Update app.py

app.py

@@ -242,18 +242,18 @@ def calculate_cloud_percentage(image, cloud_band='QA60'):
         geometry=image.geometry(),
         scale=60,  # QA60 resolution is 60 meters
         maxPixels=1e13
-    ).get(cloud_band).getInfo()
+    ).get(cloud_band)
     cloudy_pixels = cloud_mask.reduceRegion(
         reducer=ee.Reducer.sum(),
         geometry=image.geometry(),
         scale=60,  # QA60 resolution is 60 meters
         maxPixels=1e13
-    ).get(cloud_band).getInfo()
+    ).get(cloud_band)
 
     # Calculate cloud percentage
     if total_pixels == 0:
         return 0  # Avoid division by zero
-    return ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100).getInfo()
+    return ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100)
 
 # Preprocessing function with cloud filtering
 def preprocess_collection(collection, cloud_threshold):
@@ -449,7 +449,6 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 'Start Date': 'first',
                 'End Date': 'first',
                 'Calculated Value': 'mean'
-                # 'Date Range': 'first'  # Include Date Range in aggregation
             }
             if shape_type.lower() == 'point':
                 agg_dict[original_lat_col] = 'first'
@@ -746,45 +745,6 @@ if file_upload is not None:
             st.write("Map of Uploaded Polygons:")
             m.to_streamlit()
 
-# if st.button(f"Calculate {custom_formula}"):
-#     if not locations_df.empty:
-#         with st.spinner("Processing Data..."):
-#             try:
-#                 results, processing_time = process_aggregation(
-#                     locations_df,
-#                     start_date_str,
-#                     end_date_str,
-#                     dataset_id,
-#                     selected_bands,
-#                     reducer_choice,
-#                     shape_type,
-#                     aggregation_period,
-#                     original_lat_col,
-#                     original_lon_col,
-#                     custom_formula,
-#                     kernel_size,
-#                     include_boundary
-#                 )
-#                 if results:
-#                     result_df = pd.DataFrame(results)
-#                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
-#                     st.dataframe(result_df)
-#                     filename = f"{main_selection}_{dataset_id}_{start_date.strftime('%Y%m%d')}_{end_date.strftime('%Y%m%d')}_{aggregation_period.lower()}.csv"
-#                     st.download_button(
-#                         label="Download results as CSV",
-#                         data=result_df.to_csv(index=False).encode('utf-8'),
-#                         file_name=filename,
-#                         mime='text/csv'
-#                     )
-#                     st.success(f"Processing complete! Total processing time: {processing_time:.2f} seconds.")
-#                 else:
-#                     st.warning("No results were generated. Check your inputs or formula.")
-#                     st.info(f"Total processing time: {processing_time:.2f} seconds.")
-#             except Exception as e:
-#                 st.error(f"An error occurred during processing: {str(e)}")
-#     else:
-#         st.warning("Please upload a valid file to proceed.")
-        
 if st.button(f"Calculate {custom_formula}"):
     if not locations_df.empty:
         with st.spinner("Processing Data..."):