# ==================================================================================================
# DEEPFAKE AUDIO - demo_toolbox.py (Legacy Research Interface)
# ==================================================================================================
# 
# 📝 DESCRIPTION
# This script launches the original Qt5-based Research Toolbox. While the modern Gradio
# interface is the preferred entry point for general studio use, the Toolbox remains a
# critical asset for in-depth data visualization, cross-dataset exploration, and
# laboratory-grade synthesis auditing.
#
# 👤 AUTHORS
# - Amey Thakur (https://github.com/Amey-Thakur)
# - Mega Satish (https://github.com/msatmod)
#
# 🤝🏻 CREDITS
# Original Real-Time Voice Cloning methodology by CorentinJ
# Repository: https://github.com/CorentinJ/Real-Time-Voice-Cloning
#
# 🔗 PROJECT LINKS
# Repository: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO
# Video Demo: https://youtu.be/i3wnBcbHDbs
# Research: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO/blob/main/DEEPFAKE-AUDIO.ipynb
#
# 📜 LICENSE
# Released under the MIT License
# Release Date: 2021-02-06
# ==================================================================================================

import argparse
import os
from pathlib import Path

# --- CORE TOOLBOX ENGINE ---
from toolbox import Toolbox
from utils.argutils import print_args
from utils.default_models import ensure_default_models


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description="Runs the toolbox.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument("-d", "--datasets_root", type=Path, help= \
        "Path to the directory containing your datasets. See toolbox/__init__.py for a list of "
        "supported datasets.", default=None)
    parser.add_argument("-m", "--models_dir", type=Path, default="saved_models",
                        help="Directory containing all saved models")
    parser.add_argument("--cpu", action="store_true", help=\
        "If True, all inference will be done on CPU")
    parser.add_argument("--seed", type=int, default=None, help=\
        "Optional random number seed value to make toolbox deterministic.")
    args = parser.parse_args()
    arg_dict = vars(args)
    print_args(args, parser)

    # Hide GPUs from Pytorch to force CPU processing
    if arg_dict.pop("cpu"):
        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

    # Remind the user to download pretrained models if needed
    ensure_default_models(args.models_dir)

    # Launch the toolbox
    Toolbox(**arg_dict)