test loading for image

app.py

@@ -274,7 +274,7 @@ def main():
     st.image("./images/banner.png", use_column_width=True)
 
     # Sidebar content
-    st.sidebar.header("This BPMN model recognition by AI is proposed by ELCA in collaboration with EPFL.")
+    st.sidebar.header("This BPMN AI model recognition is proposed by ELCA in collaboration with EPFL.")
     st.sidebar.subheader("Instructions:")
     st.sidebar.text("1. Upload you image")
     st.sidebar.text("2. Crop the image \n  (try to put the BPMN diagram \n   in the center of the image)")
@@ -282,7 +282,7 @@ def main():
     st.sidebar.text("4. Set the scale for the XML file \n  (default is 1.0)")
     st.sidebar.text("5. Click on 'Launch Prediction'")
 
-    st.sidebar.subheader("You can close the sidebar")
+    st.sidebar.subheader("You can close this sidebar")
 
     # Set the title of the app
     st.title("BPMN model recognition demo")
@@ -311,22 +311,23 @@ def main():
 
     # Display the uploaded image if the user has uploaded an image
     if uploaded_file is not None:
-        original_image = get_image(uploaded_file)
-        col1, col2 = st.columns(2)
-
-        # Create a cropper to allow the user to crop the image and display the cropped image
-        with col1:           
-            cropped_image = st_cropper(original_image, realtime_update=True, box_color='#0000FF', should_resize_image=True, default_coords=(30, original_image.size[0]-30, 30, original_image.size[1]-30))
-        with col2:
-            st.image(cropped_image, caption="Cropped Image", use_column_width=False, width=500)
-            
-        # Display the options for the user to set the score threshold and scale
-        if cropped_image is not None:
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                score_threshold = st.slider("Set score threshold for prediction", min_value=0.0, max_value=1.0, value=0.5, step=0.05)
+        with st.spinner('wait for image...'): 
+            original_image = get_image(uploaded_file)
+            col1, col2 = st.columns(2)
+
+            # Create a cropper to allow the user to crop the image and display the cropped image
+            with col1:           
+                cropped_image = st_cropper(original_image, realtime_update=True, box_color='#0000FF', should_resize_image=True, default_coords=(30, original_image.size[0]-30, 30, original_image.size[1]-30))
             with col2:
-                st.session_state.scale = st.slider("Set scale for XML file", min_value=0.1, max_value=2.0, value=1.0, step=0.1)
+                st.image(cropped_image, caption="Cropped Image", use_column_width=False, width=500)
+                
+            # Display the options for the user to set the score threshold and scale
+            if cropped_image is not None:
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    score_threshold = st.slider("Set score threshold for prediction", min_value=0.0, max_value=1.0, value=0.5, step=0.05)
+                with col2:
+                    st.session_state.scale = st.slider("Set scale for XML file", min_value=0.1, max_value=2.0, value=1.0, step=0.1)
 
             # Launch the prediction when the user clicks the button    
             if st.button("Launch Prediction"):