Update app.py

app.py

@@ -175,7 +175,7 @@ def inference_gd(file):
 
 
 examples = ["examples/0.jpg", "examples/1.jpg", "examples/2.jpg", "examples/3.jpg"]
-with gr.Blocks() as demo:
+with gr.Blocks(theme='gradio/monochrome') as demo:
     gr.Markdown("# Runtime Monitoring Object Detection")
     gr.Markdown(
         """This interactive demo is based on the box abstraction-based monitors for Faster R-CNN model. The model is trained using [Detectron2](https://github.com/facebookresearch/detectron2) library on the in-distribution dataset [Berkeley DeepDrive-100k](https://www.bdd100k.com/), which contains objects within autonomous driving domain. The monitors are constructed by abstraction of extracted feature from the training data. The demo showcases the monitors' capacity to reject problematic detections due to out-of-distribution(OOD) objects. 
@@ -203,7 +203,7 @@ In case the output image seems too small, simply right-click on the image, and c
             with gr.Tab("Explainable AI"):
                 cam = gr.Image(label="Output")
             df = gr.Dataframe(label="Detection summary")
-    button.click(fn=inference_gd, inputs=image, outputs=[detection, df, verdict, cam])
+    button.click(fn=inference_gd, inputs=image, outputs=[detection, df, verdict, cam], cache_examples=True)
 
 demo.launch()