update app.py

app.py

@@ -515,21 +515,29 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
         st.error("Custom formula cannot be empty. Please provide a formula.")
         return aggregated_results
 
-        # Check if dataset_id is an ee.Image or ee.ImageCollection
+    # Initialize is_image and scale with default values
+    is_image = False  # Default to False, will be updated based on dataset type
+    scale = 30  # Default scale, will be overridden for ee.Image if possible
+    
+    # Check if dataset_id is an ee.Image or ee.ImageCollection
+    try:
+        dataset = ee.Image(dataset_id)
+        is_image = True
+        # Try to get the nominal scale from the dataset
         try:
-            dataset = ee.Image(dataset_id)
-            is_image = True
-            # Try to get the nominal scale from the dataset (default to 30m if unavailable)
-            try:
-                scale = dataset.projection().nominalScale().getInfo()
-            except:
-                scale = 30  
-                st.warning(f"Could not determine nominal scale for {dataset_id}. Using default scale of 30m.")
+            scale = dataset.projection().nominalScale().getInfo()
         except:
+            st.warning(f"Could not determine nominal scale for {dataset_id}. Using default scale of 30m.")
+            scale = 30
+    except:
+        try:
             dataset = ee.ImageCollection(dataset_id)
             is_image = False
-            scale = 30
-    
+            scale = 30  # Default scale for ImageCollections
+        except Exception as e:
+            st.error(f"Invalid dataset ID '{dataset_id}': {str(e)}. Must be a valid ee.Image or ee.ImageCollection.")
+            return aggregated_results
+            
     total_steps = len(locations_df)
     progress_bar = st.progress(0)
     progress_text = st.empty()