Update app.py

app.py

@@ -102,12 +102,7 @@ def prediction(path_image):
 
 
         return fig
-    inputs = gr.inputs.Image(type = 'filepath')
-    EXAMPLES = ["img1.jpg","img2.jpg","img3.jpg","img4.jpg"]
-    DESCRIPTION = """An image is occluded if the image is blocked by any object. 
-    For example if an electric pole is filled with bushes,the image is occluded since it is not clear and blocked.
-    Mobil-net is used to train a model with occluded and non occluded images, so that it can correctly classify the images.
-    Occlusion detection can be used to filter unclear images and take safety measures."""
+    
     
         
     image_path = inputs
@@ -136,7 +131,12 @@ def prediction(path_image):
     print("total time : ",round((total_time_end-total_time_start),2))
     st.write(str(simplejson.dumps(response)))
 
-
+inputs = gr.inputs.Image(type = 'filepath')
+EXAMPLES = ["img1.jpg","img2.jpg","img3.jpg","img4.jpg"]
+DESCRIPTION = """An image is occluded if the image is blocked by any object. 
+    For example if an electric pole is filled with bushes,the image is occluded since it is not clear and blocked.
+    Mobil-net is used to train a model with occluded and non occluded images, so that it can correctly classify the images.
+    Occlusion detection can be used to filter unclear images and take safety measures."""
 
 demo_app = gr.Interface(
     fn= prediction,