added sample images

app.py

@@ -18,7 +18,7 @@ inceptionV3_model = InceptionV3Classifier()
 mobilenet_model = MobileNetClassifier()
 
 def make_prediction(image, model_type):
-  if "CNN (2 layer) - Custom" == model_type:
+  if "CNN (Custom)" == model_type:
     top_classes, top_probs = custom_model.classify_image(image, top_k=3)
     return {CLASS_NAMES[cls_id]:str(prob) for cls_id, prob in zip(top_classes, top_probs)}
   elif "ResNet50" == model_type:
@@ -61,7 +61,7 @@ def train_model(epochs, batch_size, validation_split):
 
 
 def update_train_param_display(model_type):
-  if "CNN (2 layer) - Custom" == model_type:
+  if "CNN (Custom)" == model_type:
     return [gr.update(visible=True), gr.update(visible=False)]
   return [gr.update(visible=False), gr.update(visible=True)]
 
@@ -75,7 +75,7 @@ if __name__ == "__main__":
       with gr.Column(scale=1):
           img_input = gr.Image()
           model_type = gr.Dropdown(
-              ["CNN (2 layer) - Custom", 
+              ["CNN (Custom)", 
                 "ResNet50", 
                 "VGG16",
                 "Inception v3",
@@ -101,6 +101,18 @@ if __name__ == "__main__":
 
       with gr.Column(scale=1):
         output_label = gr.Label()
+
+    gr.Markdown("## Sample Images")
+    gr.Examples(
+        examples=[os.path.join(os.path.dirname(__file__), "assets/dog_2.jpg"),
+                  os.path.join(os.path.dirname(__file__), "assets/truck.jpg"),
+                  os.path.join(os.path.dirname(__file__), "assets/car.jpg")
+                 ],
+        inputs=img_input,
+        outputs=[segmentation_img_output, depth_img_output, dist_img_output, pcd_img_output],
+        fn=process_image,
+        cache_examples=True,
+    )
     
     # app logic
     predict_btn_1.click(make_prediction, inputs=[img_input, model_type], outputs=[output_label])