Update app file

app.py

@@ -21,7 +21,7 @@ st.title('P&ID Object Detection')
 st.subheader(' Identify valves and pumps with deep learning model ', divider='rainbow')
 st.caption('Developed by Deep Drawings Co.')
 
-col1, col2, col3 = st.columns(3, gap='medium')
+col1, col2, col3 = st.columns(3, gap='large')
 with col1:
     with st.expander('How to use it'):
         st.markdown(
@@ -35,8 +35,8 @@ with col1:
 
 st.write('##')
    
-col1, col2, col3, col4 = st.columns([1, 2, 2, 1], gap='large')
-with col2:
+col1, col2, col3 = st.columns(3, gap='large')
+with col1:
     st.markdown('##### Input File')
     # set input image by upload
     image_file = st.file_uploader("Upload your diagram", type=["pdf"])
@@ -53,7 +53,7 @@ with col2:
         options = ['factory_pid.png', 'plant_pid.png', 'processing_pid.png'],
         format_func = radio_func,
     )
-with col3:
+with col2:
     st.markdown('##### Preview')
     # visualize input image
     if image_file is not None:
@@ -62,11 +62,8 @@ with col3:
         image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[radio])
     with st.container(border = True):
         st.image(image, use_column_width = True)
-
-st.write('##')
-
-col1, col2, col3 = st.columns(3, gap='medium')
-with col2:
+        
+with col3:
     st.markdown('#### Set model parameters')
     postprocess_match_threshold = st.slider(
         label = 'Select confidence threshold',
@@ -74,4 +71,4 @@ with col2:
         max_value = 1.0,
         value = 0.8,
         step = 0.05
-    )
+    )