Update app.py

app.py

@@ -266,7 +266,7 @@ def preprocess_collection(collection, pixel_cloud_threshold):
     return collection
 
 # Process single geometry
-def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None, user_scale=None):
+def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None, user_scale=None, pixel_cloud_threshold=0):
     if shape_type.lower() == "point":
         latitude = row.get('latitude')
         longitude = row.get('longitude')
@@ -291,10 +291,18 @@ def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selec
         except ValueError:
             return None
     
+    # Filter collection by date and area first
     collection = ee.ImageCollection(dataset_id) \
         .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
         .filterBounds(roi)
     
+    st.write(f"After initial filtering: {collection.size().getInfo()} images")
+    
+    # Apply pixel cloud masking if threshold > 0
+    if pixel_cloud_threshold > 0:
+        collection = preprocess_collection(collection, pixel_cloud_threshold)
+        st.write(f"After cloud masking: {collection.size().getInfo()} images")
+    
     if aggregation_period.lower() == 'custom (start date to end date)':
         collection = aggregate_data_custom(collection)
     elif aggregation_period.lower() == 'daily':
@@ -375,7 +383,7 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")
     
     if tile_cloud_threshold > 0 or pixel_cloud_threshold > 0:
-        raw_collection = preprocess_collection(raw_collection, tile_cloud_threshold, pixel_cloud_threshold)
+        raw_collection = preprocess_collection(raw_collection, pixel_cloud_threshold)
         st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")
     
     with ThreadPoolExecutor(max_workers=10) as executor: