app update

app.py

@@ -71,22 +71,23 @@ title = "Custom YOLOv3"
 description = "Pytorch Lightning implemetation of YOLOv3 on Pascal VOC dataset.\
                         Supported classes are aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV/monitor."
 
-# examples = [
-#     ["images/000014.jpg", 0.5, 0.4, True, 0.5],
-#     ["images/000017.jpg", 0.6, 0.5, True, 0.5],
-#     ["images/000018.jpg", 0.55, 0.45, True, 0.5],
-#     ["images/000030.jpg", 0.5, 0.4, True, 0.5],
-#     ["images/Puppies.jpg", 0.6, 0.7, True, 0.5],
-# ]
+examples = [["examples/example1.jpg"],
+            ["examples/example2.jpg"],
+            ["examples/example3.jpg"],
+            ["examples/example4.jpg"],
+            ["examples/example5.jpg"],
+            ["examples/example6.jpg"],
+            ["examples/example7.jpg"],
+            ["examples/example8.jpg"]]
 
 demo = gr.Interface(model_inference, inputs=[gr.Image(label="Input an image"),
-                                                                          gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
-                                                                          gr.Slider(0, 1, value=0.4, label="Threshold"),
-                                                                          gr.Radio(["Yes", "No"], value="No"  , label="Show GradCAM outputs"),
-                                                                          gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
-                                                                          gr.Slider(-2, -1, value=-1, step=1, label="Which Layer?")],
-                                  outputs=[gr.Gallery(label="Model Outputs", rows=2, columns=1)],
-                                  title=title, description=description)
+                                          gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
+                                          gr.Slider(0, 1, value=0.4, label="Threshold"),
+                                          gr.Radio(["Yes", "No"], value="No"  , label="Show GradCAM outputs"),
+                                          gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
+                                          gr.Slider(-2, -1, value=-1, step=1, label="Which Layer?")],
+                                  outputs=[gr.Gallery(label="Model Outputs", rows=2, columns=1, object_fit="contain", height="auto")],
+                                  title=title, description=description, examples=examples)
 demo.launch()