myinfer_latest.py

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,session,render_template
import base64
import tempfile
import threading
import hashlib
import os
import werkzeug
from pydub import AudioSegment
import uuid
from threading import Semaphore
from threading import Lock
from multiprocessing import Process, SimpleQueue, set_start_method,get_context
from queue import Empty
from pydub import AudioSegment
from flask_dance.contrib.google import make_google_blueprint, google
import io
from space import ensure_model_in_weights_dir,upload_to_do
import boto3
import os
import ffmpeg
import os
import random




app = Flask(__name__)
app.secret_key = 'smjain_6789' 
now_dir = os.getcwd()
cpt={}
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
split_model="htdemucs"
convert_voice_lock = Lock()
#concurrent= os.getenv('concurrent', '')
# Define the maximum number of concurrent requests
MAX_CONCURRENT_REQUESTS=10


# Initialize the semaphore with the maximum number of concurrent requests
request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS)

task_status_tracker = {}
os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"  # ONLY FOR TESTING, REMOVE IN PRODUCTION
os.environ["OAUTHLIB_RELAX_TOKEN_SCOPE"] = "1"
app.config["GOOGLE_OAUTH_CLIENT_ID"] = "144930881143-n3e3ubers3vkq7jc9doe4iirasgimdt2.apps.googleusercontent.com"
app.config["GOOGLE_OAUTH_CLIENT_SECRET"] = "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"
google_blueprint = make_google_blueprint(scope=["profile", "email"])
app.register_blueprint(google_blueprint, url_prefix="/login")
ACCESS_ID = os.getenv('ACCESS_ID', '')
SECRET_KEY = os.getenv('SECRET_KEY', '')


#set_start_method('spawn', force=True)
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)"

@app.route("/")
def index():
    # Check if user is logged in
    return render_template("ui.html")
    #if google.authorized:
    #    return render_template("index.html", logged_in=True)
    #else:
    #   return render_template("index.html", logged_in=False)




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)
    )
# Function to delete files
def cleanup_files(file_paths):
    for path in file_paths:
        try:
            os.remove(path)
            print(f"Deleted {path}")
        except Exception as e:
            print(f"Error deleting {path}: {e}")

@app.route("/create_song")
def create_song():
    if not google.authorized:
        return redirect(url_for("google.login"))
    resp = google.get("/oauth2/v2/userinfo")
    assert resp.ok, resp.text
    email = resp.json()["email"]
    user_info = resp.json()
    user_id = user_info.get("id")
    name = user_info.get("name")
    
    #if not user_exists(email):
    #    user_data = {'user_id': user_id, 'user_name': name, 'email': email, 'model_created': 'No', 'time_used': '0','model_id':''}
    #    add_user(user_data)

    #models = get_user_models(email)
    
    # Assuming we're interested in whether any model has been created
    #model_exists = len(models) > 0
    return render_template("ui.html", email=email)    
    
@app.route('/download/<filename>', methods=['GET'])
def download_file(filename):
    # Configure the client with your credentials
    session = boto3.session.Session()
    client = session.client('s3',
                            region_name='nyc3',
                            endpoint_url='https://nyc3.digitaloceanspaces.com',
                            aws_access_key_id=ACCESS_ID,
                            aws_secret_access_key=SECRET_KEY)
    
    # Define the bucket and object key
    bucket_name = 'sing'  # Your bucket name
    object_key = f'{filename}'  # Construct the object key

    # Define the local path to save the file
    local_file_path = os.path.join('weights', filename)

    # Download the file from the bucket
    try:
        client.download_file(bucket_name, object_key, local_file_path)
    except client.exceptions.NoSuchKey:
        return jsonify({'error': 'File not found in the bucket'}), 404
    except Exception as e:
        return jsonify({'error': str(e)}), 500

    # Optional: Send the file directly to the client
    # return send_file(local_file_path, as_attachment=True)

    return jsonify({'success': True, 'message': 'File downloaded successfully', 'file_path': local_file_path})
    
@app.route('/list-weights', methods=['GET'])
def list_weights():
    directory = 'weights'
    files = os.listdir(directory)
    email = request.args.get('email', default='')
    if not email:
        return jsonify({"error": "Email parameter is required"}), 400
    list_models(email)
    # Extract filenames without their extensions
    filenames = [os.path.splitext(file)[0] for file in files if os.path.isfile(os.path.join(directory, file))]
    return jsonify(filenames)

@app.route("/logout")
def logout():
    # Clear the session
    session.clear()
    #if "google_oauth_token" in session:
    #    del session["google_oauth_token"]
    return redirect(url_for("index"))


@app.route('/status/<audio_id>', methods=['GET'])
def get_status(audio_id):
    # Retrieve the task status using the unique ID
    print(audio_id)
    status_info = task_status_tracker.get(audio_id, {"status": "Unknown ID", "percentage": 0})
    return jsonify({"audio_id": audio_id, "status": status_info["status"], "percentage": status_info["percentage"]})


def merge_audio_image(mp3_path, image_path, output_dir, unique_id):
    # Generate output file path
    output_path = os.path.join(output_dir, f"{unique_id}.mp4")
    
    # Ensure the image file exists
    if not os.path.isfile(image_path):
        raise FileNotFoundError(f"Image file not found: {image_path}")
    
    # Ensure the audio file exists
    if not os.path.isfile(mp3_path):
        raise FileNotFoundError(f"Audio file not found: {mp3_path}")
    
    # Get the duration of the audio file
    probe = ffmpeg.probe(mp3_path)
    audio_duration = float(probe['format']['duration'])
    
    # Create the ffmpeg command to combine image and audio into a video
    input_image = ffmpeg.input(image_path, loop=1, t=audio_duration)
    input_audio = ffmpeg.input(mp3_path)
    
    # Apply scale and pad filters to the image
    video_stream = input_image.filter('scale', size='1080x1080', force_original_aspect_ratio='decrease')\
                              .filter('pad', 1080, 1080, -1, -1)
    
    # Combine image and audio into a video
    ffmpeg.output(video_stream, input_audio, output_path, vcodec='libx264', acodec='aac', strict='experimental', pix_fmt='yuv420p').run()
    
    return output_path





processed_audio_storage = {}
@app.route('/convert_voice', methods=['POST'])
def api_convert_voice():
    acquired = request_semaphore.acquire(blocking=False)
    
    if not acquired:
        return jsonify({"error": "Too many requests, please try again later"}), 429
    #task_status_tracker[unique_id] = {"status": "Starting", "percentage": 0}
    try:
    
        #if session.get('submitted'):
        #    return jsonify({"error": "Form already submitted"}), 400

        # Process the form here...
        # Set the flag indicating the form has been submitted
        #session['submitted'] = True
        print(request.form)
        print(request.files)
        print("accessing spk_id")
        spk_id = request.form['spk_id']+'.pth'
        print("speaker id path=",spk_id)
        voice_transform = request.form['voice_transform']
        print("before file access")
        # The file part
        if 'file' not in request.files:
            return jsonify({"error": "No file part"}), 400
        file = request.files['file']
        if file.filename == '':
            return jsonify({"error": "No selected file"}), 400
        
        if file.content_length > 10 * 1024 * 1024:
            return jsonify({"error": "File size exceeds 6 MB"}), 400
        
        print("after file access")
        print("check if model is there in weights dir or not")
        filename_without_extension = os.path.splitext(file.filename)[0]
        unique_id = filename_without_extension
        ensure_model_in_weights_dir(spk_id)
        print("checking done for the model")
        content_type_format_map = {
            'audio/mpeg': 'mp3',
            'audio/wav': 'wav',
            'audio/x-wav': 'wav',
            'audio/mp4': 'mp4',
            'audio/x-m4a': 'mp4',
        }

        # Default to 'mp3' if content type is unknown (or adjust as needed)
        audio_format = content_type_format_map.get(file.content_type, 'mp3')

        # Convert the uploaded file to an audio segment
        audio = AudioSegment.from_file(io.BytesIO(file.read()), format=audio_format)
        
        #audio = AudioSegment.from_file(io.BytesIO(file.read()), format="mp3")  # Adjust format as necessary
        file.seek(0)  # Reset file pointer after reading
    
    # Calculate audio length in minutes
        audio_length_minutes = len(audio) / 60000.0  # pydub returns length in milliseconds
    
        if audio_length_minutes > 5:
            return jsonify({"error": "Audio length exceeds 5 minutes"}), 400    
            
        #created_files = []
        # Save the file to a temporary path
        #unique_id = str(uuid.uuid4())
        print(unique_id)
        
        filename = werkzeug.utils.secure_filename(file.filename)
        input_audio_path = os.path.join(tmp, f"{spk_id}_input_audio_{unique_id}.{filename.split('.')[-1]}")
        file.save(input_audio_path)
        
        #created_files.append(input_audio_path)
        
        #split audio
        task_status_tracker[unique_id] = {"status": "Processing: Step 1", "percentage": 30}

        cut_vocal_and_inst(input_audio_path,spk_id,unique_id)
        print("audio splitting performed")
        vocal_path = f"output/{spk_id}_{unique_id}/{split_model}/{spk_id}_input_audio_{unique_id}/vocals.wav"
        inst = f"output/{spk_id}_{unique_id}/{split_model}/{spk_id}_input_audio_{unique_id}/no_vocals.wav"
        print("*****before making call to convert ", unique_id)
        #task_status_tracker[unique_id] = "Processing: Step 2"
        #output_queue = SimpleQueue()
        ctx = get_context('spawn')
        output_queue = ctx.Queue()
    # Create and start the process
        p = ctx.Process(target=worker, args=(spk_id, vocal_path, voice_transform, unique_id, output_queue,))
        p.start()
    
    # Wait for the process to finish and get the result
        p.join()
        print("*******waiting for process to complete ")
        
        output_path = output_queue.get()
        task_status_tracker[unique_id] = {"status": "Processing: Step 2", "percentage": 80}
        #if isinstance(output_path, Exception):
         #   print("Exception in worker:", output_path)
        #else:
         #   print("output path of converted voice", output_path)
        #output_path = convert_voice(spk_id, vocal_path, voice_transform,unique_id)
        output_path1= combine_vocal_and_inst(output_path,inst,unique_id)
        
        processed_audio_storage[unique_id] = output_path1
        session['processed_audio_id'] = unique_id 
        task_status_tracker[unique_id] = {"status": "Finalizing", "percentage": 100}
        print(output_path1)
        
        #created_files.extend([vocal_path, inst, output_path])
        
        #upload_to_do(output_path1)
        
        image_files = ['1.jpg', '2.jpg', '3.jpg', '4.jpg', '5.jpg', '6.jpg']
    
    # Choose a random image file
        image_path = random.choice(image_files)
        
        #image_path = 'singer.jpg'
        os.makedirs("output/result", exist_ok=True)
        output_dir="output/result"
        mp4_path = merge_audio_image(output_path1, image_path, output_dir, unique_id)
        upload_to_do(mp4_path)
        
        task_status_tracker[unique_id]["status"] = "Completed"
        print("file uploaded to Digital ocean space")
        
        return jsonify({"message": "File processed successfully", "audio_id": unique_id}), 200
    finally:
        request_semaphore.release()
    #if os.path.exists(output_path1):
        
    #    return send_file(output_path1, as_attachment=True)
    #else:
    #    return jsonify({"error": "File not found."}), 404

def convert_voice_thread_safe(spk_id, vocal_path, voice_transform, unique_id):
    with convert_voice_lock:
        return convert_voice(spk_id, vocal_path, voice_transform, unique_id)



def get_vc_safe(sid, to_return_protect0):
    with convert_voice_lock:
        return get_vc(sid, to_return_protect0)

@app.route('/')
def upload_form():
    return render_template('ui.html')

@app.route('/get_processed_audio/<audio_id>')
def get_processed_audio(audio_id):
    # Retrieve the path from temporary storage or session
    if audio_id in processed_audio_storage:
        file_path = processed_audio_storage[audio_id]
        return send_file(file_path, as_attachment=True)
    return jsonify({"error": "File not found."}), 404

def worker(spk_id, input_audio_path, voice_transform, unique_id, output_queue):
    try:
        
        output_audio_path = convert_voice(spk_id, input_audio_path, voice_transform, unique_id)
        print("output in worker for audio file", output_audio_path)
        output_queue.put(output_audio_path)
        print("added to output queue")
    except Exception as e:
        print("exception in adding to queue")
        output_queue.put(e)  # Send the exception to the main process for debugging
        

def convert_voice(spk_id, input_audio_path, voice_transform,unique_id):
    get_vc(spk_id,0.5)
    print("*****before makinf call to vc ", unique_id)

   
    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,
        unique_id=unique_id
    )
    print(output_audio_path)
    return output_audio_path

def cut_vocal_and_inst(audio_path,spk_id,unique_id):
    
    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
    print("before executing splitter")
    command = f"demucs --two-stems=vocals -n {split_model} {audio_path} -o output/{spk_id}_{unique_id}"
    env = os.environ.copy()

# Add or modify the environment variable for this subprocess
    env["CUDA_VISIBLE_DEVICES"] = "0"
    
    
    
    #result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
    result = subprocess.run(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.returncode != 0:
        print("Demucs process failed:", result.stderr)
    else:
        print("Demucs process completed successfully.")
    print("after executing splitter")
    #for line in result.stdout:
    #    logs.append(line)
    #    yield "\n".join(logs), None, None
    
    print(result.stdout)
    vocal = f"output/{split_model}/{spk_id}_input_audio/vocals.wav"
    inst = f"output/{split_model}/{spk_id}_input_audio/no_vocals.wav"
    #logs.append("Audio splitting complete.")


def combine_vocal_and_inst(vocal_path, inst_path, output_path):
    
    vocal_volume=1
    inst_volume=1
    os.makedirs("output/result", exist_ok=True)
    # Assuming vocal_path and inst_path are now directly passed as arguments
    output_path = f"output/result/{output_path}.mp3"
    #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}"'
    #command=f'ffmpeg -y -i "{inst_path}" -i "{vocal_path}" -filter_complex "amix=inputs=2:duration=longest" -b:a 320k -c:a libmp3lame "{output_path}"'
    # Load the audio files
    print(vocal_path)
    print(inst_path)
    vocal = AudioSegment.from_file(vocal_path)
    instrumental = AudioSegment.from_file(inst_path)

# Overlay the vocal track on top of the instrumental track
    combined = vocal.overlay(instrumental)

# Export the result
    combined.export(output_path, format="mp3")

    #result = subprocess.run(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    return output_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,
    unique_id
):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
    global tgt_sr, net_g, vc, hubert_model, version, cpt
    print("***** in vc ", unique_id)

    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
        )
        
    
    # Get the current thread's name or ID
        
        
     
        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
        # Assuming 'unique_id' is passed to convert_voice function along with 'sid'
        print("***** before writing to file outout ", unique_id)
        output_file_path = os.path.join("output", f"converted_audio_{sid}_{unique_id}.wav")  # Adjust path as needed

        print("******* output file path ",output_file_path)
        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[sid].get("f0", 1)
            version = cpt[sid].get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt[sid]["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt[sid]["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt[sid]["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt[sid]["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[sid] = torch.load(os.path.join(weight_root, sid), map_location="cpu")
    tgt_sr = cpt[sid]["config"][-1]
    cpt[sid]["config"][-3] = cpt[sid]["weight"]["emb_g.weight"].shape[0]
    if_f0 = cpt[sid].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[sid].get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt[sid]["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt[sid]["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt[sid]["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt[sid]["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt[sid]["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[sid]["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'
        )
    )



    

if __name__ == '__main__':
    app.run(debug=False, port=5000,host='0.0.0.0')