Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -12,91 +12,107 @@ st.set_page_config(
     }
 )
 
-col_a, col_b, = st.columns(2)
+st.title("Entorno de ejecución")
+cnn, autoencoder, svm, iforest, gan, docc = st.tabs(["Redes neuronales convolucionales clásicas", "Autoencoders", "Máquina de vectores de soporte", 'Bosques de aislamiento', 'Redes generativas adversariales', 'Clasificación de una clase profunda (DOCC)'])
 
-with col_a:
-    st.title("Entorno de ejecución")
-    st.caption("Los modelos no están en orden de eficacia, sino en orden de creación.")
-    
-    # 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 = .75
-    
-    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
+with cnn:
 
-    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
+    col_a, col_b, = st.columns(2)
     
-    # Create a dropdown menu to select the model
-    model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys())
-
-    selected_models = []
+    with col_a:
+        st.title("Redes neuronales convolucionales clásicas")
+        st.caption("Los modelos no están en orden de eficacia, sino en orden de creación.")
+        
+        # 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 = .75
+        
+        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
     
-    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/255., 0)))
-            #clear_session()
-        return y_gorrito/len(model_list)
-
-    @tf.function
-    def predict(model_list, img):
-        y_gorrito = 0
-        for model in model_list:
-            y_gorrito += tf.cast(model(tf.expand_dims(img/255., 0)), dtype=tf.float32)
-        return y_gorrito / len(model_list)
+        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
+        
+        # Create a dropdown menu to select the model
+        model_choice = st.multiselect("Seleccione uno o varios modelos de clasificación", model_dict.keys())
     
-    # Set the image dimensions
-    IMAGE_WIDTH = IMAGE_HEIGHT = 224
-
-    uploaded_file = st.file_uploader(label = '',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
-    executed = False
+        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/255., 0)))
+                #clear_session()
+            return y_gorrito/len(model_list)
     
-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:
-
-            raw_img = tf.image.decode_image(uploaded_file.read(), channels=3)
-            img = tf.image.resize(raw_img,(IMAGE_WIDTH, IMAGE_HEIGHT))
-          
-            # Pass the image to the model and get the prediction
-            if ultra_flag:
-                with st.spinner('Cargando ultra-predicción...'):
-                    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...'):
-                    selected_models = [load_model(model_dict[model]) for model in model_choice]
-                    y_gorrito = predict(selected_models, img)
+        @tf.function
+        def predict(model_list, img):
+            y_gorrito = 0
+            for model in model_list:
+                y_gorrito += tf.cast(model(tf.expand_dims(img/255., 0)), dtype=tf.float32)
+            return y_gorrito / len(model_list)
+        
+        # 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:
     
-            if y_gorrito >= threshold:
-                st.success("¡Patacón Detectado!")
+                raw_img = tf.image.decode_image(uploaded_file.read(), channels=3)
+                img = tf.image.resize(raw_img,(IMAGE_WIDTH, IMAGE_HEIGHT))
+              
+                # Pass the image to the model and get the prediction
+                if ultra_flag:
+                    with st.spinner('Cargando ultra-predicción...'):
+                        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...'):
+                        selected_models = [load_model(model_dict[model]) for model in model_choice]
+                        y_gorrito = predict(selected_models, img)
+        
+                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.image(raw_img.numpy())
+                st.caption('Si los resultados no fueron los esperados, por favor, [haz click aquí](https://docs.google.com/forms/d/e/1FAIpQLScH0ZxAV8aSqs7TPYi86u0nkxvQG3iuHCStWNB-BoQnSW2V0g/viewform?usp=sf_link)')
             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.image(raw_img.numpy())
-            st.caption('Si los resultados no fueron los esperados, por favor, [haz click aquí](https://docs.google.com/forms/d/e/1FAIpQLScH0ZxAV8aSqs7TPYi86u0nkxvQG3iuHCStWNB-BoQnSW2V0g/viewform?usp=sf_link)')
-        else:
-            st.write('Revisa haber seleccionado los modelos y la imagen correctamente.')
+                st.write('Revisa haber seleccionado los modelos y la imagen correctamente.')
+
+    with autoencoder:
+        st.write('Próximamente')
+    with svm:
+        st.write('Próximamente')
+    with iforest:
+        st.write('Próximamente')
+    with gan:
+        st.write('Próximamente')
+    with docc:
+        st.write('Próximamente')