"""
Clone Vocal - Outil web de clonage vocal base sur Seed-VC (zero-shot).
Interface Gradio en francais, deploye sur HuggingFace Spaces avec ZeroGPU.
"""

import os
import sys
import logging
import tempfile
import shutil

import gradio as gr

# Monkey-patch gradio_client to fix "argument of type 'bool' is not iterable"
try:
    import gradio_client.utils as _gc_utils

    _orig_get_type = _gc_utils.get_type

    def _patched_get_type(schema, *args, **kwargs):
        if not isinstance(schema, dict):
            return "Any"
        return _orig_get_type(schema, *args, **kwargs)

    _gc_utils.get_type = _patched_get_type

    _orig_json_schema = _gc_utils._json_schema_to_python_type

    def _patched_json_schema(schema, *args, **kwargs):
        if not isinstance(schema, dict):
            return "Any"
        return _orig_json_schema(schema, *args, **kwargs)

    _gc_utils._json_schema_to_python_type = _patched_json_schema
    _gc_utils.json_schema_to_python_type = lambda schema, defs=None: _patched_json_schema(
        schema, defs
    )
except Exception:
    pass

# Setup logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

# Startup: clone Seed-VC
logger.info("Initialisation de l'application...")

from pipeline.setup import setup_seed_vc
from pipeline.storage import init_storage, list_models, download_model, delete_model, get_reference_path

try:
    setup_seed_vc()
except Exception as e:
    logger.error("Erreur lors du setup: {}".format(e))

# Initialize model storage
HF_MODELS_REPO = os.environ.get("HF_MODELS_REPO", "")
if HF_MODELS_REPO:
    init_storage(HF_MODELS_REPO)
    logger.info("Stockage HuggingFace configure: {}".format(HF_MODELS_REPO))

# Import GPU-decorated functions for ZeroGPU detection
from pipeline.training import save_voice_reference, _gpu_warmup
from pipeline.separation import separate_audio
from pipeline.inference import convert_voice


# -- Training Tab --

def train_voice_model(audio_file, model_name, progress=gr.Progress()):
    """Handler: save voice reference."""
    if audio_file is None:
        return "Erreur : Veuillez uploader un fichier audio.", None

    if not model_name or not model_name.strip():
        return "Erreur : Veuillez entrer un nom pour le modele.", None

    model_name = model_name.strip().replace(" ", "_")

    def progress_callback(value, desc):
        progress(value, desc=desc)

    try:
        progress(0.0, desc="Demarrage...")
        pth_path, ref_path = save_voice_reference(
            audio_path=audio_file,
            model_name=model_name,
            progress_callback=progress_callback,
        )

        return "Reference vocale '{}' sauvegardee avec succes !".format(model_name), ref_path

    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        logger.error("Erreur training: {}".format(tb))
        return "Erreur : {}: {}\n\nDetails:\n{}".format(
            type(e).__name__, str(e), tb[-500:]
        ), None


# -- Conversion Tab --

def get_model_choices():
    """Get list of trained model names for dropdown."""
    models = list_models()
    if not models:
        return ["(aucun modele)"]
    return models


def convert_song(
    model_choice,
    song_file,
    pitch,
    similarity,
    diffusion_steps,
    vocal_volume,
    instrumental_volume,
    progress=gr.Progress(),
):
    """Full pipeline: separate + convert + mix."""
    if song_file is None:
        return "Erreur : Veuillez uploader un fichier audio.", None, None, None

    if model_choice == "(aucun modele)" or not model_choice:
        return "Erreur : Veuillez d'abord enregistrer une reference vocale.", None, None, None

    from pipeline.mixing import mix_audio

    try:
        # Step 1: Download model / find reference audio
        progress(0.05, desc="Chargement du modele...")
        pth_path, ref_or_index = download_model(model_choice)
        if not pth_path:
            return "Erreur : Modele '{}' introuvable.".format(model_choice), None, None, None

        # Find the reference audio path
        reference_path = get_reference_path(model_choice)
        if not reference_path:
            return "Erreur : Audio de reference introuvable pour '{}'.".format(model_choice), None, None, None

        # Step 2: Separate vocals from instruments
        progress(0.10, desc="Separation des pistes (Demucs)...")
        vocals_path, instruments_path = separate_audio(song_file)

        progress(0.40, desc="Conversion vocale (Seed-VC)...")

        # Step 3: Convert vocals with Seed-VC
        converted_path = convert_voice(
            audio_path=vocals_path,
            reference_path=reference_path,
            pitch=int(pitch),
            diffusion_steps=int(diffusion_steps),
            similarity=float(similarity),
        )

        progress(0.85, desc="Mixage final...")

        # Step 4: Mix converted vocals with instruments
        final_path = mix_audio(
            vocals_path=converted_path,
            instruments_path=instruments_path,
            vocal_volume=float(vocal_volume),
            instrumental_volume=float(instrumental_volume),
        )

        progress(1.0, desc="Termine !")

        return (
            "Conversion terminee avec succes !",
            vocals_path,
            converted_path,
            final_path,
        )

    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        logger.error("Erreur conversion: {}".format(tb))
        return "Erreur : {}: {}\n\nDetails:\n{}".format(
            type(e).__name__, str(e), tb[-500:]
        ), None, None, None


# -- Models Tab --

def refresh_models():
    """Refresh the model list as HTML."""
    models = list_models()
    if not models:
        return "<p style='color:gray;'>Aucun modele enregistre</p>"
    rows = "".join(
        "<tr><td>{}</td><td>Disponible</td></tr>".format(m) for m in models
    )
    return (
        "<table style='width:100%;border-collapse:collapse;'>"
        "<tr><th style='text-align:left;border-bottom:1px solid #555;padding:8px;'>Nom</th>"
        "<th style='text-align:left;border-bottom:1px solid #555;padding:8px;'>Statut</th></tr>"
        "{}</table>".format(rows)
    )


def delete_selected_model(model_name_to_delete):
    """Delete a model."""
    if not model_name_to_delete or model_name_to_delete == "(aucun modele)":
        return "Veuillez selectionner un modele a supprimer.", refresh_models()
    try:
        delete_model(model_name_to_delete)
        return "Modele '{}' supprime.".format(model_name_to_delete), refresh_models()
    except Exception as e:
        return "Erreur : {}".format(e), refresh_models()


# -- Build Gradio UI --

DESCRIPTION = """
# Clone Vocal

Outil de clonage vocal **zero-shot** base sur **Seed-VC** (Diffusion Transformer).

**Comment utiliser :**
1. **Onglet "Ma voix"** : Uploadez un court extrait de votre voix (3-30 sec) pour creer votre profil vocal
2. **Onglet "Convertir"** : Uploadez un morceau de musique, l'outil remplace la voix par la votre
3. **Onglet "Modeles"** : Gerez vos profils vocaux

> **Zero-shot** : pas d'entrainement necessaire ! Juste 3-30 secondes de votre voix suffisent.
"""

with gr.Blocks(
    title="Clone Vocal",
    theme=gr.themes.Soft(),
) as app:

    gr.Markdown(DESCRIPTION)

    with gr.Tabs():
        # Tab 1: Voice Reference
        with gr.TabItem("Ma voix"):
            gr.Markdown("### Enregistrer votre reference vocale")

            with gr.Row():
                with gr.Column(scale=2):
                    train_audio = gr.Audio(
                        label="Extrait de votre voix (WAV ou MP3, 3-30 secondes)",
                        type="filepath",
                        sources=["upload"],
                    )
                    train_model_name = gr.Textbox(
                        label="Nom du profil",
                        placeholder="ex: ma_voix",
                        max_lines=1,
                    )
                    train_btn = gr.Button(
                        "Sauvegarder",
                        variant="primary",
                        size="lg",
                    )

                with gr.Column(scale=1):
                    train_status = gr.Textbox(
                        label="Statut",
                        interactive=False,
                        lines=3,
                    )
                    train_download = gr.File(
                        label="Fichier de reference",
                        interactive=False,
                    )

            gr.Markdown(
                "**Conseils :**\n"
                "- Utilisez un enregistrement propre (pas de bruit de fond, pas de musique)\n"
                "- Parlez ou chantez naturellement pendant 3 a 30 secondes\n"
                "- Plus l'extrait est long et varie, meilleur sera le resultat\n"
                "- Format WAV ou MP3 accepte"
            )

            train_btn.click(
                fn=train_voice_model,
                inputs=[train_audio, train_model_name],
                outputs=[train_status, train_download],
            )

        # Tab 2: Conversion
        with gr.TabItem("Convertir un morceau"):
            gr.Markdown("### Remplacer la voix d'un morceau par la votre")

            with gr.Row():
                with gr.Column(scale=2):
                    convert_model = gr.Dropdown(
                        choices=get_model_choices(),
                        label="Profil vocal",
                        interactive=True,
                    )
                    refresh_btn = gr.Button("Rafraichir la liste", size="sm")
                    convert_audio = gr.Audio(
                        label="Morceau a convertir (WAV ou MP3)",
                        type="filepath",
                        sources=["upload"],
                    )

                    with gr.Accordion("Parametres avances", open=False):
                        convert_pitch = gr.Slider(
                            minimum=-24,
                            maximum=24,
                            value=0,
                            step=1,
                            label="Transposition (demi-tons)",
                        )
                        convert_similarity = gr.Slider(
                            minimum=0.0,
                            maximum=1.0,
                            value=0.7,
                            step=0.05,
                            label="Similarite vocale (0.5=naturel, 0.7=equilibre, 0.9=plus fidele)",
                        )
                        convert_diffusion = gr.Slider(
                            minimum=5,
                            maximum=100,
                            value=25,
                            step=5,
                            label="Qualite (10=rapide, 25=equilibre, 50=haute qualite)",
                        )
                        convert_vocal_vol = gr.Slider(
                            minimum=0.0,
                            maximum=2.0,
                            value=1.0,
                            step=0.1,
                            label="Volume de la voix",
                        )
                        convert_inst_vol = gr.Slider(
                            minimum=0.0,
                            maximum=2.0,
                            value=1.0,
                            step=0.1,
                            label="Volume des instruments",
                        )

                    convert_btn = gr.Button(
                        "Convertir et mixer",
                        variant="primary",
                        size="lg",
                    )

                with gr.Column(scale=1):
                    convert_status = gr.Textbox(
                        label="Statut",
                        interactive=False,
                        lines=3,
                    )
                    gr.Markdown("**Apercu des pistes :**")
                    preview_vocals = gr.Audio(
                        label="Voix originale (separee)",
                        interactive=False,
                    )
                    preview_converted = gr.Audio(
                        label="Voix convertie",
                        interactive=False,
                    )
                    gr.Markdown("**Resultat final :**")
                    final_output = gr.Audio(
                        label="Morceau final (voix + instruments)",
                        interactive=False,
                    )

            refresh_btn.click(
                fn=lambda: gr.Dropdown(choices=get_model_choices()),
                outputs=[convert_model],
            )

            convert_btn.click(
                fn=convert_song,
                inputs=[
                    convert_model,
                    convert_audio,
                    convert_pitch,
                    convert_similarity,
                    convert_diffusion,
                    convert_vocal_vol,
                    convert_inst_vol,
                ],
                outputs=[convert_status, preview_vocals, preview_converted, final_output],
            )

        # Tab 3: Models
        with gr.TabItem("Mes modeles"):
            gr.Markdown("### Gerer vos profils vocaux")

            models_table = gr.HTML(
                value=refresh_models(),
                label="Modeles enregistres",
            )

            with gr.Row():
                models_refresh_btn = gr.Button("Rafraichir", size="sm")
                models_delete_name = gr.Dropdown(
                    choices=get_model_choices(),
                    label="Modele a supprimer",
                    interactive=True,
                )
                models_delete_btn = gr.Button("Supprimer", variant="stop", size="sm")

            models_delete_status = gr.Textbox(label="Statut", interactive=False)

            models_refresh_btn.click(
                fn=refresh_models,
                outputs=[models_table],
            )
            models_refresh_btn.click(
                fn=lambda: gr.Dropdown(choices=get_model_choices()),
                outputs=[models_delete_name],
            )

            models_delete_btn.click(
                fn=delete_selected_model,
                inputs=[models_delete_name],
                outputs=[models_delete_status, models_table],
            )

        # Tab 4: Debug (temporary)
        with gr.TabItem("Debug GPU"):
            gr.Markdown("### Logs GPU Worker (pour diagnostic)")
            debug_output = gr.Textbox(
                label="Derniers logs GPU",
                interactive=False,
                lines=20,
            )
            debug_btn = gr.Button("Lire les logs", size="sm")

            def read_debug_log():
                log_path = "/home/user/app/debug_gpu.log"
                if os.path.exists(log_path):
                    with open(log_path, "r") as f:
                        return f.read()
                return "Aucun log disponible. Lancez d'abord une conversion."

            debug_btn.click(fn=read_debug_log, outputs=[debug_output])


if __name__ == "__main__":
    app.launch(server_name="0.0.0.0")