update app.py

app.py

@@ -694,25 +694,45 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 progress_bar.progress(progress_percentage)
                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
     
-    if aggregated_results:
-        result_df = pd.DataFrame(aggregated_results)
-        if aggregation_period.lower() == 'daily':
-            aggregated_output = result_df.groupby('Location Name').agg({
-                'Latitude': 'first' if shape_type.lower() == 'point' else None,
-                'Longitude': 'first' if shape_type.lower() == 'point' else None,
-                'Start Date': 'first',
-                'End Date': 'first',
-                'Calculated Value': 'mean'
-            }).reset_index()
-            # Remove None columns (for polygons)
-            aggregated_output = aggregated_output[[col for col in aggregated_output.columns if col is not None]]
-            aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
-            return aggregated_output.to_dict(orient='records')
-        else:
-            return result_df.to_dict(orient='records')
+    # if aggregated_results:
+    #     result_df = pd.DataFrame(aggregated_results)
+    #     if aggregation_period.lower() == 'daily':
+    #         aggregated_output = result_df.groupby('Location Name').agg({
+    #             'Latitude': 'first' if shape_type.lower() == 'point' else None,
+    #             'Longitude': 'first' if shape_type.lower() == 'point' else None,
+    #             'Start Date': 'first',
+    #             'End Date': 'first',
+    #             'Calculated Value': 'mean'
+    #         }).reset_index()
+    #         # Remove None columns (for polygons)
+    #         aggregated_output = aggregated_output[[col for col in aggregated_output.columns if col is not None]]
+    #         aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+    #         return aggregated_output.to_dict(orient='records')
+    #     else:
+    #         return result_df.to_dict(orient='records')
     
-    return []
-
+    # return []
+    
+    if aggregated_results:
+            result_df = pd.DataFrame(aggregated_results)
+            if aggregation_period.lower() == 'daily':
+                # Define aggregation dictionary based on shape_type
+                agg_dict = {
+                    'Start Date': 'first',
+                    'End Date': 'first',
+                    'Calculated Value': 'mean'
+                }
+                if shape_type.lower() == 'point':
+                    agg_dict['Latitude'] = 'first'
+                    agg_dict['Longitude'] = 'first'
+                
+                aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
+                aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+                return aggregated_output.to_dict(orient='records')
+            else:
+                return result_df.to_dict(orient='records')
+        
+        return []
 # When the user clicks the process button, start the calculation
 if st.button(f"Calculate ({index_choice})"):
     if file_upload is not None: