Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -15,92 +15,87 @@ st.set_page_config(
     }
 )
 
+col_a, col_b, = st.columns(2)
 
-st.title("Entorno de ejecución")
-st.markdown("Los modelos no están en orden de eficacia, sino en orden de creación. En la pestaña de Estadísticas podrá encontrar más información.")
+with col_a:
 
-# Get the absolute path to the current directory
-current_dir = os.path.abspath(os.path.dirname(__file__))
-
-# Get the absolute path to the parent directory of the current directory
-root_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
-
-# Join the path to the models folder
-DIR = os.path.join(root_dir, "models")
-
-threshold = .8
-
-ultra_button = st.checkbox('ultraptctrn: el mejor ensamble de modelos hasta la fecha.')
-ultra_flag = False
-if ultra_button:
-    ultra_flag = True
-
-models = os.listdir(DIR)
-
-model_dict = dict()
-for model in models:
-    model_name = model.split(DIR)
-    model_name = str(model.split('.h5')[0])
-    model_dir = os.path.join(DIR, model)
-    model_dict[model_name] = model_dir
+    st.title("Entorno de ejecución")
+    st.markdown("Los modelos no están en orden de eficacia, sino en orden de creación. En la pestaña de Estadísticas podrá encontrar más información.")
     
-ultraptctrn = ['ptctrn_v1.8', 'ptctrn_v1.9.1', 'ptctrn_v1.12']
-
-# Create a dropdown menu to select the model
-model_choice = st.multiselect("Seleccione un modelo de clasificación", model_dict.keys())
-
-selected_models = []
-
-def ensemble_model(model_list, img):
-    y_gorrito = np.zeros((1, 1))
-    for model in model_list:
-        instance_model = load_model(model_dict[model])
-        y_gorrito += float(instance_model.predict(np.expand_dims(img, 0)))
-        clear_session()
-    return y_gorrito/len(model_list)
-
-for model in model_choice:
-    selected_models.append(model)
-
-# Set the image dimensions
-IMAGE_WIDTH = IMAGE_HEIGHT = 224
-
-# Create a file uploader widget
-uploaded_file = st.file_uploader("Elige una imagen...", type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
-
-if uploaded_file is not None:
-    # Load the image and resize it to the required dimensions
-    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)
-    img = img / 255.
+    # Get the absolute path to the current directory
+    current_dir = os.path.abspath(os.path.dirname(__file__))
+    
+    # Get the absolute path to the parent directory of the current directory
+    root_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
+    
+    # Join the path to the models folder
+    DIR = os.path.join(root_dir, "models")
+    
+    threshold = .8
+    
+    ultra_button = st.checkbox('ultraptctrn: el mejor ensamble de modelos hasta la fecha.')
+    ultra_flag = False
+    if ultra_button:
+        ultra_flag = True
+    
+    models = os.listdir(DIR)
+    
+    model_dict = dict()
+    for model in models:
+        model_name = model.split(DIR)
+        model_name = str(model.split('.h5')[0])
+        model_dir = os.path.join(DIR, model)
+        model_dict[model_name] = model_dir
+        
+    ultraptctrn = ['ptctrn_v1.8', 'ptctrn_v1.9.1', 'ptctrn_v1.12']
+    
+    # Create a dropdown menu to select the model
+    model_choice = st.multiselect("Seleccione un modelo de clasificación", model_dict.keys())
 
-    # Pass the image to the model and get the prediction
-    if ultra_flag:
-        with st.spinner('Cargando ultra-predicción...'):
-            y_gorrito = ensemble_model(ultraptctrn, img)
-    else:
-        with st.spinner('Cargando predicción...'):
-            y_gorrito = ensemble_model(selected_models, img)
+    selected_models = []
+    
+    def ensemble_model(model_list, img):
+        y_gorrito = np.zeros((1, 1))
+        for model in model_list:
+            instance_model = load_model(model_dict[model])
+            y_gorrito += float(instance_model.predict(np.expand_dims(img, 0)))
+            clear_session()
+        return y_gorrito/len(model_list)
+    
+    for model in model_choice:
+        selected_models.append(model)
+    
+    # Set the image dimensions
+    IMAGE_WIDTH = IMAGE_HEIGHT = 224
+    
+    # Create a file uploader widget
+    uploaded_file = st.file_uploader("Elige una imagen...", type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
 
-    # Show the image
-    col1, col2, col3 = st.columns(3)
+with col_b:
 
-    with col1:
-        st.write(' ')
+    if uploaded_file is not None:
+        # Load the image and resize it to the required dimensions
+        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)
+        img = img / 255.
+    
+        # Pass the image to the model and get the prediction
+        if ultra_flag:
+            with st.spinner('Cargando ultra-predicción...'):
+                y_gorrito = ensemble_model(ultraptctrn, img)
+        else:
+            with st.spinner('Cargando predicción...'):
+                y_gorrito = ensemble_model(selected_models, img)
 
-    with col2:
         if y_gorrito > threshold:
             st.success("¡Patacón Detectado!")
         else:
             st.error("No se encontró rastro de patacón.")
         st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito), 2)*100}%')
         #st.write('Si los resultados no fueron los esperados, por favor, despliga la barra lateral y entra al botón "Report a Bug"')
-        st.image(raw_img)
-
-    with col3:
-        st.write(' ')
+        st.image(raw_img)