Update app.py

app.py

@@ -242,13 +242,16 @@ def create_cam_visualization_pil(image_pil, cam, alpha=0.6, vis_threshold=0.2):
     if cam is None:
         return image_pil
     w, h = image_pil.size
+    size = max(w, h)
 
     # Resize CAM to match image
-    cam_resized = np.array(Image.fromarray(cam).resize((w, h), resample=Image.Resampling.BILINEAR))
+    cam_resized = np.array(Image.fromarray(cam).resize((size, size), resample=Image.Resampling.BILINEAR))
 
     # Normalize CAM to [0, 1]
     cam_norm = (cam_resized - cam_resized.min()) / (np.ptp(cam_resized) + 1e-8)
 
+    cam_norm = transforms.CenterCrop((h, w))(cam_norm)
+
     # Create heatmap using matplotlib colormap
     colormap = cm.get_cmap('jet')
     cam_colored = colormap(cam_norm)[:, :, :3]  # RGB
@@ -304,7 +307,7 @@ with gr.Blocks(css=".output-class { display: none; }") as demo:
         fn=create_tags,
         inputs=[threshold_slider, sorted_tag_score_state],
         outputs=[tag_string, label_box],
-        show_progress='none'
+        show_progress='hidden'
     )
 
     label_box.select(
@@ -318,14 +321,14 @@ with gr.Blocks(css=".output-class { display: none; }") as demo:
         fn=create_cam_visualization_pil,
         inputs=[original_image_state, cam_state, alpha_slider, cam_slider],
         outputs=[image_input],
-        show_progress='none'
+        show_progress='hidden'
     )
 
     alpha_slider.input(
         fn=create_cam_visualization_pil,
         inputs=[original_image_state, cam_state, alpha_slider, cam_slider],
         outputs=[image_input],
-        show_progress='none'
+        show_progress='hidden'
     )
 
 if __name__ == "__main__":