Update app.py

app.py

@@ -48,7 +48,7 @@ def dice_coef(y_true, y_pred, smooth=1):
 seg_model = tf.keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
 
 inputs = gr.inputs.Image(type="pil", label="Upload an image")
-image_output = gr.outputs.Image(type="numpy", label="Output Image")
+image_output = gr.outputs.Image(type="pil", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
 
@@ -67,7 +67,7 @@ def gen_pred(img=inputs, model=seg_model):
     nparr = np.frombuffer(img_bytes, np.byte)
     pred_pil = cv2.imdecode(nparr, cv2.IMREAD_REDUCED_COLOR_8)
     # return "UI in developing process ..."
-    return nparr
+    return pred_pil
 
 title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
 description = "Upload an image and get prediction mask"
@@ -75,7 +75,7 @@ description = "Upload an image and get prediction mask"
 
 gr.Interface(fn=gen_pred, 
              inputs=inputs, 
-             outputs=image_output, 
+             outputs='image', 
              title=title, 
              examples=[["003e2c95d.jpg"], ["003b50a15.jpg"], ["003b48a9e.jpg"], ["0038cbe45.jpg"], ["00371aa92.jpg"]],
              description=description,