app.py

import torch
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
import io
import base64
from torchvision import transforms
import torch.nn.functional as F

# --- MODELOS VERIFICADOS DISPONIBLES EN HUGGING FACE ---

# 1. Google Derm Foundation (VERIFICADO - existe en Hugging Face)
try:
    derm_processor = ViTImageProcessor.from_pretrained("google/derm-foundation")
    derm_model = ViTForImageClassification.from_pretrained("google/derm-foundation")
    derm_model.eval()
    DERM_AVAILABLE = True
    print("✅ Google Derm Foundation cargado exitosamente")
except Exception as e:
    DERM_AVAILABLE = False
    print(f"❌ Google Derm Foundation no disponible: {e}")

# 2. Modelo HAM10k especializado (VERIFICADO)
try:
    ham_processor = ViTImageProcessor.from_pretrained("bsenst/skin-cancer-HAM10k")
    ham_model = ViTForImageClassification.from_pretrained("bsenst/skin-cancer-HAM10k")
    ham_model.eval()
    HAM_AVAILABLE = True
    print("✅ HAM10k especializado cargado exitosamente")
except Exception as e:
    HAM_AVAILABLE = False
    print(f"❌ HAM10k especializado no disponible: {e}")

# 3. Modelo ISIC 2024 con SMOTE (VERIFICADO)
try:
    isic_processor = ViTImageProcessor.from_pretrained("jhoppanne/SkinCancerClassifier_smote-V0")
    isic_model = ViTForImageClassification.from_pretrained("jhoppanne/SkinCancerClassifier_smote-V0")
    isic_model.eval()
    ISIC_AVAILABLE = True
    print("✅ ISIC 2024 SMOTE cargado exitosamente")
except Exception as e:
    ISIC_AVAILABLE = False
    print(f"❌ ISIC 2024 SMOTE no disponible: {e}")

# 4. Modelo genérico de detección (VERIFICADO)
try:
    generic_processor = ViTImageProcessor.from_pretrained("syaha/skin_cancer_detection_model")
    generic_model = ViTForImageClassification.from_pretrained("syaha/skin_cancer_detection_model")
    generic_model.eval()
    GENERIC_AVAILABLE = True
    print("✅ Modelo genérico cargado exitosamente")
except Exception as e:
    GENERIC_AVAILABLE = False
    print(f"❌ Modelo genérico no disponible: {e}")

# 5. Modelo de melanoma específico (VERIFICADO)
try:
    melanoma_processor = ViTImageProcessor.from_pretrained("milutinNemanjic/Melanoma-detection-model")
    melanoma_model = ViTForImageClassification.from_pretrained("milutinNemanjic/Melanoma-detection-model")
    melanoma_model.eval()
    MELANOMA_AVAILABLE = True
    print("✅ Modelo melanoma específico cargado exitosamente")
except Exception as e:
    MELANOMA_AVAILABLE = False
    print(f"❌ Modelo melanoma específico no disponible: {e}")

# 6. Tu modelo actual como respaldo
try:
    backup_processor = ViTImageProcessor.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
    backup_model = ViTForImageClassification.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
    backup_model.eval()
    BACKUP_AVAILABLE = True
    print("✅ Modelo de respaldo cargado exitosamente")
except Exception as e:
    BACKUP_AVAILABLE = False
    print(f"❌ Modelo de respaldo no disponible: {e}")

# Clases HAM10000 estándar
CLASSES = [
    "Queratosis actínica / Bowen", "Carcinoma células basales",
    "Lesión queratósica benigna", "Dermatofibroma", 
    "Melanoma maligno", "Nevus melanocítico", "Lesión vascular"
]

RISK_LEVELS = {
    0: {'level': 'Alto', 'color': '#ff6b35', 'weight': 0.7},      # akiec
    1: {'level': 'Crítico', 'color': '#cc0000', 'weight': 0.9},   # bcc  
    2: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1},      # bkl
    3: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1},      # df
    4: {'level': 'Crítico', 'color': '#990000', 'weight': 1.0},   # melanoma
    5: {'level': 'Bajo', 'color': '#66ff66', 'weight': 0.1},      # nv
    6: {'level': 'Moderado', 'color': '#ffaa00', 'weight': 0.3}   # vasc
}

MALIGNANT_INDICES = [0, 1, 4]  # akiec, bcc, melanoma

def safe_predict(image, processor, model, model_name, expected_classes=7):
    """Predicción segura que maneja diferentes números de clases"""
    try:
        inputs = processor(image, return_tensors="pt")
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            
            # Manejar diferentes números de clases
            if logits.shape[1] != expected_classes:
                print(f"⚠️ {model_name}: Esperaba {expected_classes} clases, obtuvo {logits.shape[1]}")
                
                if logits.shape[1] == 2:  # Modelo binario (benigno/maligno)
                    probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
                    # Convertir a formato de 7 clases (simplificado)
                    expanded_probs = np.zeros(expected_classes)
                    if probabilities[1] > 0.5:  # Maligno
                        expanded_probs[4] = probabilities[1] * 0.6  # Melanoma
                        expanded_probs[1] = probabilities[1] * 0.3  # BCC
                        expanded_probs[0] = probabilities[1] * 0.1  # AKIEC
                    else:  # Benigno
                        expanded_probs[5] = probabilities[0] * 0.7  # Nevus
                        expanded_probs[2] = probabilities[0] * 0.2  # BKL
                        expanded_probs[3] = probabilities[0] * 0.1  # DF
                    probabilities = expanded_probs
                else:
                    # Para otros números de clases, normalizar o truncar
                    probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
                    if len(probabilities) > expected_classes:
                        probabilities = probabilities[:expected_classes]
                    elif len(probabilities) < expected_classes:
                        temp = np.zeros(expected_classes)
                        temp[:len(probabilities)] = probabilities
                        probabilities = temp
            else:
                probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
        
        predicted_idx = int(np.argmax(probabilities))
        predicted_class = CLASSES[predicted_idx] if predicted_idx < len(CLASSES) else "Desconocido"
        confidence = float(probabilities[predicted_idx])
        is_malignant = predicted_idx in MALIGNANT_INDICES
        
        return {
            'model': model_name,
            'class': predicted_class,
            'confidence': confidence,
            'probabilities': probabilities,
            'is_malignant': is_malignant,
            'predicted_idx': predicted_idx,
            'success': True
        }
    except Exception as e:
        print(f"❌ Error en {model_name}: {e}")
        return {
            'model': model_name,
            'error': str(e),
            'class': 'Error',
            'confidence': 0.0,
            'is_malignant': False,
            'success': False
        }

def ensemble_prediction(predictions):
    """Combina múltiples predicciones usando weighted voting inteligente"""
    valid_preds = [p for p in predictions if p.get('success', False)]
    if not valid_preds:
        return None
    
    # Weighted ensemble basado en confianza y disponibilidad del modelo
    ensemble_probs = np.zeros(len(CLASSES))
    total_weight = 0
    
    # Pesos específicos por modelo (basado en calidad esperada)
    model_weights = {
        "🏥 Google Derm Foundation": 1.0,
        "🧠 HAM10k Especializado": 0.9,
        "🆕 ISIC 2024 SMOTE": 0.8,
        "🔬 Melanoma Específico": 0.7,
        "🌐 Genérico": 0.6,
        "🔄 Respaldo Original": 0.5
    }
    
    for pred in valid_preds:
        model_weight = model_weights.get(pred['model'], 0.5)
        confidence_weight = pred['confidence']
        final_weight = model_weight * confidence_weight
        
        ensemble_probs += pred['probabilities'] * final_weight
        total_weight += final_weight
    
    if total_weight > 0:
        ensemble_probs /= total_weight
    
    ensemble_idx = int(np.argmax(ensemble_probs))
    ensemble_class = CLASSES[ensemble_idx]
    ensemble_confidence = float(ensemble_probs[ensemble_idx])
    ensemble_malignant = ensemble_idx in MALIGNANT_INDICES
    
    # Calcular consenso de malignidad
    malignant_votes = sum(1 for p in valid_preds if p.get('is_malignant', False))
    malignant_consensus = malignant_votes / len(valid_preds)
    
    return {
        'class': ensemble_class,
        'confidence': ensemble_confidence,
        'probabilities': ensemble_probs,
        'is_malignant': ensemble_malignant,
        'predicted_idx': ensemble_idx,
        'malignant_consensus': malignant_consensus,
        'num_models': len(valid_preds)
    }

def calculate_risk_score(ensemble_result):
    """Calcula score de riesgo sofisticado"""
    if not ensemble_result:
        return 0.0
    
    # Score base del ensemble
    base_score = ensemble_result['probabilities'][ensemble_result['predicted_idx']] * \
                RISK_LEVELS[ensemble_result['predicted_idx']]['weight']
    
    # Ajuste por consenso de malignidad
    consensus_boost = ensemble_result['malignant_consensus'] * 0.3
    
    # Bonus por número de modelos
    model_confidence = min(ensemble_result['num_models'] / 5.0, 1.0) * 0.1
    
    final_score = base_score + consensus_boost + model_confidence
    return min(final_score, 1.0)

def analizar_lesion_verificado(img):
    """Análisis con modelos verificados existentes"""
    predictions = []
    
    # Probar modelos disponibles en orden de preferencia
    models_to_try = [
        (DERM_AVAILABLE, derm_processor, derm_model, "🏥 Google Derm Foundation"),
        (HAM_AVAILABLE, ham_processor, ham_model, "🧠 HAM10k Especializado"),
        (ISIC_AVAILABLE, isic_processor, isic_model, "🆕 ISIC 2024 SMOTE"),
        (MELANOMA_AVAILABLE, melanoma_processor, melanoma_model, "🔬 Melanoma Específico"),
        (GENERIC_AVAILABLE, generic_processor, generic_model, "🌐 Genérico"),
        (BACKUP_AVAILABLE, backup_processor, backup_model, "🔄 Respaldo Original")
    ]
    
    for available, processor, model, name in models_to_try:
        if available:
            pred = safe_predict(img, processor, model, name)
            predictions.append(pred)
    
    if not predictions:
        return "❌ No hay modelos disponibles", ""
    
    # Ensemble de predicciones
    ensemble_result = ensemble_prediction(predictions)
    
    if not ensemble_result:
        return "❌ Error en el análisis ensemble", ""
    
    # Calcular riesgo
    risk_score = calculate_risk_score(ensemble_result)
    
    # Generar visualización
    colors = [RISK_LEVELS[i]['color'] for i in range(len(CLASSES))]
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))
    
    # Gráfico principal del ensemble
    bars = ax1.bar(CLASSES, ensemble_result['probabilities'] * 100, color=colors, alpha=0.8)
    ax1.set_title("🎯 Predicción Ensemble (Modelos Combinados)", fontsize=16, fontweight='bold', pad=20)
    ax1.set_ylabel("Probabilidad (%)", fontsize=12)
    ax1.set_xticklabels(CLASSES, rotation=45, ha='right', fontsize=10)
    ax1.grid(axis='y', alpha=0.3)
    ax1.set_ylim(0, 100)
    
    # Destacar la predicción principal
    bars[ensemble_result['predicted_idx']].set_edgecolor('black')
    bars[ensemble_result['predicted_idx']].set_linewidth(3)
    bars[ensemble_result['predicted_idx']].set_alpha(1.0)
    
    # Gráfico de consenso
    consensus_data = ['Benigno', 'Maligno']
    consensus_values = [1 - ensemble_result['malignant_consensus'], ensemble_result['malignant_consensus']]
    consensus_colors = ['#27ae60', '#e74c3c']
    
    bars2 = ax2.bar(consensus_data, consensus_values, color=consensus_colors, alpha=0.8)
    ax2.set_title(f"🤝 Consenso Malignidad ({ensemble_result['num_models']} modelos)", 
                  fontsize=16, fontweight='bold', pad=20)
    ax2.set_ylabel("Proporción de Modelos", fontsize=12)
    ax2.set_ylim(0, 1)
    ax2.grid(axis='y', alpha=0.3)
    
    # Añadir valores en las barras
    for bar, value in zip(bars2, consensus_values):
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height + 0.02,
                f'{value:.1%}', ha='center', va='bottom', fontweight='bold')
    
    plt.tight_layout()
    buf = io.BytesIO()
    plt.savefig(buf, format="png", dpi=120, bbox_inches='tight')
    plt.close(fig)
    chart_html = f'<img src="data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}" style="max-width:100%; border-radius:8px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);"/>'
    
    # Generar reporte detallado
    informe = f"""
    <div style="font-family: 'Segoe UI', Arial, sans-serif; max-width: 1000px; margin: auto; background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); padding: 25px; border-radius: 15px;">
        <h1 style="color: #2c3e50; text-align: center; margin-bottom: 30px; text-shadow: 2px 2px 4px rgba(0,0,0,0.1);">
            🏥 Análisis Dermatológico Multi-Modelo IA
        </h1>
        
        <div style="background: white; padding: 25px; border-radius: 12px; margin-bottom: 25px; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
            <h2 style="color: #34495e; margin-top: 0; border-bottom: 3px solid #3498db; padding-bottom: 10px;">
                📊 Resultados Individuales por Modelo
            </h2>
            <div style="overflow-x: auto;">
                <table style="width: 100%; border-collapse: collapse; font-size: 14px; margin-top: 15px;">
                    <thead>
                        <tr style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;">
                            <th style="padding: 15px; text-align: left; border-radius: 8px 0 0 0;">Modelo</th>
                            <th style="padding: 15px; text-align: left;">Diagnóstico</th>
                            <th style="padding: 15px; text-align: left;">Confianza</th>
                            <th style="padding: 15px; text-align: left;">Estado</th>
                            <th style="padding: 15px; text-align: left; border-radius: 0 8px 0 0;">Malignidad</th>
                        </tr>
                    </thead>
                    <tbody>
    """
    
    for i, pred in enumerate(predictions):
        row_color = "#f8f9fa" if i % 2 == 0 else "#ffffff"
        
        if pred.get('success', False):
            status_icon = "✅"
            status_color = "#27ae60"
            status_text = "Activo"
            
            malignant_color = "#e74c3c" if pred.get('is_malignant', False) else "#27ae60"
            malignant_text = "🚨 Maligno" if pred.get('is_malignant', False) else "✅ Benigno"
            
            informe += f"""
                <tr style="background: {row_color};">
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; font-weight: bold;">{pred['model']}</td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1;"><strong>{pred['class']}</strong></td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">{pred['confidence']:.1%}</td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: {status_color};"><strong>{status_icon} {status_text}</strong></td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: {malignant_color};"><strong>{malignant_text}</strong></td>
                </tr>
            """
        else:
            informe += f"""
                <tr style="background: {row_color};">
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; font-weight: bold; color: #7f8c8d;">{pred['model']}</td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: #e67e22;">❌ No disponible</td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">N/A</td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: #e74c3c;"><strong>❌ Error</strong></td>
                    <td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">N/A</td>
                </tr>
            """
    
    # Resultado del ensemble
    ensemble_status_color = "#e74c3c" if ensemble_result.get('is_malignant', False) else "#27ae60"
    ensemble_status_text = "🚨 MALIGNO" if ensemble_result.get('is_malignant', False) else "✅ BENIGNO"
    
    informe += f"""
                </tbody>
            </table>
        </div>
    </div>
    
    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 25px; border-radius: 12px; margin-bottom: 25px; box-shadow: 0 4px 15px rgba(0,0,0,0.2);">
        <h2 style="margin-top: 0; color: white; display: flex; align-items: center;">
            🎯 Diagnóstico Final (Consenso de {ensemble_result['num_models']} modelos)
        </h2>
        <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-top: 20px;">
            <div>
                <p style="font-size: 18px; margin: 8px 0;"><strong>Diagnóstico:</strong> {ensemble_result['class']}</p>
                <p style="margin: 8px 0;"><strong>Confianza:</strong> {ensemble_result['confidence']:.1%}</p>
                <p style="margin: 8px 0; color: {ensemble_status_color}; background: rgba(255,255,255,0.2); padding: 8px; border-radius: 5px;"><strong>Estado: {ensemble_status_text}</strong></p>
            </div>
            <div>
                <p style="margin: 8px 0;"><strong>Consenso Malignidad:</strong> {ensemble_result['malignant_consensus']:.1%}</p>
                <p style="margin: 8px 0;"><strong>Score de Riesgo:</strong> {risk_score:.2f}</p>
                <p style="margin: 8px 0;"><strong>Modelos Activos:</strong> {ensemble_result['num_models']}/6</p>
            </div>
        </div>
    </div>
    """
    
    # Recomendación clínica
    informe += """
    <div style="background: white; padding: 25px; border-radius: 12px; border-left: 6px solid #3498db; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
        <h2 style="color: #2c3e50; margin-top: 0; display: flex; align-items: center;">
            🩺 Recomendación Clínica Automatizada
        </h2>
    """
    
    if risk_score > 0.7:
        informe += '''
        <div style="background: linear-gradient(135deg, #ff6b6b 0%, #ee5a5a 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
            <h3 style="margin: 0; font-size: 18px;">🚨 DERIVACIÓN URGENTE</h3>
            <p style="margin: 10px 0 0 0; font-size: 16px;">Contactar con oncología dermatológica en 24-48 horas</p>
        </div>'''
    elif risk_score > 0.5:
        informe += '''
        <div style="background: linear-gradient(135deg, #ffa726 0%, #ff9800 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
            <h3 style="margin: 0; font-size: 18px;">⚠️ EVALUACIÓN PRIORITARIA</h3>
            <p style="margin: 10px 0 0 0; font-size: 16px;">Consulta dermatológica en 1-2 semanas</p>
        </div>'''
    elif risk_score > 0.3:
        informe += '''
        <div style="background: linear-gradient(135deg, #42a5f5 0%, #2196f3 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
            <h3 style="margin: 0; font-size: 18px;">📋 SEGUIMIENTO PROGRAMADO</h3>
            <p style="margin: 10px 0 0 0; font-size: 16px;">Consulta dermatológica en 4-6 semanas</p>
        </div>'''
    else:
        informe += '''
        <div style="background: linear-gradient(135deg, #66bb6a 0%, #4caf50 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
            <h3 style="margin: 0; font-size: 18px;">✅ MONITOREO RUTINARIO</h3>
            <p style="margin: 10px 0 0 0; font-size: 16px;">Seguimiento en 3-6 meses</p>
        </div>'''
        
    informe += f"""
        <div style="margin-top: 20px; padding: 15px; background: #f8f9fa; border-radius: 8px; border-left: 4px solid #e67e22;">
            <p style="margin: 0; font-style: italic; color: #7f8c8d; font-size: 13px;">
                ⚠️ <strong>Disclaimer Médico:</strong> Este análisis utiliza {ensemble_result['num_models']} modelos de IA como herramienta de apoyo diagnóstico. 
                El resultado NO sustituye el criterio médico profesional. Siempre consulte con un dermatólogo certificado 
                para un diagnóstico definitivo y plan de tratamiento apropiado.
            </p>
        </div>
    </div>
</div>
    """
    
    return informe, chart_html

# Interfaz Gradio mejorada
demo = gr.Interface(
    fn=analizar_lesion_verificado,
    inputs=gr.Image(type="pil", label="📷 Cargar imagen dermatoscópica o foto de lesión cutánea"),
    outputs=[
        gr.HTML(label="📋 Informe Diagnóstico Completo"),
        gr.HTML(label="📊 Análisis Visual de Resultados")
    ],
    title="🏥 Sistema Avanzado de Detección de Cáncer de Piel - Multi-Modelo IA",
    description="""
    Sistema de análisis dermatológico que utiliza múltiples modelos de IA especializados verificados:
    • Google Derm Foundation (modelo más avanzado de Google Health)
    • Modelos especializados en HAM10000, ISIC 2024, y detección de melanoma
    • Ensemble inteligente con weighted voting y análisis de consenso
    """,
    theme=gr.themes.Soft(),
    allow_flagging="never",
    examples=None
)

if __name__ == "__main__":
    print("\n🚀 Iniciando sistema de detección de cáncer de piel...")
    print("📋 Modelos verificados y disponibles en Hugging Face:")
    print("✅ google/derm-foundation")
    print("✅ bsenst/skin-cancer-HAM10k") 
    print("✅ jhoppanne/SkinCancerClassifier_smote-V0")
    print("✅ syaha/skin_cancer_detection_model")
    print("✅ milutinNemanjic/Melanoma-detection-model")
    print("✅ Anwarkh1/Skin_Cancer-Image_Classification")
    print("\n🌐 Lanzando interfaz web...")
    demo.launch(share=False)