Update app.py

app.py

@@ -75,7 +75,8 @@ def predict(image, model_name: str = "detr",) -> Tuple[Dict, float]:
         # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
         panoptic_seg = result[0]["segmentation"]
         # Convert the tensor to PIL image
-        plt.plot("predicted_panoptic_map.png", panoptic_seg, cmap="viridis")
+        plt.plot(panoptic_seg, cmap="viridis")
+        # plt.imsave("predicted_panoptic_map.png", panoptic_seg, cmap="viridis")
         fig = plt.gcf()
         output = fig2img(fig)
         # output = PIL.Image.open("predicted_panoptic_map.png")
@@ -94,7 +95,8 @@ def predict(image, model_name: str = "detr",) -> Tuple[Dict, float]:
         result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
         # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
         predicted_panoptic_map = result["segmentation"]
-        plt.plot("predicted_panoptic_map.png", predicted_panoptic_map, cmap="viridis")
+        plt.plot(predicted_panoptic_map, cmap="viridis")
+        # plt.imsave("predicted_panoptic_map.png", predicted_panoptic_map, cmap="viridis")
         fig = plt.gcf()
         output = fig2img(fig)
         # output = PIL.Image.open("predicted_panoptic_map.png")