of 2 bytes each
    dur, frames, tcost = run_vad(frames, 16000)
    print(f"warm up done, time_cost: {tcost:.3f} s")


warm_up()


def determine_pause(audio: np.ndarray, sampling_rate: int) -> bool:
    """Take in the stream, determine if a pause happened"""

    temp_audio = audio
    
    dur_vad, _, time_vad = run_vad(temp_audio, sampling_rate)
    duration = len(audio) / sampling_rate

    print(f"duration_after_vad: {dur_vad:.3f} s, time_vad: {time_vad:.3f} s")

    return (duration - dur_vad) > 0.5


def speaking(audio: np.ndarray, sampling_rate: int):
    audio_buffer = io.BytesIO()
    
    audio = AudioSegment(
        audio.tobytes(),
        frame_rate=sampling_rate,
        sample_width=data.dtype.itemsize,
        channels=(1 if len(data.shape) == 1 else data.shape[1]),
    )
    file = audio.export(audio_buffer, format="wav")

    with open("input_audio.wav", "wb") as f:
        f.write(audio_buffer.getvalue())

    audio_bytes = audio_buffer.getvalue()

    base64_encoded = str(base64.b64encode(audio_bytes), encoding="utf-8")
    files = {"audio": base64_encoded}
    with requests.post(API_URL, json=files, stream=True) as response:
        try:
            for chunk in response.iter_content(chunk_size=OUT_CHUNK):
                if chunk:
                    # Create an audio segment from the numpy array
                    audio_segment = AudioSegment(
                        chunk,
                        frame_rate=OUT_RATE,
                        sample_width=OUT_SAMPLE_WIDTH,
                        channels=OUT_CHANNELS,
                    )

                    # Export the audio segment to MP3 bytes - use a high bitrate to maximise quality
                    mp3_io = io.BytesIO()
                    audio_segment.export(mp3_io, format="mp3", bitrate="320k")

                    # Get the MP3 bytes
                    mp3_bytes = mp3_io.getvalue()
                    mp3_io.close()
                    yield mp3_bytes

        except Exception as e:
            raise gr.Error(f"Error during audio streaming: {e}")



@dataclass
class AppState:
    stream: np.ndarray | None = None
    sampling_rate: int = 0
    pause_detected: bool = False


def process_audio(audio: tuple, state: AppState):
    if state.stream is None:
        state.stream = audio[1]
        state.sampling_rate = audio[0]
    else:
        state.stream =  np.concatenate((state.stream, audio[1]))

    pause_detected = determine_pause(state.stream, state.sampling_rate)
    state.pause_detected = pause_detected

    if state.pause_detected:
        return gr.Audio(recording=False), state
    return None, state


def response(state: AppState):
    if not state.pause_detected:
        return None, None, AppState()

    for mp3_bytes in speaking(state.stream, state.sampling_rate):
        yield None, mp3_bytes, state
    
    yield gr.Audio(recording=True), None, AppState()


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            input_audio = gr.Audio(
                label="Input Audio", sources="microphone", type="numpy"
            )
        with gr.Column():
            output_audio = gr.Audio(label="Output Audio", streaming=True, autoplay=True)
    state = gr.State(value=AppState())

    stream = input_audio.stream(
        process_audio,
        [input_audio, state],
        [input_audio, state],
        stream_every=0.5,
        time_limit=30,
    )
    respond = input_audio.stop_recording(
        response,
        [state],
        [input_audio, output_audio, state]
    )
    cancel = gr.Button("Stop Conversation", variant="stop")
    cancel.click(lambda: AppState(), None, [state], cancels=[respond])


demo.launch()