Update app.py

app.py

@@ -356,7 +356,7 @@ def process_image_with_xception_gradcam(image, model, device, pred_class):
         _, _, _, _, face_box, _ = dataset[0]
         
         return raw_cam, overlay, comparison, face_box
-                else:
+    else:
         st.error("Failed to generate GradCAM visualization")
         return None, None, None, None
 
@@ -435,18 +435,18 @@ def generate_gradcam_caption(image, processor, model, max_length=60):
         # Try to parse the caption based on different possible formats
         try:
             # Original format with "high activation:" etc.
-        formatted_text = ""
+            formatted_text = ""
             if "high activation :" in caption:
                 high_match = caption.split("high activation :")[1].split("moderate")[0]
-            formatted_text += f"**High activation**:\n{high_match.strip()}\n\n"
+                formatted_text += f"**High activation**:\n{high_match.strip()}\n\n"
             
             if "moderate activation :" in caption:
                 moderate_match = caption.split("moderate activation :")[1].split("low")[0]
-            formatted_text += f"**Moderate activation**:\n{moderate_match.strip()}\n\n"
+                formatted_text += f"**Moderate activation**:\n{moderate_match.strip()}\n\n"
             
             if "low activation :" in caption:
                 low_match = caption.split("low activation :")[1]
-            formatted_text += f"**Low activation**:\n{low_match.strip()}"
+                formatted_text += f"**Low activation**:\n{low_match.strip()}"
             
             # If nothing was extracted using the original format, try alternative formats
             if not formatted_text.strip():