Update app.py

app.py

@@ -180,13 +180,14 @@ with gr.Blocks(theme='gradio/monochrome') as demo:
     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. 
 
-To utilize the demo, upload an image or select one from the provided examples, and click on "Infer" to view the following results: 
+To utilize the demo, upload an image and click on "Infer" to view the following results: 
 
 - **Detection**: outputs of Object Detector
 - **Detection summary**: a summary of the detection outputs
-- **Verdict**: verdicts from Monitors
+- **Verdict**: verdicts from Monitors (problematic detections caused by out-of-distribution(OOD) objects will be identified as OOD objects)
 - **Explainable AI**: visual explanation generated by [grad-cam](https://github.com/jacobgil/pytorch-grad-cam) library which is based on Class Activation Mapping(CAM) method.
 
+You can also select an image from the cached **examples** to quickly try out. 
 In case the output image seems too small, simply right-click on the image, and choose “Open image in new tab” to visualize it in full size.
     """
     )