import torch, os, traceback, sys, warnings, shutil, numpy as np
import gradio as gr
import librosa
import asyncio
import rarfile
import edge_tts
import yt_dlp
import ffmpeg
import gdown
import subprocess
import wave
import soundfile as sf
from scipy.io import wavfile
from datetime import datetime
from urllib.parse import urlparse
from mega import Mega
from flask import Flask, request, jsonify, send_file
import base64
import tempfile
import os
app = Flask(__name__)

now_dir = os.getcwd()
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from fairseq import checkpoint_utils
from vc_infer_pipeline import VC
from config import Config
config = Config()

tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]

hubert_model = None

f0method_mode = ["pm", "harvest", "crepe"]
f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"

if os.path.isfile("rmvpe.pt"):
    f0method_mode.insert(2, "rmvpe")
    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"

def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(config.device)
    if config.is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()

load_hubert()

weight_root = "weights"
index_root = "weights/index"
weights_model = []
weights_index = []
for _, _, model_files in os.walk(weight_root):
    for file in model_files:
        if file.endswith(".pth"):
            weights_model.append(file)
for _, _, index_files in os.walk(index_root):
    for file in index_files:
        if file.endswith('.index') and "trained" not in file:
            weights_index.append(os.path.join(index_root, file))

def check_models():
    weights_model = []
    weights_index = []
    for _, _, model_files in os.walk(weight_root):
        for file in model_files:
            if file.endswith(".pth"):
                weights_model.append(file)
    for _, _, index_files in os.walk(index_root):
        for file in index_files:
            if file.endswith('.index') and "trained" not in file:
                weights_index.append(os.path.join(index_root, file))
    return (
        gr.Dropdown.update(choices=sorted(weights_model), value=weights_model[0]),
        gr.Dropdown.update(choices=sorted(weights_index))
    )

def clean():
    return (
        gr.Dropdown.update(value=""),
        gr.Slider.update(visible=False)
    )



@app.route('/convert_voice', methods=['POST'])
def api_convert_voice():
    data = request.json
    
    spk_id = data['spk_id']
    #input_audio_path = data['input_audio_path']
    voice_transform = data['voice_transform']
    
    
    audio_data = data['audio']  # Base64-encoded audio data

    # Decode the base64 audio
    audio_bytes = base64.b64decode(audio_data)
    
    # Create a temporary file to save the decoded audio
    temp_audio_path = os.path.join(tmp, f"{spk_id}_input_audio.wav")
    
    with open(temp_audio_path, 'wb') as temp_audio_file:
        temp_audio_file.write(audio_bytes)
    print(spk_id)
    
    
    output_path = convert_voice(spk_id, temp_audio_file, voice_transform)
    print(output_path)
    if os.path.exists(output_path):
        return send_file(output_path, as_attachment=True)
    else:
        return jsonify({"error": "File not found."}), 404
    


def convert_voice(spk_id, input_audio_path, voice_transform):
    get_vc(spk_id,0.5)
    output_audio_path = vc_single(
        sid=0,
        input_audio_path=input_audio_path,
        f0_up_key=voice_transform,  # Assuming voice_transform corresponds to f0_up_key
        f0_file=None ,
        f0_method="rmvpe",
        file_index=spk_id,  # Assuming file_index_path corresponds to file_index
        index_rate=0.75,
        filter_radius=3,
        resample_sr=0,
        rms_mix_rate=0.25,
        protect=0.33  # Adjusted from protect_rate to protect to match the function signature
    )
    print(output_audio_path)
    return output_audio_path


def vc_single(
    sid,
    input_audio_path,    
    f0_up_key,
    f0_file,
    f0_method,
    file_index,
    index_rate,
    filter_radius,
    resample_sr,
    rms_mix_rate,
    protect
):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
    global tgt_sr, net_g, vc, hubert_model, version, cpt
    
    try:
        logs = []
        print(f"Converting...")
        
        audio, sr = librosa.load(input_audio_path, sr=16000, mono=True)
        print(f"found audio ")
        f0_up_key = int(f0_up_key)
        times = [0, 0, 0]
        if hubert_model == None:
            load_hubert()
        print("loaded hubert")
        if_f0 = 1
        audio_opt = vc.pipeline(
            hubert_model,
            net_g,
            0,
            audio,
            input_audio_path,
            times,
            f0_up_key,
            f0_method,
            file_index,
            # file_big_npy,
            index_rate,
            if_f0,
            filter_radius,
            tgt_sr,
            resample_sr,
            rms_mix_rate,
            version,
            protect,
            f0_file=f0_file
        )
        if resample_sr >= 16000 and tgt_sr != resample_sr:
            tgt_sr = resample_sr
        index_info = (
            "Using index:%s." % file_index
            if os.path.exists(file_index)
            else "Index not used."
        )
        print("writing to FS")
        output_file_path = os.path.join("output", f"converted_audio_{sid}.wav")  # Adjust path as needed
        
        os.makedirs(os.path.dirname(output_file_path), exist_ok=True)  # Create the output directory if it doesn't exist
        print("create dir")
        # Save the audio file using the target sampling rate
        sf.write(output_file_path, audio_opt, tgt_sr)
        
        print("wrote to FS")

        # Return the path to the saved file along with any other information
        
        return output_file_path
           
            
    except:
        info = traceback.format_exc()
        
        return info, (None, None)

def get_vc(sid, to_return_protect0):
    global n_spk, tgt_sr, net_g, vc, cpt, version, weights_index
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
            print("clean_empty_cache")
            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            ###楼下不这么折腾清理不干净
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del net_g, cpt
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            cpt = None
        return (
            gr.Slider.update(maximum=2333, visible=False),
            gr.Slider.update(visible=True),
            gr.Dropdown.update(choices=sorted(weights_index), value=""),
            gr.Markdown.update(value="# <center> No model selected")
        )
    print(f"Loading {sid} model...")
    selected_model = sid[:-4]
    cpt = torch.load(os.path.join(weight_root, sid), map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
    if_f0 = cpt.get("f0", 1)
    if if_f0 == 0:
        to_return_protect0 = {
            "visible": False,
            "value": 0.5,
            "__type__": "update",
        }
    else:
        to_return_protect0 = {
            "visible": True,
            "value": to_return_protect0,
            "__type__": "update",
        }
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt["weight"], strict=False))
    net_g.eval().to(config.device)
    if config.is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    n_spk = cpt["config"][-3]
    weights_index = []
    for _, _, index_files in os.walk(index_root):
        for file in index_files:
            if file.endswith('.index') and "trained" not in file:
                weights_index.append(os.path.join(index_root, file))
    if weights_index == []:
        selected_index = gr.Dropdown.update(value="")
    else:
        selected_index = gr.Dropdown.update(value=weights_index[0])
    for index, model_index in enumerate(weights_index):
        if selected_model in model_index:
            selected_index = gr.Dropdown.update(value=weights_index[index])
            break
    return (
        gr.Slider.update(maximum=n_spk, visible=True),
        to_return_protect0,
        selected_index,
        gr.Markdown.update(
            f'## <center> {selected_model}\n'+
            f'### <center> RVC {version} Model'
        )
    )

def find_audio_files(folder_path, extensions):
    audio_files = []
    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if any(file.endswith(ext) for ext in extensions):
                audio_files.append(file)
    return audio_files

def vc_multi(
    spk_item,
    vc_input,
    vc_output,
    vc_transform0,
    f0method0,
    file_index,
    index_rate,
    filter_radius,
    resample_sr,
    rms_mix_rate,
    protect,
):
    global tgt_sr, net_g, vc, hubert_model, version, cpt
    logs = []
    logs.append("Converting...")
    yield "\n".join(logs)
    print()
    try:
        if os.path.exists(vc_input):
            folder_path = vc_input
            extensions = [".mp3", ".wav", ".flac", ".ogg"]
            audio_files = find_audio_files(folder_path, extensions)
            for index, file in enumerate(audio_files, start=1):
                audio, sr = librosa.load(os.path.join(folder_path, file), sr=16000, mono=True)
                input_audio_path = folder_path, file
                f0_up_key = int(vc_transform0)
                times = [0, 0, 0]
                if hubert_model == None:
                    load_hubert()
                if_f0 = cpt.get("f0", 1)
                audio_opt = vc.pipeline(
                    hubert_model,
                    net_g,
                    spk_item,
                    audio,
                    input_audio_path,
                    times,
                    f0_up_key,
                    f0method0,
                    file_index,
                    index_rate,
                    if_f0,
                    filter_radius,
                    tgt_sr,
                    resample_sr,
                    rms_mix_rate,
                    version,
                    protect,
                    f0_file=None
                )
                if resample_sr >= 16000 and tgt_sr != resample_sr:
                    tgt_sr = resample_sr
                output_path = f"{os.path.join(vc_output, file)}"
                os.makedirs(os.path.join(vc_output), exist_ok=True)
                sf.write(
                    output_path,
                    audio_opt,
                    tgt_sr,
                )
                info = f"{index} / {len(audio_files)} | {file}"
                print(info)
                logs.append(info)
                yield "\n".join(logs)
        else:
            logs.append("Folder not found or path doesn't exist.")
            yield "\n".join(logs)
    except:
        info = traceback.format_exc()
        print(info)
        logs.append(info)
        yield "\n".join(logs)

def download_audio(url, audio_provider):
    logs = []
    os.makedirs("dl_audio", exist_ok=True)
    if url == "":
        logs.append("URL required!")
        yield None, "\n".join(logs)
        return None, "\n".join(logs)
    if audio_provider == "Youtube":
        logs.append("Downloading the audio...")
        yield None, "\n".join(logs)
        ydl_opts = {
            'noplaylist': True,
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'result/dl_audio/audio',
        }
        audio_path = "result/dl_audio/audio.wav"
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([url])
        logs.append("Download Complete.")
        yield audio_path, "\n".join(logs)

def cut_vocal_and_inst_yt(split_model):
    logs = []
    logs.append("Starting the audio splitting process...")
    yield "\n".join(logs), None, None, None
    command = f"demucs --two-stems=vocals -n {split_model} result/dl_audio/audio.wav -o output"
    result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
    for line in result.stdout:
        logs.append(line)
        yield "\n".join(logs), None, None, None
    print(result.stdout)
    vocal = f"output/{split_model}/audio/vocals.wav"
    inst = f"output/{split_model}/audio/no_vocals.wav"
    logs.append("Audio splitting complete.")
    yield "\n".join(logs), vocal, inst, vocal

def cut_vocal_and_inst(split_model, audio_data):
    logs = []
    vocal_path = "output/result/audio.wav"
    os.makedirs("output/result", exist_ok=True)
    wavfile.write(vocal_path, audio_data[0], audio_data[1])
    logs.append("Starting the audio splitting process...")
    yield "\n".join(logs), None, None
    command = f"demucs --two-stems=vocals -n {split_model} {vocal_path} -o output"
    result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
    for line in result.stdout:
        logs.append(line)
        yield "\n".join(logs), None, None
    print(result.stdout)
    vocal = f"output/{split_model}/audio/vocals.wav"
    inst = f"output/{split_model}/audio/no_vocals.wav"
    logs.append("Audio splitting complete.")
    yield "\n".join(logs), vocal, inst
    
def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model):
    os.makedirs("output/result", exist_ok=True)
    vocal_path = "output/result/output.wav"
    output_path = "output/result/combine.mp3"
    inst_path = f"output/{split_model}/audio/no_vocals.wav"
    wavfile.write(vocal_path, audio_data[0], audio_data[1])
    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
    print(result.stdout.decode())
    return output_path

def download_and_extract_models(urls):
    logs = []
    os.makedirs("zips", exist_ok=True)
    os.makedirs(os.path.join("zips", "extract"), exist_ok=True)
    os.makedirs(os.path.join(weight_root), exist_ok=True)
    os.makedirs(os.path.join(index_root), exist_ok=True)
    for link in urls.splitlines():
        url = link.strip()
        if not url:
            raise gr.Error("URL Required!")
            return "No URLs provided."
        model_zip = urlparse(url).path.split('/')[-2] + '.zip'
        model_zip_path = os.path.join('zips', model_zip)
        logs.append(f"Downloading...")
        yield "\n".join(logs)
        if "drive.google.com" in url:
            gdown.download(url, os.path.join("zips", "extract"), quiet=False)
        elif "mega.nz" in url:
            m = Mega()
            m.download_url(url, 'zips')
        else:
            os.system(f"wget {url} -O {model_zip_path}")
        logs.append(f"Extracting...")
        yield "\n".join(logs)
        for filename in os.listdir("zips"):
            archived_file = os.path.join("zips", filename)
            if filename.endswith(".zip"):
                shutil.unpack_archive(archived_file, os.path.join("zips", "extract"), 'zip')
            elif filename.endswith(".rar"):
                with rarfile.RarFile(archived_file, 'r') as rar:
                    rar.extractall(os.path.join("zips", "extract"))
        for _, dirs, files in os.walk(os.path.join("zips", "extract")):
            logs.append(f"Searching Model and Index...")
            yield "\n".join(logs)
            model = False
            index = False
            if files:
                for file in files:
                    if file.endswith(".pth"):
                        basename = file[:-4]
                        shutil.move(os.path.join("zips", "extract", file), os.path.join(weight_root, file))
                        model = True
                    if file.endswith('.index') and "trained" not in file:
                        shutil.move(os.path.join("zips", "extract", file), os.path.join(index_root, file))
                        index = True
            else:
                logs.append("No model in main folder.")
                yield "\n".join(logs)
                logs.append("Searching in subfolders...")
                yield "\n".join(logs)
                for sub_dir in dirs:
                    for _, _, sub_files in os.walk(os.path.join("zips", "extract", sub_dir)):
                        for file in sub_files:
                            if file.endswith(".pth"):
                                basename = file[:-4]
                                shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(weight_root, file))
                                model = True
                            if file.endswith('.index') and "trained" not in file:
                                shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(index_root, file))
                                index = True  
                        shutil.rmtree(os.path.join("zips", "extract", sub_dir))
            if index is False:
                logs.append("Model only file, no Index file detected.")
                yield "\n".join(logs)
        logs.append("Download Completed!")
        yield "\n".join(logs)
    logs.append("Successfully download all models! Refresh your model list to load the model")
    yield "\n".join(logs)
if __name__ == '__main__':
    app.run(debug=False, port=5000,host='0.0.0.0')