app.py

import traceback  # Asegúrate de tener esto al inicio de tu script

def analizar_lesion_combined(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]

        # 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)

        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]

        # 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%"/>'

        # 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> 
        """

        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 += "</div>"

        return informe, html_chart

    except Exception as e:
        print("🔴 ERROR en analizar_lesion_combined:")
        print(str(e))
        traceback.print_exc()
        return f"<b>Error interno:</b> {str(e)}", ""