Update app.py

app.py

@@ -155,10 +155,12 @@ clf_report = classification_report(all_labels, all_preds, target_names=class_nam
 def describe_model():
     fig, axes = plt.subplots(2, 1, figsize=(8, 10))
 
+    # Confusion Matrix
     sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues',
                 xticklabels=class_names, yticklabels=class_names, ax=axes[0])
     axes[0].set_title("Confusion Matrix")
 
+    # Show sample predictions
     images, labels = next(iter(val_loader))
     outputs = model(images.to(device))
     _, preds = torch.max(outputs, 1)
@@ -169,11 +171,12 @@ def describe_model():
     axes[1].set_title("Sample Predictions: " + " | ".join([class_names[p.item()] for p in preds[:8]]))
     axes[1].axis('off')
 
-    dims_summary = f"Image sizes (sampled): {list(set(image_shapes))}"
-    summary = "\n".join(epoch_logs) + "\n\n" + dims_summary
+    # Summary (without image_shapes)
+    summary = "\n".join(epoch_logs)
 
     return fig, summary + "\n\n" + clf_report
 
+
 def predict_image(img):
     img = img.convert("RGB")
     transform_pred = transforms.Compose([