Update app.py

app.py

@@ -14,33 +14,31 @@ st.markdown('You can click "Browse files" multiple times until adding all images
 
 #uploaded_file = st.file_uploader("Upload an image file", type="jpg", accept_multiple_files=True)
 
-imageContainer = st.empty()
-uploaded_file = imageContainer.file_uploader("Upload an image file", type="jpg", accept_multiple_files=True)
-imageContainer.image(uploaded_file, width=100)
+#imageContainer = st.empty()
 
-closeImage = st.button("clear all images")
+#closeImage = st.button("clear all images")
+with st.form("list", clear_on_submit=True):
+    uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
+    submitted = st.form_submit_button("submit")
+    st.image(uploaded_file, width=100)
 
 img_height = 224
 img_width = 224
 class_names = ['Palm', 'Others']
 
 model = tf.keras.models.load_model('model')
-Generate_pred = st.button("Generate Prediction")
         
 
 if uploaded_file is not None:
-    if Generate_pred:
-        for file in uploaded_file:
-            img = Image.open(file)
-            img_array = img_to_array(img)
-            img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
-            processed_image = preprocess_input(img_array)
-            
-            predictions = model.predict(processed_image)
-            score = predictions[0]
-            st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
+    Generate_pred = st.button("Generate Prediction")
+        if Generate_pred:
+            for file in uploaded_file:
+                img = Image.open(file)
+                img_array = img_to_array(img)
+                img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
+                processed_image = preprocess_input(img_array)
+                
+                predictions = model.predict(processed_image)
+                score = predictions[0]
+                st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
     
-if closeImage:
-    imageContainer.empty()
-    if uploaded_file is not None:
-        del uploaded_file