from typing import Union

from argparse import ArgumentParser
from pathlib import Path
import subprocess
import librosa
import os
import time
import random
import yt_dlp

from search import get_youtube, download_random
import soundfile

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from moviepy.editor import *
from moviepy.video.io.VideoFileClip import VideoFileClip
import moviepy.editor as mpe

import asyncio
import json
import hashlib
from os import path, getenv
from pydub import AudioSegment

import gradio as gr

import torch

import edge_tts

from datetime import datetime
from scipy.io.wavfile import write

import config
import util
from infer_pack.models import (
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono
)
from vc_infer_pipeline import VC

# music search

def auto_search(name):
    save_music_path = '/tmp/downloaded'
    if not os.path.exists(save_music_path):
        os.makedirs(save_music_path)

    config = {'logfilepath': 'musicdl.log', save_music_path: save_music_path, 'search_size_per_source': 5,
              'proxies': {}}
    save_path = os.path.join(save_music_path, name + '.mp3')
    # youtube
    get_youtube(name, os.path.join(save_music_path, name))
    # task1 = threading.Thread(
    #     target=get_youtube,
    #     args=(name, os.path.join(save_music_path, name))
    # )
    # task1.start()
    # task2 = threading.Thread(
    #     target=download_random,
    #     args=(name, config, save_path)
    # )
    # task2.start()
    # task1.join(timeout=20)
    # task2.join(timeout=10)

    if not os.path.exists(save_path):
        return "Not Found", None
    signal, sampling_rate = soundfile.read(save_path, dtype=np.int16)
    # signal, sampling_rate = open_audio(save_path)

    return (sampling_rate, signal)

# SadTalker

import os, sys
from src.gradio_demo import SadTalker  


try:
    import webui  # in webui
    in_webui = True
except:
    in_webui = False


def toggle_audio_file(choice):
    if choice == False:
        return gr.update(visible=True), gr.update(visible=False)
    else:
        return gr.update(visible=False), gr.update(visible=True)
    
def ref_video_fn(path_of_ref_video):
    if path_of_ref_video is not None:
        return gr.update(value=True)
    else:
        return gr.update(value=False)

sad_talker = SadTalker("checkpoints", "src/config", lazy_load=True)



# combine video with music

def combine_music(video, audio):
    my_clip = mpe.VideoFileClip(video)
    audio_background = mpe.AudioFileClip(audio)
    final_audio = mpe.CompositeAudioClip([my_clip.audio, audio_background])
    final_clip = my_clip.set_audio(final_audio)
    final_clip.write_videofile("video.mp4")
    return "video.mp4"

# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
in_hf_space = getenv('SYSTEM') == 'spaces'

high_quality = True

# Argument parsing
arg_parser = ArgumentParser()
arg_parser.add_argument(
    '--hubert',
    default=getenv('RVC_HUBERT', 'hubert_base.pt'),
    help='path to hubert base model (default: hubert_base.pt)'
)
arg_parser.add_argument(
    '--config',
    default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
    help='path to config file (default: multi_config.json)'
)
arg_parser.add_argument(
    '--api',
    action='store_true',
    help='enable api endpoint'
)
arg_parser.add_argument(
    '--cache-examples',
    action='store_true',
    help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified'  # noqa
)
args = arg_parser.parse_args()

app_css = '''
#model_info img {
    max-width: 100px;
    max-height: 100px;
    float: right;
}

#model_info p {
    margin: unset;
}
'''

app = gr.Blocks(
    theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
    css=app_css,
    analytics_enabled=False
)

# Load hubert model
hubert_model = util.load_hubert_model(config.device, args.hubert)
hubert_model.eval()

# Load models
multi_cfg = json.load(open(args.config, 'r'))
loaded_models = []

for model_name in multi_cfg.get('models'):
    print(f'Loading model: {model_name}')

    # Load model info
    model_info = json.load(
        open(path.join('model', model_name, 'config.json'), 'r')
    )

    # Load RVC checkpoint
    cpt = torch.load(
        path.join('model', model_name, model_info['model']),
        map_location='cpu'
    )
    tgt_sr = cpt['config'][-1]
    cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk

    if_f0 = cpt.get('f0', 1)
    net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
    if if_f0 == 1:
        net_g = SynthesizerTrnMs768NSFsid(
            *cpt['config'],
            is_half=util.is_half(config.device)
        )
    else:
        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])

    del net_g.enc_q

    # According to original code, this thing seems necessary.
    print(net_g.load_state_dict(cpt['weight'], strict=False))

    net_g.eval().to(config.device)
    net_g = net_g.half() if util.is_half(config.device) else net_g.float()

    vc = VC(tgt_sr, config)
    
    loaded_models.append(dict(
        name=model_name,
        metadata=model_info,
        vc=vc,
        net_g=net_g,
        if_f0=if_f0,
        target_sr=tgt_sr
    ))
        
print(f'Models loaded: {len(loaded_models)}')

# Edge TTS speakers
tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())  # noqa

# Make MV
def make_bars_image(height_values, index, new_height):
    
    # Define the size of the image
    width = 512  
    height = new_height
    
    # Create a new image with a transparent background
    image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0))
    
    # Get the image drawing context
    draw = ImageDraw.Draw(image)
    
    # Define the rectangle width and spacing
    rect_width = 2
    spacing = 2
    
    # Define the list of height values for the rectangles
    #height_values = [20, 40, 60, 80, 100, 80, 60, 40]
    num_bars = len(height_values)
    # Calculate the total width of the rectangles and the spacing
    total_width = num_bars * rect_width + (num_bars - 1) * spacing
    
    # Calculate the starting position for the first rectangle
    start_x = int((width - total_width) / 2)
    # Define the buffer size
    buffer_size = 80
    # Draw the rectangles from left to right
    x = start_x
    for i, height in enumerate(height_values):
        
        # Define the rectangle coordinates
        y0 = buffer_size
        y1 = height + buffer_size
        x0 = x
        x1 = x + rect_width

        # Draw the rectangle
        draw.rectangle([x0, y0, x1, y1], fill='white')  
        
        # Move to the next rectangle position
        if i < num_bars - 1:
            x += rect_width + spacing
        

    # Rotate the image by 180 degrees
    image = image.rotate(180)
    
    # Mirror the image
    image = image.transpose(Image.FLIP_LEFT_RIGHT)
    
    # Save the image
    image.save('audio_bars_'+ str(index) + '.png')

    return 'audio_bars_'+ str(index) + '.png'

def db_to_height(db_value):
    # Scale the dB value to a range between 0 and 1
    scaled_value = (db_value + 80) / 80
    
    # Convert the scaled value to a height between 0 and 100
    height = scaled_value * 50
    
    return height

def infer(title, audio_in, image_in):
    # Load the audio file
    audio_path = audio_in
    audio_data, sr = librosa.load(audio_path)

    # Get the duration in seconds
    duration = librosa.get_duration(y=audio_data, sr=sr)
    
    # Extract the audio data for the desired time
    start_time = 0 # start time in seconds
    end_time = duration # end time in seconds
    
    start_index = int(start_time * sr)
    end_index = int(end_time * sr)
    
    audio_data = audio_data[start_index:end_index]
    
    # Compute the short-time Fourier transform
    hop_length = 512

    
    stft = librosa.stft(audio_data, hop_length=hop_length)
    spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max)

    # Get the frequency values
    freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0])

    # Select the indices of the frequency values that correspond to the desired frequencies
    n_freqs = 114
    freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int)
    
    # Extract the dB values for the desired frequencies
    db_values = []
    for i in range(spectrogram.shape[1]):
        db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i])))
    
    # Print the dB values for the first time frame
    print(db_values[0])

    proportional_values = []

    for frame in db_values:
        proportional_frame = [db_to_height(db) for f, db in frame]
        proportional_values.append(proportional_frame)

    print(proportional_values[0])
    print("AUDIO CHUNK: " + str(len(proportional_values)))

    # Open the background image
    background_image = Image.open(image_in)
    
    # Resize the image while keeping its aspect ratio
    bg_width, bg_height = background_image.size
    aspect_ratio = bg_width / bg_height
    new_width = 512
    new_height = int(new_width / aspect_ratio)
    resized_bg = background_image.resize((new_width, new_height))

    # Apply black cache for better visibility of the white text
    bg_cache = Image.open('black_cache.png')
    resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache)

    # Create a new ImageDraw object
    draw = ImageDraw.Draw(resized_bg)
    
    # Define the text to be added
    text = title
    font = ImageFont.truetype("NotoSansSC-Regular.otf", 16)
    text_color = (255, 255, 255) # white color
    
    # Calculate the position of the text
    text_width, text_height = draw.textsize(text, font=font)
    x = 30
    y = new_height - 70
    
    # Draw the text on the image
    draw.text((x, y), text, fill=text_color, font=font)

    # Save the resized image
    resized_bg.save('resized_background.jpg')
    
    generated_frames = []
    for i, frame in enumerate(proportional_values): 
        bars_img = make_bars_image(frame, i, new_height)
        bars_img = Image.open(bars_img)
        # Paste the audio bars image on top of the background image
        fresh_bg = Image.open('resized_background.jpg')
        fresh_bg.paste(bars_img, (0, 0), mask=bars_img)
        # Save the image
        fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg')
        generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg')
    print(generated_frames)

    # Create a video clip from the images
    clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time))
    audio_clip = AudioFileClip(audio_in)
    clip = clip.set_audio(audio_clip)
    # Set the output codec
    codec = 'libx264'
    audio_codec = 'aac'
    # Save the video to a file
    clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec)

    retimed_clip = VideoFileClip("my_video.mp4")

    # Set the desired frame rate
    new_fps = 25
    
    # Create a new clip with the new frame rate
    new_clip = retimed_clip.set_fps(new_fps)
    
    # Save the new clip as a new video file
    new_clip.write_videofile("my_video_retimed.mp4", codec=codec, audio_codec=audio_codec)

    return "my_video_retimed.mp4"

# mix vocal and non-vocal
def mix(audio1, audio2):
  sound1 = AudioSegment.from_file(audio1)
  sound2 = AudioSegment.from_file(audio2)
  length = len(sound1)
  mixed = sound1[:length].overlay(sound2)

  mixed.export("song.wav", format="wav")

  return "song.wav"

# Bilibili
def youtube_downloader(
    video_identifier,
    start_time,
    end_time,
    is_full_song,
    output_filename="track.wav",
    num_attempts=5,
    url_base="",
    quiet=False,
    force=True,
):
    if is_full_song:
        ydl_opts = {
            'noplaylist': True,
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'dl_audio/youtube_audio',
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([video_identifier])
        audio_path = "dl_audio/youtube_audio.wav"
        return audio_path
    
    else:
        output_path = Path(output_filename)
        if output_path.exists():
            if not force:
                return output_path
            else:
                output_path.unlink()
    
        quiet = "--quiet --no-warnings" if quiet else ""
        command = f"""
            yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}"  # noqa: E501
        """.strip()
    
        attempts = 0
        while True:
            try:
                _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
            except subprocess.CalledProcessError:
                attempts += 1
                if attempts == num_attempts:
                    return None
            else:
                break
    
        if output_path.exists():
            return output_path
        else:
            return None

def audio_separated(audio_input, progress=gr.Progress()):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(0.1)

    # check file input
    if audio_input is None:
        # show progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.01)
            
        return (None, None, 'Please input audio.')

    # create filename
    filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S")
    
    # progress
    progress(progress=0.10, desc="Please wait...")
    
    # make dir output
    os.makedirs("output", exist_ok=True)
    
    # progress
    progress(progress=0.20, desc="Please wait...")
    
    # write
    if high_quality:
        write(filename+".wav", audio_input[0], audio_input[1])
    else:
        write(filename+".mp3", audio_input[0], audio_input[1])
        
    # progress
    progress(progress=0.50, desc="Please wait...")

    # demucs process
    if high_quality:
        command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output"
    else:
        command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output"
    
    os.system(command_demucs)
    
    # progress
    progress(progress=0.70, desc="Please wait...")
    
    # remove file audio
    if high_quality:
        command_delete = "rm -v ./"+filename+".wav"
    else:
        command_delete = "rm -v ./"+filename+".mp3"
    
    os.system(command_delete)
    
    # progress
    progress(progress=0.80, desc="Please wait...")
    
    # progress
    for i in progress.tqdm(range(80,100), desc="Please wait..."):
        time.sleep(0.1)

    if high_quality:
        return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..."
    else:
        return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..."

        
# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
def vc_func(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_audio is None:
        return (None, 'Please provide input audio.')

    if model_index is None:
        return (None, 'Please select a model.')

    model = loaded_models[model_index]

    # Reference: so-vits
    (audio_samp, audio_npy) = input_audio

    # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
    # Can be change well, we will see
    if (audio_npy.shape[0] / audio_samp) > 600 and in_hf_space:
        return (None, 'Input audio is longer than 600 secs.')

    # Bloody hell: https://stackoverflow.com/questions/26921836/
    if audio_npy.dtype != np.float32:  # :thonk:
        audio_npy = (
            audio_npy / np.iinfo(audio_npy.dtype).max
        ).astype(np.float32)

    if len(audio_npy.shape) > 1:
        audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))

    if audio_samp != 16000:
        audio_npy = librosa.resample(
            audio_npy,
            orig_sr=audio_samp,
            target_sr=16000
        )

    pitch_int = int(pitch_adjust)

    resample = (
        0 if resample_option == 'Disable resampling'
        else int(resample_option)
    )

    times = [0, 0, 0]

    checksum = hashlib.sha512()
    checksum.update(audio_npy.tobytes())

    output_audio = model['vc'].pipeline(
        hubert_model,
        model['net_g'],
        model['metadata'].get('speaker_id', 0),
        audio_npy,
        checksum.hexdigest(),
        times,
        pitch_int,
        f0_method,
        path.join('model', model['name'], model['metadata']['feat_index']),
        feat_ratio,
        model['if_f0'],
        filter_radius,
        model['target_sr'],
        resample,
        rms_mix_rate,
        'v2'
    )

    out_sr = (
        resample if resample >= 16000 and model['target_sr'] != resample
        else model['target_sr']
    )

    print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
    return ((out_sr, output_audio), 'Success')


async def edge_tts_vc_func(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_text is None:
        return (None, 'Please provide TTS text.')

    if tts_speaker is None:
        return (None, 'Please select TTS speaker.')

    if model_index is None:
        return (None, 'Please select a model.')

    speaker = tts_speakers_list[tts_speaker]['ShortName']
    (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
    return vc_func(
        (tts_sr, tts_np),
        model_index,
        pitch_adjust,
        f0_method,
        feat_ratio,
        filter_radius,
        rms_mix_rate,
        resample_option
    )


def update_model_info(model_index):
    if model_index is None:
        return str(
            '### Model info\n'
            'Please select a model from dropdown above.'
        )

    model = loaded_models[model_index]
    model_icon = model['metadata'].get('icon', '')

    return str(
        '### Model info\n'
        '![model icon]({icon})'
        '**{name}**\n\n'
        'Author: {author}\n\n'
        'Source: {source}\n\n'
        '{note}'
    ).format(
        name=model['metadata'].get('name'),
        author=model['metadata'].get('author', 'Anonymous'),
        source=model['metadata'].get('source', 'Unknown'),
        note=model['metadata'].get('note', ''),
        icon=(
            model_icon
            if model_icon.startswith(('http://', 'https://'))
            else '/file/model/%s/%s' % (model['name'], model_icon)
        )
    )


def _example_vc(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = vc_func(
        input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
        filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


async def _example_edge_tts(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = await edge_tts_vc_func(
        input_text, model_index, tts_speaker, pitch_adjust, f0_method,
        feat_ratio, filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


with app:
    gr.HTML("<center>"
            "<h1>🥳🎶🎡 - AI歌手数字人+RVC最新算法</h1>"
            "</center>")
    gr.Markdown("### <center>🌊 - 身临其境般的AI音乐体验，AI歌手“想把我唱给你听”；Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)</center>")
    gr.Markdown("### <center>更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>")

    with gr.Tab("🤗 - 轻松提取音乐"):
        with gr.Row():
            with gr.Column():
                ydl_url_input  = gr.Textbox(label="音乐视频网址(可直接填写相应的BV号)", value = "https://www.bilibili.com/video/BV...")
                with gr.Row():
                    start = gr.Number(value=0, label="起始时间 (秒)")
                    end = gr.Number(value=15, label="结束时间 (秒)")
                    check_full = gr.Checkbox(label="是否上传整首歌曲", info="若勾选则不需要填写起止时间", value=True)
                with gr.Accordion('搜索歌曲名上传', open=False):
                    search_name = gr.Dropdown(label="通过歌曲名搜索", info="选一首您喜欢的歌曲吧", choices=["周杰伦晴天","周杰伦兰亭序","周杰伦七里香","周杰伦花海","周杰伦反方向的钟","周杰伦一路向北","周杰伦稻香","周杰伦明明就","周杰伦爱在西元前","孙燕姿逆光","陈奕迅富士山下","许嵩有何不可","薛之谦其实","邓紫棋光年之外","李荣浩年少有为"])
                    vc_search = gr.Button("用歌曲名来搜索吧")

                ydl_url_submit = gr.Button("提取声音文件吧", variant="primary")
                as_audio_submit = gr.Button("去除背景音吧", variant="primary")
            with gr.Column():
                ydl_audio_output = gr.Audio(label="歌曲原声")
                as_audio_input  = ydl_audio_output
                as_audio_vocals    = gr.Audio(label="歌曲人声部分")
                as_audio_no_vocals = gr.Audio(label="歌曲伴奏部分", type="filepath")
                as_audio_message   = gr.Textbox(label="Message", visible=False)

    vc_search.click(auto_search, [search_name], [ydl_audio_output])
    ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end, check_full], outputs=[ydl_audio_output])
    as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True)
                    
    with gr.Row():
        with gr.Tab('🎶 - 歌声转换'):
            with gr.Row():
                with gr.Column():
                    input_audio = as_audio_vocals
                    vc_convert_btn = gr.Button('进行歌声转换吧！', variant='primary')
                    full_song = gr.Button("加入歌曲伴奏吧！", variant="primary")
                    new_song = gr.Audio(label="AI歌手+伴奏", type="filepath")
    
                    pitch_adjust = gr.Slider(
                        label='变调(默认为0；+2为升高两个key)',
                        minimum=-12,
                        maximum=12,
                        step=1,
                        value=0
                    )
                    f0_method = gr.Radio(
                        label='人声提取方法(pm时间更短；rmvpe效果更好)',
                        choices=['pm', 'rmvpe'],
                        value='pm',
                        interactive=True
                    )
    
                    with gr.Accordion('更多设置', open=False):
                        feat_ratio = gr.Slider(
                            label='Feature ratio',
                            minimum=0,
                            maximum=1,
                            step=0.1,
                            value=0.6,
                            visible=False
                        )
                        filter_radius = gr.Slider(
                            label='Filter radius',
                            minimum=0,
                            maximum=7,
                            step=1,
                            value=3,
                            visible=False
                        )
                        rms_mix_rate = gr.Slider(
                            label='Volume envelope mix rate',
                            minimum=0,
                            maximum=1,
                            step=0.1,
                            value=1,
                            visible=False                
                        )
                        resample_rate = gr.Dropdown(
                            [
                                'Disable resampling',
                                '16000',
                                '22050',
                                '44100',
                                '48000'
                            ],
                            label='是否更新采样率(默认为否)',
                            value='Disable resampling'
                        )
    
                with gr.Column():
                    # Model select
                    model_index = gr.Dropdown(
                        [
                            '%s - %s' % (
                                m['metadata'].get('source', 'Unknown'),
                                m['metadata'].get('name')
                            )
                            for m in loaded_models
                        ],
                        label='请选择您的AI歌手(必选)',
                        type='index'
                    )
        
                    # Model info
                    with gr.Box():
                        model_info = gr.Markdown(
                            '### AI歌手信息\n'
                            'Please select a model from dropdown above.',
                            elem_id='model_info'
                        )
        
                    output_audio = gr.Audio(label='AI歌手(无伴奏)', type="filepath")
                    output_msg = gr.Textbox(label='Output message', visible=False)

        vc_convert_btn.click(
            vc_func,
            [
                input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
                filter_radius, rms_mix_rate, resample_rate
            ],
            [output_audio, output_msg],
            api_name='audio_conversion'
        )
        
        full_song.click(fn=mix, inputs=[output_audio, as_audio_no_vocals], outputs=[new_song])

        model_index.change(
            update_model_info,
            inputs=[model_index],
            outputs=[model_info],
            show_progress=False,
            queue=False
        )   

        with gr.Tab("📺 - 音乐视频"):
            with gr.Row():
                with gr.Column():
                    inp1 = gr.Textbox(label="为视频配上精彩的文案吧(选填)")
                    inp2 = new_song
                    inp3 = gr.Image(source='upload', type='filepath', label="上传一张背景图片吧")
                    btn = gr.Button("生成您的专属音乐视频吧", variant="primary")
              
                with gr.Column():
                    out1 = gr.Video(label='您的专属音乐视频').style(width=512)
                    
        btn.click(fn=infer, inputs=[inp1, inp2, inp3], outputs=[out1])

        with gr.Tab("🤵‍♀️ - AI歌手数字人"):
            with gr.Row().style(equal_height=False):
                with gr.Column(variant='panel'):
                    with gr.Tabs(elem_id="sadtalker_source_image"):
                        with gr.TabItem('图片上传'):
                            with gr.Row():
                                source_image = gr.Image(label="请上传一张您喜欢角色的图片", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
        
                    with gr.Tabs(elem_id="sadtalker_driven_audio"):
                        with gr.TabItem('💕倾情演绎'):
                            with gr.Column(variant='panel'):
                                driven_audio = output_audio
                                
                            submit = gr.Button('想把我唱给你听', elem_id="sadtalker_generate", variant='primary')

                            gen_mv = gr.Button('为视频添加伴奏吧', variant='primary')

                            with gr.Row():
                            
                                gen_video = gr.Video(label="AI歌手数字人视频", format="mp4", interactive=False).style(width=256)
                                inp_mv_1 = gen_video
                                inp_mv_2 = as_audio_no_vocals
                                music_video = gr.Video(label="视频+伴奏", format="mp4").style(width=256)
      
                with gr.Column(variant='panel'): 
                    with gr.Tabs(elem_id="sadtalker_checkbox"):
                        with gr.TabItem('视频设置'):
                            with gr.Column(variant='panel'):
                                # width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
                                # height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
                                pose_style = gr.Slider(minimum=0, maximum=46, step=1, label="Pose style", value=0, visible=False) # 
                                size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?", visible=False) # 
                                preprocess_type = gr.Radio(['crop', 'extfull'], value='crop', label='是否聚焦角色面部', info="crop：视频会聚焦角色面部；extfull：视频会显示图片全貌")
                                is_still_mode = gr.Checkbox(label="静态模式 (开启静态模式，角色的面部动作会减少；默认开启)", value=True, visible=False)
                                batch_size = gr.Slider(label="Batch size (数值越大，生成速度越快；若显卡性能好，可增大数值)", step=1, maximum=32, value=4)
                                enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", visible=False)
                                    

            submit.click(
                        fn=sad_talker.test, 
                        inputs=[source_image,
                                driven_audio,
                                preprocess_type,
                                is_still_mode,
                                enhancer,
                                batch_size,                            
                                size_of_image,
                                pose_style
                                ], 
                        outputs=[gen_video]
                        )
        
            gen_mv.click(fn=combine_music, inputs=[inp_mv_1, inp_mv_2], outputs=[music_video])
        
 
    gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>")
    gr.Markdown("<center>🧸 - 如何使用此程序：填写视频网址和视频起止时间后，依次点击“提取声音文件吧”、“去除背景音吧”、“进行歌声转换吧！”、“加入歌曲伴奏吧！”四个按键即可。</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')

app.queue(
    concurrency_count=1,
    max_size=20,
    api_open=args.api
).launch(show_error=True)