Update app.py

app.py

@@ -555,20 +555,12 @@ def update_interface(enable_interference):
         }
 
 with gr.Blocks() as demo:
-    gr.Markdown("""
+    gr.Markdown(
+    """
     ## Fast Indoor Radio Propagation Prediction using Deep Learning
     This app uses deep learning models for radio map estimation (RME) with and without interference, simulating 2.4 GHz and 5 GHz bands. RME involves estimating the received RF power based on spatial information maps.
-
-    Instructions for use:
-
-    - A predefined list of indoor floor plans is available for use.
-    - You can upload your own indoor floor plan.
-    - Negative numbers are not allowed.
-    - The established format for the coordinates of each access point (AP) must be followed.
-    - A maximum of 2 APs per channel is allowed for the interference case.
-    - A maximum of 5 APs is allowed for the non-interference case.
-    - The uploaded plan must meet the dimensions [256,256,3], with free spaces as white pixels and walls as black pixels.
-    """)
+    """
+    )
 
     enable_interference = gr.Checkbox(label="Enable Interference Analysis", value=True)
 
@@ -627,4 +619,18 @@ with gr.Blocks() as demo:
                           coords_ch1_input, coords_ch6_input, coords_ch11_input],
                   outputs=[image_ch1, image_ch6, image_ch11, image_ap1, image_ap2, image_cover_final, image_cells])
 
+    gr.Markdown(
+    """
+    Instructions for use:
+
+    - A predefined list of indoor floor plans is available for use.
+    - You can upload your own indoor floor plan.
+    - Negative numbers are not allowed.
+    - The established format for the coordinates of each access point (AP) must be followed.
+    - A maximum of 2 APs per channel is allowed for the interference case.
+    - A maximum of 5 APs is allowed for the non-interference case.
+    - The uploaded plan must meet the dimensions [256,256,3], with free spaces as white pixels and walls as black pixels.
+    """
+    )
+
 demo.launch()