Update app.py

app.py

@@ -1,5 +1,3 @@
-# app.py
-
 import gradio as gr
 import torch
 import numpy as np
@@ -14,15 +12,15 @@ import os
 import traceback
 
 # Descomprimir el modelo si no se ha descomprimido aún
-if not os.path.exists("saved_model"):
+if not os.path.exists("saved_model.pb"):
     with zipfile.ZipFile("saved_model.zip", "r") as zip_ref:
-        zip_ref.extractall("saved_model")
+        zip_ref.extractall(".")
 
 # Cargar modelo ISIC con TensorFlow desde el directorio correcto
 from keras.layers import TFSMLayer
 
 try:
-    model_isic = TFSMLayer("saved_model/saved_model", call_endpoint="serving_default")
+    model_isic = TFSMLayer(".", call_endpoint="serving_default")
 except Exception as e:
     print("\U0001F534 Error al cargar el modelo ISIC con TFSMLayer:", e)
     raise
@@ -103,7 +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:
@@ -138,3 +136,4 @@ demo = gr.Interface(
 if __name__ == "__main__":
     demo.launch()
 
+