import os
import torch
import shutil
import librosa
import numpy as np
import gradio as gr
import librosa.display
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from collections import Counter
from model import EvalNet
from PIL import Image
from utils import *
import warnings

warnings.filterwarnings("ignore")


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=0.07):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)
        log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
        dur = librosa.get_duration(y=y, 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=0.07):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        cqt_spec = librosa.cqt(y=y, sr=sr)
        log_cqt_spec = librosa.power_to_db(np.abs(cqt_spec) ** 2, ref=np.max)
        dur = librosa.get_duration(y=y, 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=0.07):
    os.makedirs("./tmp", exist_ok=True)
    try:
        y, sr = librosa.load(audio_path, sr=48000)
        chroma_spec = librosa.feature.chroma_stft(y=y, sr=sr)
        log_chroma_spec = librosa.power_to_db(np.abs(chroma_spec) ** 2, ref=np.max)
        dur = librosa.get_duration(y=y, 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, log_name, folder_path="./tmp"):
    if os.path.exists(folder_path):
        shutil.rmtree(folder_path)

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

    model = EvalNet(log_name).model
    spec = log_name.split("_")[-3]
    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]]


classes = ["m_chest", "f_chest", "m_falsetto", "f_falsetto"]

models = get_modelist()

translate = {
    "m_chest": "male chest voice",
    "f_chest": "female chest voice",
    "m_falsetto": "male falsetto voice",
    "f_falsetto": "female falsetto voice",
}

examples = []
example_wavs = find_wav_files()
for wav in example_wavs:
    examples.append([wav, models[0]])

with gr.Blocks() as demo:
    gr.Markdown(
        """
**Please note: It may take longer to obtain recognition results when using the selected model for the first time, as downloading weights is required. Please be patient while waiting for the results.**
"""
    )

    gr.Interface(
        fn=inference,
        inputs=[
            gr.Audio(label="Upload singing voice recording", type="filepath"),
            gr.Dropdown(choices=models, label="Select model", value=models[0]),
        ],
        outputs=[
            gr.Textbox(label="Audio filename", show_copy_button=True),
            gr.Textbox(label="Singing method recognition", show_copy_button=True),
        ],
        examples=examples,
    )

demo.launch()