Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -45,7 +45,7 @@ with col_a:
     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())
@@ -69,7 +69,7 @@ with col_a:
     
     #for model in model_choice:
         #selected_models.append(model)
-    selected_models = [load_model(model_dict[model]) for model in model_choice]
+    
     
     # Set the image dimensions
     IMAGE_WIDTH = IMAGE_HEIGHT = 224
@@ -80,7 +80,7 @@ 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 and len(selected_models) > 0 or ultra_flag:
+        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)
@@ -92,14 +92,15 @@ with col_b:
             #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)
-        
+            #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]
                     y_gorrito = predict(ultraptctrn, img)
             else:
                 with st.spinner('Cargando predicción...'):
+                    selected_models = [load_model(model_dict[model]) for model in model_choice]
                     y_gorrito = predict(selected_models, img)
     
             if y_gorrito > threshold: