Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -31,11 +31,6 @@ with col_a:
     
     threshold = .8
     
-    ultra_button = st.checkbox('Ultra-Patacotrón (mejores resultados)')
-    ultra_flag = False
-    if ultra_button:
-        ultra_flag = True
-    
     models = os.listdir(DIR)
     
     model_dict = dict()
@@ -44,13 +39,18 @@ with col_a:
         model_name = str(model.split('.h5')[0])
         model_dir = os.path.join(DIR, model)
         model_dict[model_name] = model_dir
-        
-    ultraptctrn = ['ptctrn_v1.4', 'ptctrn_v1.5', 'ptctrn_v1.6', 'ptctrn_v1.12']
+
+    ultraversions = ['ptctrn_v1.4', 'ptctrn_v1.5', 'ptctrn_v1.6', 'ptctrn_v1.12']
+    ultra_button = st.checkbox('Ultra-Patacotrón (mejores resultados)')
+    ultra_flag = False
+    if ultra_button:
+        ultra_flag = True
+        ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
     
     # Create a dropdown menu to select the model
     model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys())
 
-    selected_models = []
+    #selected_models = []
     
     def ensemble_model(model_list, img):
         y_gorrito = np.zeros((1, 1))
@@ -67,8 +67,9 @@ with col_a:
             y_gorrito += tf.cast(model(tf.expand_dims(img/255., 0)), dtype=tf.float32)
         return y_gorrito / len(model_list)
     
-    for model in model_choice:
-        selected_models.append(model)
+    #for model in model_choice:
+        #selected_models.append(model)
+    selected_models = [load_model(model) for model in model_choice]
     
     # Set the image dimensions
     IMAGE_WIDTH = IMAGE_HEIGHT = 224