Update app.py

app.py

@@ -12,27 +12,23 @@ import os
 import traceback
 
 # Descomprimir el modelo si no se ha descomprimido aún
-if not os.path.exists("saved_model.pb"):
+if not os.path.exists("saved_model"):
     with zipfile.ZipFile("saved_model.zip", "r") as zip_ref:
-        zip_ref.extractall(".")
+        zip_ref.extractall("saved_model")
 
 # Cargar modelo ISIC con TensorFlow desde el directorio correcto
 from keras.layers import TFSMLayer
 
 try:
-    model_isic = TFSMLayer(".", call_endpoint="serving_default")
+    model_isic = TFSMLayer("saved_model/saved_model", call_endpoint="serving_default")
 except Exception as e:
     print("\U0001F534 Error al cargar el modelo ISIC con TFSMLayer:", e)
     raise
 
-# Verificación de archivos FastAI
+# Cargar modelos fastai
 model_malignancy = load_learner("ada_learn_malben.pkl")
 model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")
 
-# Cargar modelos fastai
-model_malignancy = load_learner("modelo_malignancy.pkl")
-model_norm2000 = load_learner("modelo_norm2000.pkl")
-
 # Cargar modelo ViT
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 feature_extractor = AutoImageProcessor.from_pretrained("nateraw/vit-skin-cancer")
@@ -56,7 +52,7 @@ def preprocess_image_isic(pil_image):
     array = np.array(image) / 255.0
     return np.expand_dims(array, axis=0)
 
-# Función de análisis (como ya la tienes)
+# Función de análisis
 def analizar_lesion_combined(img):
     try:
         img_fastai = PILImage.create(img)
@@ -74,6 +70,7 @@ def analizar_lesion_combined(img):
 
         x_isic = preprocess_image_isic(img)
         preds_isic_dict = model_isic(x_isic)
+        print("\U0001F50D 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))
@@ -104,7 +101,7 @@ def analizar_lesion_combined(img):
             <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>"""
+        </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:
@@ -120,7 +117,7 @@ def analizar_lesion_combined(img):
         return informe, html_chart
 
     except Exception as e:
-        print("🔴 ERROR en analizar_lesion_combined:")
+        print("\U0001F534 ERROR en analizar_lesion_combined:")
         print(str(e))
         traceback.print_exc()
         return f"<b>Error interno:</b> {str(e)}", ""
@@ -138,3 +135,4 @@ demo = gr.Interface(
 # LANZAMIENTO
 if __name__ == "__main__":
     demo.launch()
+