# app.py - Antigravity Shield v4.2 JURY (Visual Edition Optimized)
# -*- coding: utf-8 -*-
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
import noisereduce as nr
import soundfile as sf
import hashlib
import os
import gradio as gr

# ==========================================
# 1. OUTILS DE TRAITEMENT
# ==========================================
class AudioManipulator:
    def charger_audio(self, path):
        return librosa.load(path, sr=44100)

    def reduire_bruit(self, y, sr):
        return nr.reduce_noise(y=y, sr=sr, prop_decrease=0.8)

# ==========================================
# 2. LE BOUCLIER IA v4.2 JURY
# ==========================================
class AuthenticMusicShield:
    def __init__(self):
        print("🚀 Lancement du Bouclier v4.2 (Expert Radio & Jury)...")
        self.ia = None  # Ne pas charger le modèle au démarrage

    def load_model(self):
        if self.ia is None:
            try:
                from transformers import pipeline
                self.ia = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
                print("✅ Modèle IA chargé.")
            except Exception as e:
                print(f"⚠️ Échec du chargement du modèle IA : {e}")
                self.ia = None

    def get_sha256(self, path):
        with open(path, "rb") as f:
            return hashlib.sha256(f.read()).hexdigest()

    def generer_spectrogramme(self, y, sr):
        plt.figure(figsize=(10, 4))
        S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
        S_dB = librosa.power_to_db(S, ref=np.max)
        librosa.display.specshow(S_dB, sr=sr, x_axis='time', y_axis='mel', cmap='magma')
        plt.colorbar(format='%+2.0f dB')
        plt.title('Empreinte Spectrale (Texture)')
        plt.tight_layout()
        plot_path = "spectrum.png"
        plt.savefig(plot_path)
        plt.close()
        return plot_path

    def analyser_expert(self, path):
        if path is None: 
            return None, "En attente...", None
        
        try:
            # Charger le modèle IA seulement si nécessaire
            self.load_model()
            
            file_name = os.path.basename(path)
            file_hash = self.get_sha256(path)
            
            y, sr = librosa.load(path, sr=44100)
            y_denoised = nr.reduce_noise(y=y, sr=sr)

            # Analyse Jitter & SID
            pitches, _ = librosa.piptrack(y=y, sr=sr)
            jitter = np.std(pitches[pitches > 0]) / 1000 if np.any(pitches > 0) else 0
            stft = np.abs(librosa.stft(y_denoised))
            score_sid = 95.0 if np.mean(stft[-1]) > 0.0003 else 15.0

            # Analyse IA
            top_label = "Inconnu"
            score_ia = 0
            if self.ia:
                res_ia = self.ia(path)
                top_label = res_ia[0]['label']
                score_ia = res_ia[0]['score'] * 100

            # Détection du type de source
            is_radio_speech = any(tag in top_label for tag in ["Speech", "Conversation", "Hum", "Radio"])

            # ==========================================
            # LOGIQUE DE DÉCISION v4.2 JURY
            # ==========================================
            if score_sid > 80:
                verdict = "✅ AUTHENTIQUE CERTIFIÉ (SID)"
                note = "Badge SID détecté. Intégrité et provenance garanties."
            elif is_radio_speech:
                if 0.01 < jitter < 1.5:
                    verdict = "✅ AUTHENTIQUE (Humain/Radio)"
                    note = f"Source réelle confirmée par IA ({top_label})."
                else:
                    verdict = "🔴 DEEPFAKE DÉTECTÉ"
                    note = "Anomalie physique extrême sur voix humaine."
            elif "Music" in top_label or "Singing" in top_label or "Inconnu" in top_label:
                if 0.12 < jitter < 0.85:
                    verdict = "✅ AUTHENTIQUE (Artiste)"
                    note = "Vibration humaine naturelle validée."
                else:
                    verdict = "🔴 AI COVER / DEEPFAKE DÉTECTÉ"
                    note = f"Instabilité ou perfection artificielle. Jitter: {jitter:.4f}"
            else:
                verdict = "⚠️ ANALYSE INCONCLUSIVE"
                note = "Signal non identifié par le bouclier."

            # Génération du spectrogramme
            spec_img = self.generer_spectrogramme(y, sr)

            # Rapport Formaté pour le JURY
            report = f"\n{'='*45}\n🛡️ RAPPORT D'EXPERTISE v4.2 JURY\n{'='*45}\n"
            report += f" 📂 NOM FICHIER  : {file_name}\n"
            report += f" 🔑 ID SHA-256   : {file_hash[:16]}...\n"
            report += f" 🎙️ SOURCE IA    : {top_label} ({score_ia:.1f}%)\n"
            report += f" 🧬 INDICE VIE   : {jitter:.4f}\n"
            report += f"{'-'*45}\n"
            report += f" >>> VERDICT : {verdict}\n"
            report += f" >>> NOTE    : {note}\n{'='*45}\n"

            return spec_img, report, {"Verdict": verdict, "Jitter": round(jitter, 4), "SID": score_sid}
            
        except Exception as e:
            return None, f"Erreur : {str(e)}", None

# --- Interface Gradio ---
shield = AuthenticMusicShield()
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🛡️ Antigravity Shield v4.2 PRO (Jury Edition Optimized)")
    
    with gr.Row():
        with gr.Column():
            audio_input = gr.Audio(type="filepath", label="Audio Input")
            run_btn = gr.Button("🔍 ANALYSER L'AUTHENTICITÉ", variant="primary")
        
        with gr.Column():
            image_output = gr.Image(label="Spectrogramme (Preuve Visuelle)")
            report_output = gr.Textbox(label="Rapport Officiel", lines=10)
            metrics_output = gr.Json(label="Métriques Techniques")

    run_btn.click(
        fn=shield.analyser_expert,
        inputs=audio_input,
        outputs=[image_output, report_output, metrics_output]
    )

# SSR désactivé pour réduire la RAM et éviter crash 137
demo.launch(ssr_mode=False)