#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import asyncio
import base64
import time
from enum import Enum
from io import BytesIO
import os
from os import times
from pathlib import Path
import shutil
import sys
from typing import List
import uuid

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../"))

import edge_tts
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np

import streamlit as st
from streamlit_shortcuts import shortcut_button
from streamlit_webrtc import webrtc_streamer

from project_settings import project_path, temp_dir

# ENTRYPOINT ["streamlit", "run", "streamlit/nx_noise_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
# streamlit run streamlit/nx_speech_app.py --server.port=8501 --server.address=0.0.0.0


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--src_dir",
        default=(project_path / "data/speech/en-PH/2025-01-14/2025-01-14").as_posix(),
        type=str
    )
    parser.add_argument(
        "--tgt_dir",
        default=(project_path / "data/speech/en-PH/2025-01-14/2025-01-14/finished").as_posix(),
        type=str
    )
    args = parser.parse_args()
    return args


class Labels(Enum):
    speech = "speech"
    noise = "noise"


async def edge_tts_text_to_speech(text: str, speaker: str = "zh-CN-XiaoxiaoNeural"):
    communicate = edge_tts.Communicate(text, speaker)

    audio_file = temp_dir / f"{uuid.uuid4()}.wav"
    audio_file = audio_file.as_posix()

    await communicate.save(audio_file)
    return audio_file


def generate_spectrogram(filename: str, title: str = "Spectrogram"):
    signal, sample_rate = librosa.load(filename, sr=None)

    mag = np.abs(librosa.stft(signal))
    # mag_db = librosa.amplitude_to_db(mag, ref=np.max)
    mag_db = librosa.amplitude_to_db(mag, ref=20)

    plt.figure(figsize=(10, 4))
    librosa.display.specshow(mag_db, sr=sample_rate)
    plt.title(title)

    buf = BytesIO()
    plt.savefig(buf, format="png", bbox_inches="tight")
    plt.close()
    buf.seek(0)
    return buf


@st.cache_data
def get_shortcut_audio_dict():
    result = {
        Labels.speech.value: asyncio.run(edge_tts_text_to_speech("语音")),
        Labels.noise.value: asyncio.run(edge_tts_text_to_speech("噪音")),
    }
    return result

shortcut_audio_dict = get_shortcut_audio_dict()


def main():
    args = get_args()

    src_dir = Path(args.src_dir)
    tgt_dir = Path(args.tgt_dir)

    # 获取文件列表
    audio_files: List[Path] = [filename for filename in src_dir.glob("**/*.wav")]
    if len(audio_files) == 0:
        st.error("没有未标注的音频了。")
        st.stop()

    audio_file: Path = audio_files[0]

    # session_state
    if "play_audio" not in st.session_state:
        st.session_state.play_audio = False

    # ui
    st.title("🔊 音频文件浏览器")
    column1, column2 = st.columns([4, 4])
    with column1:
        st.audio(audio_file, format=f"{audio_file.suffix}", autoplay=True)

        with st.spinner("生成频谱图中..."):
            spectrogram = generate_spectrogram(audio_file)
        st.image(spectrogram, use_container_width=True)

        # hidden
        audio_placeholder = st.empty()

    # function
    def shortcut_audio_play(filename: str):
        with open(filename, "rb") as f:
            data = f.read()
        b64 = base64.b64encode(data).decode()

        audio_html = f"""
        <div hidden>
            <audio id="hiddenAudio" controls autoplay>
                <source src="data:audio/wav;base64,{b64}" type="audio/wav">
            </audio>
        </div>
        <script>
            document.getElementById("hiddenAudio").play()
        </script>
        """
        audio_placeholder.markdown(audio_html, unsafe_allow_html=True)
        return

    def when_click_annotation_button(filename: Path, label: str, tgt_dir: Path):
        shortcut_audio = shortcut_audio_dict[label]
        shortcut_audio_play(shortcut_audio)
        time.sleep(1)

        sub_tgt_dir = tgt_dir / label
        sub_tgt_dir.mkdir(parents=True, exist_ok=True)
        shutil.move(filename.as_posix(), sub_tgt_dir)

    # control
    with column2:
        shortcut_button(
            label=Labels.speech.value,
            shortcut="1",
            on_click=when_click_annotation_button,
            kwargs={
                "filename": audio_file,
                "label": Labels.speech.value,
                "tgt_dir": tgt_dir,
            },
            type="primary",
        )
        shortcut_button(
            shortcut="2",
            label=Labels.noise.value,
            on_click=when_click_annotation_button,
            kwargs={
                "filename": audio_file,
                "label": Labels.noise.value,
                "tgt_dir": tgt_dir,
            },
            type="primary",
        )

    return


if __name__ == "__main__":
    main()