Update app.py

app.py

@@ -17,28 +17,27 @@ st.markdown('You can click "Browse files" multiple times until adding all images
 #imageContainer = st.empty()
 
 #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')
-        
 
-if uploaded_file is not None:
-    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)]))
+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("clear")
+    st.image(uploaded_file, width=100)
+
+    if uploaded_file is not None:
+        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)]))