Update app.py

app.py

@@ -4,6 +4,8 @@ from PIL import Image
 import tensorflow as tf
 from utils import preprocess_image
 
+
+
 # Initialize labels and model
 labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
 model = tf.keras.models.load_model('classify_model.h5')
@@ -96,39 +98,12 @@ if opt == 'Upload image from device':
     if file:
         image = preprocess_image(file)
 
-# Sidebar section
-st.sidebar.title("Options")
-
-# Create a radio button widget for training mode
-training_mode = st.sidebar.radio("Select training mode", ["None", "Dropout", "Batch Normalization"])
-
-if training_mode == 'None':
-    user_choice = 'Predict'
-elif training_mode == "Dropout" or "Batch Normalization":
-    user_choice = 'Train'
-
-
-# Display the current training mode and user choice
-st.write(f"Training mode: {training_mode}")
-st.write(f"User choice: {user_choice}")
-
 try:
     if image is not None:
         st.image(image, width=256, caption='Uploaded Image')
-        
-        # Execute different code blocks based on user choice
-        if user_choice == "Predict":
-            # Call the model with the training mode argument
-            # Use a dictionary to map the training mode to the corresponding boolean value
-            prediction = model.predict(image[np.newaxis, ...], training={"None": False, "Dropout": True, "Batch Normalization": True}[training_mode])
+        if st.button('Predict'):
+            prediction = model.predict(image[np.newaxis, ...])
             st.success(f'Prediction: {labels[np.argmax(prediction[0], axis=-1)]}')
-        elif user_choice == "Train":
-            # Generate some dummy target data for demonstration
-            # You can replace this with your actual target data
-            target = np.random.randint(0, 6, size=(1,))
-            # Call the model without the training argument
-            loss = model.train_on_batch(image[np.newaxis, ...], target)
-            st.success(f'Loss: {loss}')
-
 except Exception as e:
     st.error(f"An error occurred: {e}.  Please contact us EcoClim Solutions at EcoClimSolutions.wordpress.com.")
+