Update app.py

app.py

@@ -1,79 +1,85 @@
+import traceback  # Asegúrate de tener esto al inicio de tu script
+
 def analizar_lesion_combined(img):
-    # Convertir imagen para Fastai
-    img_fastai = PILImage.create(img)
+    try:
+        # Convertir imagen para Fastai
+        img_fastai = PILImage.create(img)
+
+        # ViT prediction
+        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]
+        pred_idx_vit = int(np.argmax(probs_vit))
+        pred_class_vit = CLASSES[pred_idx_vit]
+        confidence_vit = probs_vit[pred_idx_vit]
 
-    # ViT prediction
-    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]
-    pred_idx_vit = int(np.argmax(probs_vit))
-    pred_class_vit = CLASSES[pred_idx_vit]
-    confidence_vit = probs_vit[pred_idx_vit]
+        # Fast.ai models
+        pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
+        prob_malignant = float(probs_fast_mal[1])  # índice 1 = maligno
+        pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
 
-    # Fast.ai models
-    pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
-    prob_malignant = float(probs_fast_mal[1])  # índice 1 = maligno
-    pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
+        # Modelo TensorFlow ISIC (usando TFSMLayer)
+        x_isic = preprocess_image_isic(img)
+        preds_isic_dict = model_isic(x_isic)
 
-    # Modelo TensorFlow ISIC (usando TFSMLayer)
-    x_isic = preprocess_image_isic(img)
-    preds_isic_dict = model_isic(x_isic)  # devuelve dict con tensores
+        print("🔍 Claves de salida de model_isic:", preds_isic_dict.keys())
 
-    # DEBUG: imprime claves para saber cuál usar
-    print("Claves de salida de model_isic:", preds_isic_dict.keys())
+        key = list(preds_isic_dict.keys())[0]
+        preds_isic = preds_isic_dict[key].numpy()[0]
+        pred_idx_isic = int(np.argmax(preds_isic))
+        pred_class_isic = CLASSES[pred_idx_isic]
+        confidence_isic = preds_isic[pred_idx_isic]
 
-    # Por ejemplo, si la clave correcta es 'outputs', cámbiala aquí:
-    # Si sólo hay una clave, la usamos directamente:
-    key = list(preds_isic_dict.keys())[0]
-    preds_isic = preds_isic_dict[key].numpy()[0]
+        # Gráfico ViT
+        colors_bars = [RISK_LEVELS[i]['color'] for i in range(7)]
+        fig, ax = plt.subplots(figsize=(8, 3))
+        ax.bar(CLASSES, probs_vit*100, color=colors_bars)
+        ax.set_title("Probabilidad ViT 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)
+        plt.tight_layout()
+        buf = io.BytesIO()
+        plt.savefig(buf, format="png")
+        plt.close(fig)
+        img_bytes = buf.getvalue()
+        img_b64 = base64.b64encode(img_bytes).decode("utf-8")
+        html_chart = f'<img src="data:image/png;base64,{img_b64}" style="max-width:100%"/>'
 
-    pred_idx_isic = int(np.argmax(preds_isic))
-    pred_class_isic = CLASSES[pred_idx_isic]
-    confidence_isic = preds_isic[pred_idx_isic]
+        # Informe HTML con los 4 modelos
+        informe = f"""
+        <div style="font-family:sans-serif; max-width:800px; margin:auto">
+        <h2>🧪 Diagnóstico por 4 modelos de IA</h2>
+        <table style="border-collapse: collapse; width:100%; font-size:16px">
+            <tr><th style="text-align:left">🔍 Modelo</th><th>Resultado</th><th>Confianza</th></tr>
+            <tr><td>🧠 ViT (transformer)</td><td><b>{pred_class_vit}</b></td><td>{confidence_vit:.1%}</td></tr>
+            <tr><td>🧬 Fast.ai (clasificación)</td><td><b>{pred_fast_type}</b></td><td>N/A</td></tr>
+            <tr><td>⚠️ Fast.ai (malignidad)</td><td><b>{"Maligno" if prob_malignant > 0.5 else "Benigno"}</b></td><td>{prob_malignant:.1%}</td></tr>
+            <tr><td>🔬 ISIC TensorFlow</td><td><b>{pred_class_isic}</b></td><td>{confidence_isic:.1%}</td></tr>
+        </table>
+        <br>
+        <b>🩺 Recomendación automática:</b><br> 
+        """
 
-    # Gráfico ViT
-    colors_bars = [RISK_LEVELS[i]['color'] for i in range(7)]
-    fig, ax = plt.subplots(figsize=(8, 3))
-    ax.bar(CLASSES, probs_vit*100, color=colors_bars)
-    ax.set_title("Probabilidad ViT 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)
-    plt.tight_layout()
-    buf = io.BytesIO()
-    plt.savefig(buf, format="png")
-    plt.close(fig)
-    img_bytes = buf.getvalue()
-    img_b64 = base64.b64encode(img_bytes).decode("utf-8")
-    html_chart = f'<img src="data:image/png;base64,{img_b64}" style="max-width:100%"/>'
+        cancer_risk_score = sum(probs_vit[i] * RISK_LEVELS[i]['weight'] for i in range(7))
+        if prob_malignant > 0.7 or cancer_risk_score > 0.6:
+            informe += "🚨 <b>CRÍTICO</b> – Derivación urgente a oncología dermatológica"
+        elif prob_malignant > 0.4 or cancer_risk_score > 0.4:
+            informe += "⚠️ <b>ALTO RIESGO</b> – Consulta con dermatólogo en 7 días"
+        elif cancer_risk_score > 0.2:
+            informe += "📋 <b>RIESGO MODERADO</b> – Evaluación programada (2-4 semanas)"
+        else:
+            informe += "✅ <b>BAJO RIESGO</b> – Seguimiento de rutina (3-6 meses)"
 
-    # Informe HTML con los 4 modelos
-    informe = f"""
-    <div style="font-family:sans-serif; max-width:800px; margin:auto">
-    <h2>🧪 Diagnóstico por 4 modelos de IA</h2>
-    <table style="border-collapse: collapse; width:100%; font-size:16px">
-        <tr><th style="text-align:left">🔍 Modelo</th><th>Resultado</th><th>Confianza</th></tr>
-        <tr><td>🧠 ViT (transformer)</td><td><b>{pred_class_vit}</b></td><td>{confidence_vit:.1%}</td></tr>
-        <tr><td>🧬 Fast.ai (clasificación)</td><td><b>{pred_fast_type}</b></td><td>N/A</td></tr>
-        <tr><td>⚠️ Fast.ai (malignidad)</td><td><b>{"Maligno" if prob_malignant > 0.5 else "Benigno"}</b></td><td>{prob_malignant:.1%}</td></tr>
-        <tr><td>🔬 ISIC TensorFlow</td><td><b>{pred_class_isic}</b></td><td>{confidence_isic:.1%}</td></tr>
-    </table>
-    <br>
-    <b>🩺 Recomendación automática:</b><br> 
-    """
+        informe += "</div>"
 
-    cancer_risk_score = sum(probs_vit[i] * RISK_LEVELS[i]['weight'] for i in range(7))
-    if prob_malignant > 0.7 or cancer_risk_score > 0.6:
-        informe += "🚨 <b>CRÍTICO</b> – Derivación urgente a oncología dermatológica"
-    elif prob_malignant > 0.4 or cancer_risk_score > 0.4:
-        informe += "⚠️ <b>ALTO RIESGO</b> – Consulta con dermatólogo en 7 días"
-    elif cancer_risk_score > 0.2:
-        informe += "📋 <b>RIESGO MODERADO</b> – Evaluación programada (2-4 semanas)"
-    else:
-        informe += "✅ <b>BAJO RIESGO</b> – Seguimiento de rutina (3-6 meses)"
+        return informe, html_chart
 
-    informe += "</div>"
+    except Exception as e:
+        print("🔴 ERROR en analizar_lesion_combined:")
+        print(str(e))
+        traceback.print_exc()
+        return f"<b>Error interno:</b> {str(e)}", ""
 
-    return informe, html_chart