Update app.py

app.py

@@ -142,23 +142,17 @@ def fig2img(fig):
     img = Image.open(buf)
     return img
 
-description = """Object Detection with YOLOS. Choose https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txtyour model and you're good to go.
-You can adapt the minimum probability threshold with the slider.
-Additionally you can restrict the classes that will be shown by putting in a comma separated list of 
-[COCO classes](https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txt). 
-Leaving the field empty will show all classes"""
-
 image_in = gr.components.Image()
 image_out = gr.components.Image()
 model_choice = gr.components.Dropdown(["yolos-tiny", "yolos-small", "yolos-base", "yolos-small-300", "yolos-small-dwr"], value="yolos-small", label="YOLOS Model")
 prob_threshold_slider = gr.components.Slider(minimum=0, maximum=1.0, step=0.01, value=0.9, label="Probability Threshold")
-classes_to_show = gr.components.Textbox(placeholder="e.g. person, boat", label="Classes to use (empty means all classes)")
+classes_to_show = gr.components.Textbox(placeholder="e.g. car, truck, traffic light", label="Classes to use (empty means all classes)")
 
 Iface = gr.Interface(
     fn=infer,
     inputs=[image_in,model_choice, prob_threshold_slider, classes_to_show],
     outputs=image_out,
-    #examples=[["examples/10_People_Marching_People_Marching_2_120.jpg"], ["examples/12_Group_Group_12_Group_Group_12_26.jpg"], ["examples/43_Row_Boat_Canoe_43_247.jpg"]],
+    examples=examples
     title="Object Detection with YOLOS",
     description=description,
 ).launch()