Update app.py

app.py

@@ -35,33 +35,6 @@ scaled_anchors = (
 ).to(model.device)
 
 cam = YoloGradCAM(model=model, target_layers=[model.layers[-2]], scaled_anchors=scaled_anchors, use_cuda=False)
-'''cfg.IMG_DIR = cfg.DATASET + "/images/"
-cfg.LABEL_DIR = cfg.DATASET + "/labels/"
-eval_dataset = YOLODataset(
-    cfg.DATASET + "/25examples.csv",
-    transform=cfg.test_transforms,
-    S=[cfg.IMAGE_SIZE // 32, cfg.IMAGE_SIZE // 16, cfg.IMAGE_SIZE // 8],
-    img_dir=cfg.IMG_DIR,
-    label_dir=cfg.LABEL_DIR,
-    anchors=cfg.ANCHORS,
-    mosaic=False
-)
-eval_loader = DataLoader(
-    dataset=eval_dataset,
-    batch_size=cfg.BATCH_SIZE,
-    num_workers=cfg.NUM_WORKERS,
-    pin_memory=cfg.PIN_MEMORY,
-    shuffle=True,
-    drop_last=False,
-)
-
-scaled_anchors = (
-        torch.tensor(cfg.ANCHORS)
-        * torch.tensor(cfg.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
-)
-scaled_anchors = scaled_anchors.to(cfg.DEVICE)
-
-utils.plot_examples(model, eval_loader, 0.5, 0.6, scaled_anchors)'''
 
 sample_images = [
     ['images/000001.jpg'],
@@ -153,11 +126,11 @@ with gr.Blocks() as app:
     with gr.Row():
         if_show_grad_cam = gr.Checkbox(value=True, label='Show Class Activation Map (What the model sees)?')
 
-    # with gr.Row(visible=True) as top_class_output:
-    with gr.Row(visible=True)  as top_class_output:
-        top_class_output_img = gr.Image(interactive=False, label='Prediction Output')
-    with gr.Row(visible=True)  as top_class_output:
-        grad_cam_out = gr.Image(interactive=False, visible=True, label='CAM Outcome')
+    with gr.Row(visible=True) as top_class_output:
+        with gr.Column(visible=True)  as top_class_output:
+            top_class_output_img = gr.Image(interactive=False, label='Prediction Output')
+        with gr.Column(visible=True)  as top_class_output:
+            grad_cam_out = gr.Image(interactive=False, visible=True, label='CAM Outcome')
 
 
     def show_cam_output(input):