Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -45,7 +45,6 @@ with col_a:
     ultra_flag = False
     if ultra_button:
         ultra_flag = True
-        
     
     # Create a dropdown menu to select the model
     model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys())
@@ -67,36 +66,27 @@ 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)
-    
-    
     # Set the image dimensions
     IMAGE_WIDTH = IMAGE_HEIGHT = 224
 
     uploaded_file = st.file_uploader(label = '',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
-
+    executed = False
+    
 with col_b:
     if st.button('¿Hay un patacón en la imagen?'):
         if len(selected_models) > 0 and ultra_flag:
             st.write('Debe elegir un solo método: Ultra-Patacotrón o selección múltiple.')
         elif uploaded_file is not None:
-            # Load the image and resize it to the required dimensions
-            #img = tf.io.read_file(uploaded_file)
+
             raw_img = tf.image.decode_image(uploaded_file.read(), channels=3)
             img = tf.image.resize(raw_img,(IMAGE_WIDTH, IMAGE_HEIGHT))
-
-            #img = np.frombuffer(uploaded_file.read(), np.uint8)
-            #img = cv2.imdecode(img, cv2.IMREAD_COLOR)
-            #raw_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            #img = cv2.resize(img, (IMAGE_WIDTH, IMAGE_HEIGHT))
-        
-            # Convert the image to RGB and preprocess it for the model
-            #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)            
+          
             # Pass the image to the model and get the prediction
             if ultra_flag:
                 with st.spinner('Cargando ultra-predicción...'):
-                    ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
+                    if not executed: 
+                        ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
+                        executed = True
                     y_gorrito = predict(ultraptctrn, img)
             else:
                 with st.spinner('Cargando predicción...'):