#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from functools import lru_cache
import json
import logging
from pathlib import Path
import platform
import tempfile
import time
import uuid

import gradio as gr
import librosa
import numpy as np
from scipy.io import wavfile

import log
from project_settings import environment, project_path, log_directory
from toolbox.os.command import Command
from toolbox.age_and_gender.models.audeering import AudeeringModel

log.setup_size_rotating(log_directory=log_directory)

logger = logging.getLogger("main")


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--examples_dir",
        default=(project_path / "data/examples").as_posix(),
        type=str,
    )
    args = parser.parse_args()
    return args


def save_input_audio(sample_rate: int, signal: np.ndarray) -> str:
    temp_audio_dir = Path(tempfile.gettempdir()) / "input_audio"
    temp_audio_dir.mkdir(parents=True, exist_ok=True)
    filename = temp_audio_dir / f"{uuid.uuid4()}.wav"
    filename = filename.as_posix()
    wavfile.write(
        filename,
        sample_rate, signal
    )
    return filename


def shell(cmd: str):
    return Command.popen(cmd)


age_and_gender_model_map = {
    "audeering-6-ft":{
        "infer_cls": AudeeringModel,
        "kwargs": {
            "model_path":
                (project_path / "pretrained_models/wav2vec2-large-robust-6-ft-age-gender").as_posix()
                if platform.system() == "Windows" else "audeering/wav2vec2-large-robust-6-ft-age-gender"
        },
        "sample_rate": 16000,
    },
    "audeering-24-ft": {
        "infer_cls": AudeeringModel,
        "kwargs": {
            "model_path":
                (project_path / "pretrained_models/wav2vec2-large-robust-24-ft-age-gender").as_posix()
                if platform.system() == "Windows" else "audeering/wav2vec2-large-robust-24-ft-age-gender",
        },
        "sample_rate": 16000,
    },
}


@lru_cache(maxsize=3)
def load_get_age_and_gender_model(infer_cls, **kwargs):
    infer_engine = infer_cls(**kwargs)

    return infer_engine


def when_click_get_age_and_gender_button(audio_t, engine: str):
    sample_rate, signal = audio_t
    filename = save_input_audio(sample_rate, signal)

    logger.info(f"run get_age_and_gender; engine: {engine}.")

    infer_engine_param = age_and_gender_model_map.get(engine)
    if infer_engine_param is None:
        raise gr.Error(f"invalid denoise engine: {engine}.")

    try:
        infer_cls = infer_engine_param["infer_cls"]
        kwargs = infer_engine_param["kwargs"]
        sample_rate = infer_engine_param["sample_rate"]

        signal, _ = librosa.load(filename, sr=sample_rate)
        duration = len(signal) / sample_rate

        infer_engine = load_get_age_and_gender_model(infer_cls=infer_cls, **kwargs)


        time_begin = time.time()
        age_and_gender = infer_engine.__call__(signal, sample_rate)
        time_cost = time.time() - time_begin

        rtf = time_cost / duration

        result = {
            **age_and_gender,
            "duration": round(duration, 4),
            "time_cost": round(time_cost, 4),
            "rtf": round(rtf, 4),
        }

        result = json.dumps(result, ensure_ascii=False, indent=4)
    except Exception as e:
        raise gr.Error(f"get_age_and_gender failed, error type: {type(e)}, error text: {str(e)}.")

    return result


def main():
    args = get_args()

    # examples
    examples_dir = Path(args.examples_dir)

    # choices
    age_and_gender_model_choices = list(age_and_gender_model_map.keys())

    # ui
    with gr.Blocks() as blocks:
        with gr.Tabs():
            with gr.TabItem("age_and_gender"):
                with gr.Row():
                    with gr.Column(variant="panel", scale=5):
                        ag_audio = gr.Audio(label="audio")
                        ag_engine = gr.Dropdown(choices=age_and_gender_model_choices, value=age_and_gender_model_choices[0], label="engine")
                        ag_button = gr.Button(variant="primary")
                    with gr.Column(variant="panel", scale=5):
                        ag_output = gr.Text(label="output")

                gr.Examples(
                    examples=[
                        [filename.as_posix(), age_and_gender_model_choices[0]]
                        for filename in examples_dir.glob("*.wav")
                    ],
                    inputs=[ag_audio, ag_engine],
                    outputs=[ag_output],
                    fn=when_click_get_age_and_gender_button,
                )
                ag_button.click(
                    when_click_get_age_and_gender_button,
                    inputs=[ag_audio, ag_engine],
                    outputs=[ag_output],
                )
            with gr.TabItem("shell"):
                shell_text = gr.Textbox(label="cmd")
                shell_button = gr.Button("run")
                shell_output = gr.Textbox(label="output")

                shell_button.click(
                    shell,
                    inputs=[shell_text,],
                    outputs=[shell_output],
                )

    # http://127.0.0.1:7860/
    # http://10.75.27.247:7861/
    blocks.queue().launch(
        share=False if platform.system() == "Windows" else False,
        # server_name="127.0.0.1" if platform.system() == "Windows" else "0.0.0.0",
        server_name="0.0.0.0",
        server_port=environment.get("port", 7860, dtype=int),
    )
    return


if __name__ == "__main__":
    main()