import os
import torch
import random
import shutil
import librosa
import warnings
import numpy as np
import gradio as gr
import librosa.display
import matplotlib.pyplot as plt
from utils import get_modelist, find_wav_files, embed_img, TEMP_DIR
from collections import Counter
from model import EvalNet


TRANSLATE = {
    "m_bel": "男声美声唱法 (Bel Canto, Male)",
    "f_bel": "女声美声唱法 (Bel Canto, Female)",
    "m_folk": "男声民族唱法 (Folk Singing, Male)",
    "f_folk": "女声民族唱法 (Folk Singing, Female)",
}
CLASSES = list(TRANSLATE.keys())
SAMPLE_RATE = 22050


def wav2mel(audio_path: str, width=1.6, topdb=40):
    os.makedirs(TEMP_DIR, exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
        non_silents = librosa.effects.split(y, top_db=topdb)
        non_silent = np.concatenate([y[start:end] for start, end in non_silents])
        mel_spec = librosa.feature.melspectrogram(y=non_silent, sr=sr)
        log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
        dur = librosa.get_duration(y=non_silent, sr=sr)
        total_frames = log_mel_spec.shape[1]
        step = int(width * total_frames / dur)
        count = int(total_frames / step)
        begin = int(0.5 * (total_frames - count * step))
        end = begin + step * count
        for i in range(begin, end, step):
            librosa.display.specshow(log_mel_spec[:, i : i + step])
            plt.axis("off")
            plt.savefig(
                f"{TEMP_DIR}/mel_{round(dur, 2)}_{i}.jpg",
                bbox_inches="tight",
                pad_inches=0.0,
            )
            plt.close()

    except Exception as e:
        print(f"Error converting {audio_path} : {e}")


def wav2cqt(audio_path: str, width=1.6, topdb=40):
    os.makedirs(TEMP_DIR, exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
        non_silents = librosa.effects.split(y, top_db=topdb)
        non_silent = np.concatenate([y[start:end] for start, end in non_silents])
        cqt_spec = librosa.cqt(y=non_silent, sr=sr)
        log_cqt_spec = librosa.power_to_db(np.abs(cqt_spec) ** 2, ref=np.max)
        dur = librosa.get_duration(y=non_silent, sr=sr)
        total_frames = log_cqt_spec.shape[1]
        step = int(width * total_frames / dur)
        count = int(total_frames / step)
        begin = int(0.5 * (total_frames - count * step))
        end = begin + step * count
        for i in range(begin, end, step):
            librosa.display.specshow(log_cqt_spec[:, i : i + step])
            plt.axis("off")
            plt.savefig(
                f"{TEMP_DIR}/cqt_{round(dur, 2)}_{i}.jpg",
                bbox_inches="tight",
                pad_inches=0.0,
            )
            plt.close()

    except Exception as e:
        print(f"Error converting {audio_path} : {e}")


def wav2chroma(audio_path: str, width=1.6, topdb=40):
    os.makedirs(TEMP_DIR, exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
        non_silents = librosa.effects.split(y, top_db=topdb)
        non_silent = np.concatenate([y[start:end] for start, end in non_silents])
        chroma_spec = librosa.feature.chroma_stft(y=non_silent, sr=sr)
        log_chroma_spec = librosa.power_to_db(np.abs(chroma_spec) ** 2, ref=np.max)
        dur = librosa.get_duration(y=non_silent, sr=sr)
        total_frames = log_chroma_spec.shape[1]
        step = int(width * total_frames / dur)
        count = int(total_frames / step)
        begin = int(0.5 * (total_frames - count * step))
        end = begin + step * count
        for i in range(begin, end, step):
            librosa.display.specshow(log_chroma_spec[:, i : i + step])
            plt.axis("off")
            plt.savefig(
                f"{TEMP_DIR}/chroma_{round(dur, 2)}_{i}.jpg",
                bbox_inches="tight",
                pad_inches=0.0,
            )
            plt.close()

    except Exception as e:
        print(f"Error converting {audio_path} : {e}")


def most_common_element(input_list: list):
    counter = Counter(input_list)
    mce, _ = counter.most_common(1)[0]
    return mce


def infer(wav_path: str, log_name: str, folder_path=TEMP_DIR):
    if os.path.exists(folder_path):
        shutil.rmtree(folder_path)

    if not wav_path:
        return None, "请输入音频 Please input an audio!"

    try:
        model = EvalNet(log_name, len(TRANSLATE)).model

    except Exception as e:
        return None, f"{e}"

    spec = log_name.split("_")[-3]
    eval("wav2%s" % spec)(wav_path)
    outputs = []
    all_files = os.listdir(folder_path)
    for file_name in all_files:
        if file_name.lower().endswith(".jpg"):
            file_path = os.path.join(folder_path, file_name)
            input = embed_img(file_path)
            output: torch.Tensor = model(input)
            pred_id = torch.max(output.data, 1)[1]
            outputs.append(int(pred_id))

    max_count_item = most_common_element(outputs)
    shutil.rmtree(folder_path)
    return os.path.basename(wav_path), TRANSLATE[CLASSES[max_count_item]]


if __name__ == "__main__":
    warnings.filterwarnings("ignore")
    models = get_modelist()
    examples = []
    example_wavs = find_wav_files()
    model_num = len(models)
    for wav in example_wavs:
        examples.append([wav, models[random.randint(0, model_num - 1)]])

    with gr.Blocks() as demo:
        gr.Interface(
            fn=infer,
            inputs=[
                gr.Audio(label="上传录音 Upload a recording (>40dB)", type="filepath"),
                gr.Dropdown(
                    choices=models, label="选择模型 Select a model", value=models[0]
                ),
            ],
            outputs=[
                gr.Textbox(label="音频文件名 Audio filename", show_copy_button=True),
                gr.Textbox(
                    label="唱法识别 Singing method recognition", show_copy_button=True
                ),
            ],
            examples=examples,
            cache_examples=False,
            allow_flagging="never",
            title="建议录音时长保持在 5s 左右, 过长会影响识别效率<br>It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
        )

        gr.Markdown(
            """
# 引用 Cite
```bibtex
@dataset{zhaorui_liu_2021_5676893,
  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}
```"""
        )

    demo.launch()