Update app.py

app.py

@@ -57,13 +57,14 @@ def inference(img):
     return Image.fromarray(np.uint8(panoptic_result)).convert('RGB'),Image.fromarray(np.uint8(instance_result)).convert('RGB'),Image.fromarray(np.uint8(semantic_result)).convert('RGB')
     
 
-title = "Detectron 2"
-description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' target='_blank'>Github Repo</a></p>"
+title = "Mask2Former"
+description = "Gradio demo for Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.01527' target='_blank'>Masked-attention Mask Transformer for Universal Image Segmentation</a> | <a href='https://github.com/facebookresearch/Mask2Former' target='_blank'>Github Repo</a></p>"
 
 examples = [['Vj17K5z.jpg']]
 
-gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=[gr.outputs.Image(label="Panoptic segmentation",type="pil"),gr.outputs.Image(label="instance segmentation",type="pil"),gr.outputs.Image(label="semantic segmentation",type="pil")],enable_queue=True, title=title,
+gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=[gr.outputs.Image(label="Panoptic segmentation",type="pil"),gr.outputs.Image(label="instance segmentation",type="pil"),gr.outputs.Image(label="semantic segmentation",type="pil")], title=title,
     description=description,
     article=article,
-    examples=examples).launch()
+    examples=examples).launch(enable_queue=True,cache_examples=True)