import json
import os
import shutil
import urllib.request
import zipfile
import gdown
from argparse import ArgumentParser

import gradio as gr
import spaces
import random

from main import song_cover_pipeline

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

mdxnet_models_dir = 'mdxnet_models'
rvc_models_dir = 'rvc_models'
output_dir = 'song_output'

def download_and_extract_model(model_url, model_name, progress=gr.Progress()):
    try:
        os.makedirs(rvc_models_dir, exist_ok=True)

        extraction_folder = os.path.join(rvc_models_dir, model_name)
        zip_path = os.path.join(rvc_models_dir, f'{model_name}.zip')

        if os.path.exists(extraction_folder):
            model_name = model_name + str(random.randint(0, 1000))
            extraction_folder = os.path.join(rvc_models_dir, model_name)
            zip_path = os.path.join(rvc_models_dir, f'{model_name}.zip')
        
        progress(0, desc=f'[~] Downloading voice model with name {model_name}...')
        
        try:
            if 'huggingface.co' in model_url:
                urllib.request.urlretrieve(model_url, zip_path)
            elif 'pixeldrain.com' in model_url:
                pixeldrain_id = model_url.split('/')[-1]
                pixeldrain_url = f'https://pixeldrain.com/api/file/{pixeldrain_id}'
                urllib.request.urlretrieve(pixeldrain_url, zip_path)
            elif 'drive.google.com' in model_url:
                file_id = model_url.split('/')[-2]
                gdown.download(id=file_id, output=zip_path, quiet=False)
            else:
                urllib.request.urlretrieve(model_url, zip_path)
        except Exception as download_error:
            raise gr.Error(f"Failed to download the model: {str(download_error)}")

        if not os.path.exists(zip_path):
            raise gr.Error(f"Failed to download the model. The zip file was not created.")

        progress(0.5, desc="Extracting model...")
        extract_zip(extraction_folder, zip_path)
        
        pth_files = [f for f in os.listdir(extraction_folder) if f.endswith('.pth')]
        if not pth_files:
            raise ValueError("No .pth file found in the downloaded model.")
        
        progress(1, desc="Model ready")
        return model_name

    except Exception as e:
        if os.path.exists(extraction_folder):
            shutil.rmtree(extraction_folder)
        if os.path.exists(zip_path):
            os.remove(zip_path)
        raise gr.Error(f"Error downloading or extracting model: {str(e)}")

def cleanup_temp_model(model_name):
    temp_dir = os.path.join(rvc_models_dir, model_name)
    try:
        shutil.rmtree(temp_dir)
        print(f"[+] Temporary model files cleaned up: {temp_dir}")
    except Exception as e:
        print(f"Error cleaning up temporary model files: {str(e)}")

def extract_zip(extraction_folder, zip_name):
    os.makedirs(extraction_folder)
    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
        zip_ref.extractall(extraction_folder)
    os.remove(zip_name)

    index_filepath, model_filepath = None, None
    for root, dirs, files in os.walk(extraction_folder):
        for name in files:
            if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100:
                index_filepath = os.path.join(root, name)

            if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40:
                model_filepath = os.path.join(root, name)

    if not model_filepath:
        raise gr.Error(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.')

    # move model and index file to extraction folder
    os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath)))
    if index_filepath:
        os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath)))

    # remove any unnecessary nested folders
    for filepath in os.listdir(extraction_folder):
        if os.path.isdir(os.path.join(extraction_folder, filepath)):
            shutil.rmtree(os.path.join(extraction_folder, filepath))


def download_online_model(url, dir_name, progress=gr.Progress()):
    try:
        progress(0, desc=f'[~] Downloading voice model with name {dir_name}...')
        zip_name = url.split('/')[-1]
        extraction_folder = os.path.join(rvc_models_dir, dir_name)
        if os.path.exists(extraction_folder):
            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')

        if 'huggingface.co' in url:
            urllib.request.urlretrieve(url, zip_name)

        if 'pixeldrain.com' in url:
            zip_name = dir_name + '.zip'
            url = f'https://pixeldrain.com/api/file/{zip_name}'
            urllib.request.urlretrieve(url, zip_name)

        elif 'drive.google.com' in url:
            # Extract the Google Drive file ID
            zip_name = dir_name + '.zip'
            file_id = url.split('/')[-2]
            output = os.path.join('.', f'{dir_name}.zip')  # Adjust the output path if needed
            gdown.download(id=file_id, output=output, quiet=False)

        progress(0.5, desc='[~] Extracting zip...')
        extract_zip(extraction_folder, zip_name)
        return f'[+] {dir_name} Model successfully downloaded!'

    except Exception as e:
        raise gr.Error(str(e))


def upload_local_model(zip_path, dir_name, progress=gr.Progress()):
    try:
        extraction_folder = os.path.join(rvc_models_dir, dir_name)
        if os.path.exists(extraction_folder):
            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')

        zip_name = zip_path.name
        progress(0.5, desc='[~] Extracting zip...')
        extract_zip(extraction_folder, zip_name)
        return f'[+] {dir_name} Model successfully uploaded!'

    except Exception as e:
        raise gr.Error(str(e))

def pub_dl_autofill(pub_models, event: gr.SelectData):
    return gr.Text.update(value=pub_models.loc[event.index[0], 'URL']), gr.Text.update(value=pub_models.loc[event.index[0], 'Model Name'])


def swap_visibility():
    return gr.update(visible=True), gr.update(visible=False), gr.update(value=''), gr.update(value=None)


def process_file_upload(file):
    return file.name, gr.update(value=file.name)


def show_hop_slider(pitch_detection_algo):
    if pitch_detection_algo == 'mangio-crepe':
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def song_cover_pipeline_with_model_download(song_input, model_url, model_name, pitch, keep_files, is_webui, main_gain, backup_gain,
                                            inst_gain, index_rate, filter_radius, rms_mix_rate, f0_method, crepe_hop_length,
                                            protect, pitch_all, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping,
                                            output_format, progress=gr.Progress()):
    model_path = None
    try:
        model_path = download_and_extract_model(model_url, model_name, progress)
        print(f"Model path: {model_path}")
        vocal_only_path, vocal_backing_path = song_cover_pipeline(song_input, model_path, pitch, keep_files, is_webui, main_gain, backup_gain,
                                     inst_gain, index_rate, filter_radius, rms_mix_rate, f0_method, crepe_hop_length,
                                     protect, pitch_all, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping,
                                     output_format, progress)
        
        # Clean up old folders in song_output
        output_folders = [f for f in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, f))]
        output_folders.sort(key=lambda x: os.path.getmtime(os.path.join(output_dir, x)))
        
        while len(output_folders) > 100:
            oldest_folder = output_folders.pop(0)
            shutil.rmtree(os.path.join(output_dir, oldest_folder))
        
        return vocal_only_path, vocal_backing_path
    except gr.Error as e:
        print(f"Error: {str(e)}")
        gr.Warning(f"An error occurred 💥: {str(e)}", duration=15)
        raise gr.Error(f"An error occurred: {str(e)}")
    finally:
        if model_path:
            cleanup_temp_model(model_path)


if __name__ == '__main__':
    parser = ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True)
    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
    parser.add_argument("--listen", action="store_true", default=False, help="Make the WebUI reachable from your local network.")
    parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
    parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
    args = parser.parse_args()

    with gr.Blocks(title='AICoverGenWebUI', theme='NoCrypt/miku@1.2.2') as app:

        gr.Label('AICoverGen WebUI created with ❤️', show_label=False)

        # main tab
        with gr.Tab("Generate"):

            with gr.Accordion('Main Options'):
                with gr.Row():
                    with gr.Column():
                        model_url = gr.Text(label='Voice Model URL', info='Enter the URL of the voice model zip file. Get voice models url from https://voice-models.com. Accepted url should be in format below', value='https://huggingface.co/megaaziib/my-rvc-models-collection/resolve/main/kobo.zip')
                        model_name = gr.Text(label='Voice Model Name', info='Enter the name of the voice model', value='kobo')
                        # rvc_model = gr.Dropdown(voice_models, label='Voice Models', info='Models folder "AICoverGen --> rvc_models". After new models are added into this folder, click the refresh button')

                    with gr.Column(visible=False) as yt_link_col:
                        song_input = gr.Text(label='Song input', info='Link to a song on YouTube or full path to a local file. For file upload, click the button below. Youtube Banned Huggingface IP, use File Upload instead', value='https://youtu.be/FRh7LvlQTuA')
                        show_file_upload_button = gr.Button('Upload file instead')

                    with gr.Column(visible=True) as file_upload_col:
                        local_file = gr.File(label='Upload a local music file')
                        song_input_file = gr.UploadButton('Upload 📂', file_types=['audio'], variant='primary')
                        show_yt_link_button = gr.Button('Paste YouTube link/Path to local file instead')
                        song_input_file.upload(process_file_upload, inputs=[song_input_file], outputs=[local_file, song_input])

                    with gr.Column():
                        pitch = gr.Slider(-24, 24, value=0, step=1, label='Pitch Change (Vocals ONLY)', info='Generally, use 12 for male to female conversions and -12 for vice-versa. (12 Points = 1 Octaves)')
                        pitch_all = gr.Slider(-12, 12, value=0, step=1, label='Overall Pitch Change', info='Changes pitch/key of vocals and instrumentals together. Altering this slightly reduces sound quality. (Semitones)')
                    show_file_upload_button.click(swap_visibility, outputs=[file_upload_col, yt_link_col, song_input, local_file])
                    show_yt_link_button.click(swap_visibility, outputs=[yt_link_col, file_upload_col, song_input, local_file])

            with gr.Accordion('Voice conversion options', open=False):
                with gr.Row():
                    index_rate = gr.Slider(0, 1, value=0.5, label='Index Rate', info="Controls how much of the AI voice's accent to keep in the vocals")
                    filter_radius = gr.Slider(0, 7, value=3, step=1, label='Filter radius', info='If >=3: apply median filtering median filtering to the harvested pitch results. Can reduce breathiness')
                    rms_mix_rate = gr.Slider(0, 1, value=0.25, label='RMS mix rate', info="Control how much to mimic the original vocal's loudness (0) or a fixed loudness (1)")
                    protect = gr.Slider(0, 0.5, value=0.33, label='Protect rate', info='Protect voiceless consonants and breath sounds. Set to 0.5 to disable.')
                    with gr.Column():
                        f0_method = gr.Dropdown(['rmvpe', 'mangio-crepe'], value='rmvpe', label='Pitch detection algorithm', info='Best option is rmvpe (clarity in vocals), then mangio-crepe (smoother vocals)')
                        crepe_hop_length = gr.Slider(32, 320, value=128, step=1, visible=False, label='Crepe hop length', info='Lower values leads to longer conversions and higher risk of voice cracks, but better pitch accuracy.')
                        f0_method.change(show_hop_slider, inputs=f0_method, outputs=crepe_hop_length)
                keep_files = gr.Checkbox(label='Keep intermediate files', info='Keep all audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals. Leave unchecked to save space')

            with gr.Accordion('Audio mixing options', open=False):
                gr.Markdown('### Volume Change (decibels)')
                with gr.Row():
                    main_gain = gr.Slider(-20, 20, value=0, step=1, label='Main Vocals')
                    backup_gain = gr.Slider(-20, 20, value=0, step=1, label='Backup Vocals')
                    inst_gain = gr.Slider(-20, 20, value=0, step=1, label='Music')

                gr.Markdown('### Reverb Control on AI Vocals')
                with gr.Row():
                    reverb_rm_size = gr.Slider(0, 1, value=0.15, label='Room size', info='The larger the room, the longer the reverb time')
                    reverb_wet = gr.Slider(0, 1, value=0.2, label='Wetness level', info='Level of AI vocals with reverb')
                    reverb_dry = gr.Slider(0, 1, value=0.8, label='Dryness level', info='Level of AI vocals without reverb')
                    reverb_damping = gr.Slider(0, 1, value=0.7, label='Damping level', info='Absorption of high frequencies in the reverb')

                gr.Markdown('### Audio Output Format')
                output_format = gr.Dropdown(['mp3', 'wav'], value='mp3', label='Output file type', info='mp3: small file size, decent quality. wav: Large file size, best quality')

            with gr.Row():
                clear_btn = gr.ClearButton(value='Clear', components=[song_input, model_url, keep_files, local_file])
                generate_btn = gr.Button("Generate", variant='primary')
            with gr.Row():
                ai_cover = gr.Audio(label='AI Cover (Vocal Only Inference)', show_share_button=False)
                ai_backing = gr.Audio(label='AI Cover (Vocal Backing Inference)', show_share_button=False)

            is_webui = gr.Number(value=1, visible=False)
            generate_btn.click(song_cover_pipeline_with_model_download,
                inputs=[song_input, model_url, model_name, pitch, keep_files, is_webui, main_gain, backup_gain,

                        inst_gain, index_rate, filter_radius, rms_mix_rate, f0_method, crepe_hop_length,
                        protect, pitch_all, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping,
                        output_format],
                outputs=[ai_cover, ai_backing])
            clear_btn.click(lambda: [0, 0, 0, 0, 0.5, 3, 0.25, 0.33, 'rmvpe', 128, 0, 0.15, 0.2, 0.8, 0.7, 'mp3', None],
                outputs=[pitch, main_gain, backup_gain, inst_gain, index_rate, filter_radius, rms_mix_rate,
                            protect, f0_method, crepe_hop_length, pitch_all, reverb_rm_size, reverb_wet,
                            reverb_dry, reverb_damping, output_format, ai_cover])

    app.launch(
        share=args.share_enabled,
        server_name=None if not args.listen else (args.listen_host or '0.0.0.0'),
        server_port=args.listen_port,
    )