update app.py

app.py

@@ -887,7 +887,7 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     if index_choice.lower() == 'custom_formula' and not custom_formula:
         st.error("Custom formula cannot be empty. Please provide a formula.")
         return aggregated_results  # Return early to avoid further processing
-
+    
     # Initialize progress bar
     total_steps = len(locations_df)
     progress_bar = st.progress(0)
@@ -923,10 +923,31 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 # Process each image in the collection
                 image_list = collection.toList(collection.size())
                 all_values = []  # To store calculated values for mean calculation
+                start_date = None
+                end_date = None
                 
                 for i in range(image_list.size().getInfo()):
                     image = ee.Image(image_list.get(i))
                     
+                    # Extract the timestamp based on the aggregation period
+                    if aggregation_period.lower() == 'daily':
+                        timestamp = image.get('day')
+                    elif aggregation_period.lower() == 'weekly':
+                        timestamp = image.get('week_start')  # Use week start date
+                    elif aggregation_period.lower() == 'monthly':
+                        timestamp = image.get('month')
+                    elif aggregation_period.lower() == 'yearly':
+                        timestamp = image.get('year')
+                    
+                    # Format the timestamp as a valid date string
+                    date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
+                    
+                    # Update start_date and end_date
+                    if start_date is None or date < start_date:
+                        start_date = date
+                    if end_date is None or date > end_date:
+                        end_date = date
+                    
                     # Calculate the index for each period
                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
                     
@@ -952,7 +973,9 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                         'Location Name': location_name,
                         'Latitude': latitude,
                         'Longitude': longitude,
-                        'Mean Value': mean_value
+                        'Mean Value': mean_value,
+                        'Start Date': start_date,
+                        'End Date': end_date
                     })
                 
                 # Update progress bar
@@ -989,10 +1012,31 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 # Process each image in the collection
                 image_list = collection.toList(collection.size())
                 all_values = []  # To store calculated values for mean calculation
+                start_date = None
+                end_date = None
                 
                 for i in range(image_list.size().getInfo()):
                     image = ee.Image(image_list.get(i))
                     
+                    # Extract the timestamp based on the aggregation period
+                    if aggregation_period.lower() == 'daily':
+                        timestamp = image.get('day')
+                    elif aggregation_period.lower() == 'weekly':
+                        timestamp = image.get('week_start')  # Use week start date
+                    elif aggregation_period.lower() == 'monthly':
+                        timestamp = image.get('month')
+                    elif aggregation_period.lower() == 'yearly':
+                        timestamp = image.get('year')
+                    
+                    # Format the timestamp as a valid date string
+                    date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
+                    
+                    # Update start_date and end_date
+                    if start_date is None or date < start_date:
+                        start_date = date
+                    if end_date is None or date > end_date:
+                        end_date = date
+                    
                     # Calculate the index for each period
                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
                     
@@ -1016,7 +1060,9 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                     mean_value = sum(all_values) / len(all_values)
                     aggregated_results.append({
                         'Location Name': location_name,
-                        'Mean Value': mean_value
+                        'Mean Value': mean_value,
+                        'Start Date': start_date,
+                        'End Date': end_date
                     })
                 
                 # Update progress bar
@@ -1039,8 +1085,8 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
             result_df.rename(columns={'latitude': 'Latitude', 'longitude': 'Longitude'}, inplace=True)
             result_df.drop(columns=['name'], inplace=True)
         
-        # Return only the required fields
-        return result_df[['Location Name', 'Latitude', 'Longitude', 'Mean Value']].to_dict(orient='records')
+        # Return only the required fields, including Start Date and End Date
+        return result_df[['Location Name', 'Latitude', 'Longitude', 'Mean Value', 'Start Date', 'End Date']].to_dict(orient='records')
     
     return []
 ####################################################