Update app.py

app.py

@@ -23,30 +23,25 @@ class_names = ['Palm', 'Others']
 model = tf.keras.models.load_model('model')
 
 #Generate_pred = st.button("Generate Prediction")
-uploaded_file = None
-while (uploaded_file is not None):
-    st.image(uploaded_file, width=100)
-    with st.form("form", clear_on_submit=True):
-        uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
-        if uploaded_file is not None:
-            st.image(uploaded_file, width=100)
+with st.form("form", clear_on_submit=True):
+    uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
+    if uploaded_file is not None:
+        st.image(uploaded_file, width=100)
         
-        submitted = st.form_submit_button("Toggle here to predict or to delete the data")
-            
-        if submitted and uploaded_file is not None:
-            
+    submitted = st.form_submit_button("Toggle here to predict or to delete the data")
             
+    #    if submitted and uploaded_file is not None:
     
-            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)
+#            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)]))
+#                predictions = model.predict(processed_image)
+#                score = predictions[0]
+#                st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
     
-            uploaded_file = None
+#            uploaded_file = None