Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -60,16 +60,7 @@ with cnn:
         
         selected_models = []
     
-        @tf.function
-        def tf_predict(model_list, weights, img): #faster, but for few formats
-            y_gorrito = 0
-            raw_img = tf.image.decode_image(img, channels=3)
-            img = tf.image.resize(raw_img,(IMAGE_WIDTH, IMAGE_HEIGHT))
-            for model, weight in zip(model_list, weights):
-                y_gorrito += tf.cast(model(tf.expand_dims(img/255., 0)), dtype=tf.float32)*weight
-            return [y_gorrito / sum(weights), raw_img.numpy()]
-
-        def basic_predict(model_list, weights, img): #for non-supported formats
+        def predict(model_list, weights, img): #for non-supported formats
             y_gorrito = 0
             raw_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
             img = cv2.resize(img, (IMAGE_WIDTH, IMAGE_HEIGHT))
@@ -93,7 +84,7 @@ with cnn:
         
         if st.button('¿Hay un patacón en la imagen?'):
             if len(model_choice) > 0 and ultra_flag:
-                st.write('Debe elegir un solo método: Ultra-Patacotrón o selección múltiple.')
+                st.write('Debe elegir un solo método: Ultra-Patacotrón o Selección Múltiple.')
             
             elif uploaded_file is not None:
                 img = preprocess(uploaded_file)
@@ -103,27 +94,21 @@ with cnn:
                             ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
                             executed = True
                         final_weights = weight_list if len(weight_list) >= 1 else [1 for i in range(len(ultraptctrn))]
-                        try:
-                            y_gorrito, raw_img = tf_predict(ultraptctrn, final_weights, img)
-                        except:
-                            y_gorrito, raw_img = basic_predict(ultraptctrn, final_weights, img)
+                        y_gorrito, raw_img = predict(ultraptctrn, final_weights, img)
                         
                 else:
                     with st.spinner('Cargando predicción...'):
                         selected_models = [load_model(model_dict[model]) for model in model_choice if model not in selected_models]
-                        final_weights = weight_list if len(weight_list) >= 1 else [1 for i in range(len(selected_models))]
-                        try:             
-                            y_gorrito, raw_img = tf_predict(selected_models, final_weights, img)
-                        except:
-                            y_gorrito, raw_img = basic_predict(selected_models, final_weights, img)
-        
+                        final_weights = weight_list if len(weight_list) >= 1 else [1 for i in range(len(selected_models))]       
+                        y_gorrito, raw_img = predict(selected_models, final_weights, img)
+
                 if round(float(y_gorrito*100)) >= threshold:
                     st.success("¡Patacón Detectado!")
                 
                 else:
                     st.error("No se considera que haya un patacón en la imagen")
-                    
-                st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%')
+                if not ultra_flag:
+                    st.caption(f'La probabilidad de que la imagen tenga un patacón es del: {round(float(y_gorrito * 100), 2)}%')
                 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)')
                 
                 st.image(raw_img)