Update app.py

app.py

@@ -1,7 +1,5 @@
 import torch
 from transformers import ViTImageProcessor, ViTForImageClassification
-from fastai.learner import load_learner
-from fastai.vision.core import PILImage
 from PIL import Image
 import matplotlib.pyplot as plt
 import numpy as np
@@ -9,138 +7,457 @@ import gradio as gr
 import io
 import base64
 from torchvision import transforms
-from efficientnet_pytorch import EfficientNet
-
-# --- Cargar modelo ViT preentrenado fine‑tuned HAM10000 ---
-TF_MODEL_NAME = "Anwarkh1/Skin_Cancer-Image_Classification"
-feature_extractor_tf = ViTImageProcessor.from_pretrained(TF_MODEL_NAME)
-model_tf_vit = ViTForImageClassification.from_pretrained(TF_MODEL_NAME)
-model_tf_vit.eval()
-
-# 🔹 Cargar modelo ViT base
-MODEL_NAME = "ahishamm/vit-base-HAM-10000-sharpened-patch-32"
-feature_extractor = ViTImageProcessor.from_pretrained(MODEL_NAME)
-model_vit = ViTForImageClassification.from_pretrained(MODEL_NAME)
-model_vit.eval()
-
-# 🔹 Cargar modelos Fast.ai locales
-model_malignancy = load_learner("ada_learn_malben.pkl")
-model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")
-
-# 🔹 EfficientNet B7 para binario (benigno vs maligno)
-model_eff = EfficientNet.from_pretrained("efficientnet-b7", num_classes=2)
-model_eff.eval()
-
-eff_transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485, 0.456, 0.406],
-                         [0.229, 0.224, 0.225])
-])
-
-# Clases estándar de HAM10000
+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",
+    "Lesión queratósica benigna", "Dermatofibroma", 
     "Melanoma maligno", "Nevus melanocítico", "Lesión vascular"
 ]
+
 RISK_LEVELS = {
-    0: {'level': 'Moderado', 'color': '#ffaa00', 'weight': 0.6},
-    1: {'level': 'Alto',     'color': '#ff4444', 'weight': 0.8},
-    2: {'level': 'Bajo',     'color': '#44ff44', 'weight': 0.1},
-    3: {'level': 'Bajo',     'color': '#44ff44', 'weight': 0.1},
-    4: {'level': 'Crítico',  'color': '#cc0000', 'weight': 1.0},
-    5: {'level': 'Bajo',     'color': '#44ff44', 'weight': 0.1},
-    6: {'level': 'Bajo',     'color': '#44ff44', 'weight': 0.1}
+    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 analizar_lesion_combined(img):
-    img_fastai = PILImage.create(img)
-
-    # ViT base
-    inputs = feature_extractor(img, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model_vit(**inputs)
-        probs_vit = outputs.logits.softmax(dim=-1).cpu().numpy()[0]
-    idx_vit = int(np.argmax(probs_vit))
-    class_vit = CLASSES[idx_vit]
-    conf_vit = probs_vit[idx_vit]
-
-    # Fast.ai modelos
-    _, _, probs_mal = model_malignancy.predict(img_fastai)
-    prob_malign = float(probs_mal[1])
-    pred_fast_type, _, _ = model_norm2000.predict(img_fastai)
-
-    # ViT fine‑tuned (último modelo recomendado)
-    inputs_tf = feature_extractor_tf(img, return_tensors="pt")
-    with torch.no_grad():
-        outputs_tf = model_tf_vit(**inputs_tf)
-        probs_tf = outputs_tf.logits.softmax(dim=-1).cpu().numpy()[0]
-    idx_tf = int(np.argmax(probs_tf))
-    class_tf_model = CLASSES[idx_tf]
-    conf_tf = probs_tf[idx_tf]
-    mal_tf = "Maligno" if idx_tf in MALIGNANT_INDICES else "Benigno"
-
-    # EfficientNet B7
-    img_eff = eff_transform(img).unsqueeze(0)
-    with torch.no_grad():
-        out_eff = model_eff(img_eff)
-        prob_eff = torch.softmax(out_eff, dim=1)[0, 1].item()
-    eff_result = "Maligno" if prob_eff > 0.5 else "Benigno"
-
-    # Gráfico ViT base
-    colors = [RISK_LEVELS[i]['color'] for i in range(7)]
-    fig, ax = plt.subplots(figsize=(8, 3))
-    ax.bar(CLASSES, probs_vit*100, color=colors)
-    ax.set_title("Probabilidad ViT base por tipo de lesión")
-    ax.set_ylabel("Probabilidad (%)")
-    ax.set_xticks(np.arange(len(CLASSES)))
-    ax.set_xticklabels(CLASSES, rotation=45, ha='right')
-    ax.grid(axis='y', alpha=0.2)
+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")
+    plt.savefig(buf, format="png", dpi=120, bbox_inches='tight')
     plt.close(fig)
-    html_chart = f'<img src="data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}" style="max-width:100%"/>'
-
-    # Generar informe
+    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:sans-serif; max-width:800px; margin:auto">
-    <h2>🧪 Diagnóstico por múltiples modelos de IA</h2>
-    <table style="width:100%; font-size:16px; border-collapse:collapse">
-      <tr><th>Modelo</th><th>Resultado</th><th>Confianza</th></tr>
-      <tr><td>🧠 ViT base</td><td><b>{class_vit}</b></td><td>{conf_vit:.1%}</td></tr>
-      <tr><td>🧬 Fast.ai (tipo)</td><td><b>{pred_fast_type}</b></td><td>N/A</td></tr>
-      <tr><td>⚠️ Fast.ai (malignidad)</td><td><b>{'Maligno' if prob_malign > 0.5 else 'Benigno'}</b></td><td>{prob_malign:.1%}</td></tr>
-      <tr><td>🌟 ViT fined‑tuned (HAM10000)</td><td><b>{mal_tf} ({class_tf_model})</b></td><td>{conf_tf:.1%}</td></tr>
-      <tr><td>🏥 EfficientNet B7 (binario)</td><td><b>{eff_result}</b></td><td>{prob_eff:.1%}</td></tr>
-    </table><br>
-    <b>🩺 Recomendación automática:</b><br>
+    <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>
     """
-
-    # Nivel de riesgo automático
-    risk = sum(probs_vit[i] * RISK_LEVELS[i]['weight'] for i in range(7))
-    if prob_malign > 0.7 or risk > 0.6 or prob_eff > 0.7:
-        informe += "🚨 <b>CRÍTICO</b> – Derivación urgente a oncología dermatológica"
-    elif prob_malign > 0.4 or risk > 0.4 or prob_eff > 0.5:
-        informe += "⚠️ <b>ALTO RIESGO</b> – Consulta con dermatólogo en 7 días"
-    elif risk > 0.2:
-        informe += "📋 <b>RIESGO MODERADO</b> – Evaluación programada en 2-4 semanas"
+    
+    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 += "✅ <b>BAJO RIESGO</b> – Seguimiento de rutina (3-6 meses)"
-    informe += "</div>"
-
-    return informe, html_chart
+        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_combined,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.HTML(label="Informe"), gr.HTML(label="Gráfico ViT base")],
-    title="Detector de Lesiones Cutáneas (ViT + Fast.ai + EfficientNet)",
+    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__":
-    demo.launch()
-
-
+    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)