Update app.py

app.py

@@ -115,10 +115,10 @@ def main_func(inputs):
         final_mask = masks[0]
         mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
         mask_colors[final_mask, :] = np.array([[256, 0, 0]])
-        return Image.fromarray((mask_colors+ image_input).astype('uint8'), 'RGB')
+        return 'Prediction: Vehicle damage prediction is given.',Image.fromarray((mask_colors+ image_input).astype('uint8'), 'RGB')
     else:
         print('Prediction:: No vehicle found in the image')
-        return Image.fromarray(image_input)
+        return 'Prediction:: No vehicle or damage found in the image',Image.fromarray(image_input)
 
 def reset_data():
     global cache_data
@@ -128,12 +128,15 @@ with gr.Blocks() as demo:
     gr.Markdown("# Vehicle damage detection")
     gr.Markdown("""This app uses the SAM model and clipseg model to get a vehicle damage area from image.""")
     with gr.Row():
-        image_input = gr.Image()
-        image_output = gr.Image()
+        image_input = gr.Image(label='Input Image')
+        image_output = gr.Image(label='Damage Detection')
+    with gr.Row():
+        examples = gr.Examples(examples="./examples", inputs=image_input)
+        prediction_op = gr.gradio.Textbox(label='Prediction')
     
     image_button = gr.Button("Segment Image", variant='primary')
 
-    image_button.click(main_func, inputs=image_input, outputs=image_output)
+    image_button.click(main_func, inputs=image_input, outputs=[prediction_op, image_output])
     image_input.upload(reset_data)
 
 demo.launch()