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
import torchvision.transforms as transforms
from utils import get_modelist, find_wav_files
from collections import Counter
from model import EvalNet
from PIL import Image


CLASSES = ["m_bel", "f_bel", "m_folk", "f_folk"]


def most_common_element(input_list):
    # 使用 Counter 统计每个元素的出现次数
    counter = Counter(input_list)
    # 使用 most_common 方法获取出现次数最多的元素
    most_common_element, _ = counter.most_common(1)[0]
    return most_common_element


def wav_to_mel(audio_path: str, width=1.6, topdb=40):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        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"./tmp/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 wav_to_cqt(audio_path: str, width=1.6, topdb=40):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        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"./tmp/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 wav_to_chroma(audio_path: str, width=1.6, topdb=40):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        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"./tmp/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 embed_img(img_path, input_size=224):
    transform = transforms.Compose(
        [
            transforms.Resize([input_size, input_size]),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ]
    )
    img = Image.open(img_path).convert("RGB")
    return transform(img).unsqueeze(0)


def inference(wav_path: str, log_name: str, folder_path="./tmp"):
    if os.path.exists(folder_path):
        shutil.rmtree(folder_path)

    if not wav_path:
        wav_path = "./examples/f_bel.wav"

    model = EvalNet(log_name).model
    spec = log_name.split("_")[-1]
    eval("wav_to_%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 = model(input)
            pred_id = torch.max(output.data, 1)[1]
            outputs.append(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()
    translate = {
        "m_bel": "Male bel canto",
        "m_folk": "Male folk singing",
        "f_bel": "Female bel canto",
        "f_folk": "Female folk singing",
    }
    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=inference,
            inputs=[
                gr.Audio(label="Uploading a recording", type="filepath"),
                gr.Dropdown(choices=models, label="Select a model", value=models[0]),
            ],
            outputs=[
                gr.Textbox(label="Audio filename", show_copy_button=True),
                gr.Textbox(label="Singing style recognition", show_copy_button=True),
            ],
            examples=examples,
            allow_flagging="never",
            title="It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
        )

    demo.launch()