Update app.py

app.py

@@ -826,6 +826,115 @@ if file_upload is not None:
 #     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..."):
+#             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,
+#                     cloud_threshold=cloud_threshold
+#                 )
+#                 if results:
+#                     result_df = pd.DataFrame(results)
+#                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
+#                     st.dataframe(result_df)
+                    
+#                     # Debug: Print column names to verify
+#                     st.write("Available columns in results:", result_df.columns.tolist())
+                    
+#                     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.")
+                    
+#                     # Graph Visualization Section
+#                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
+                    
+#                     # Dynamically identify the value column (handle both 'Calculated Value' and 'Aggregated Value')
+#                     value_column = None
+#                     if 'Calculated Value' in result_df.columns:
+#                         value_column = 'Calculated Value'
+#                     elif 'Aggregated Value' in result_df.columns:
+#                         value_column = 'Aggregated Value'
+#                     else:
+#                         st.warning("No value column found for plotting. Available columns: " + ", ".join(result_df.columns))
+#                         st.stop()
+                    
+#                     # Dynamically identify the time column
+#                     if aggregation_period.lower() == 'custom (start date to end date)':
+#                         x_column = 'Date Range'  
+#                     elif 'Date' in result_df.columns:
+#                         x_column = 'Date'
+#                     elif 'Week' in result_df.columns:
+#                         x_column = 'Week'
+#                     elif 'Month' in result_df.columns:
+#                         x_column = 'Month'
+#                     elif 'Year' in result_df.columns:
+#                         x_column = 'Year'
+#                     else:
+#                         st.warning("No valid time column found for plotting. Available columns: " + ", ".join(result_df.columns))
+#                         st.stop()
+                        
+#                     # Ensure we have valid data to plot
+#                     if result_df.empty:
+#                         st.warning("No data available for plotting.")
+#                         st.stop()
+                    
+#                     # Line Chart
+#                     try:
+#                         st.subheader("Line Chart")
+#                         st.line_chart(result_df.set_index(x_column)[value_column])
+#                     except Exception as e:
+#                         st.error(f"Error creating line chart: {str(e)}")
+                    
+#                     # Bar Chart
+#                     try:
+#                         st.subheader("Bar Chart")
+#                         st.bar_chart(result_df.set_index(x_column)[value_column])
+#                     except Exception as e:
+#                         st.error(f"Error creating bar chart: {str(e)}")
+                    
+#                     # Advanced Plot (Plotly)
+#                     try:
+#                         st.subheader("Advanced Interactive Plot (Plotly)")
+#                         fig = px.line(
+#                             result_df,
+#                             x=x_column,
+#                             y=value_column,
+#                             color='Location Name' if 'Location Name' in result_df.columns else None,
+#                             title=f"{custom_formula} Over Time"
+#                         )
+#                         st.plotly_chart(fig)
+#                     except Exception as e:
+#                         st.error(f"Error creating interactive plot: {str(e)}")
+                
+#                 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..."):
@@ -848,12 +957,32 @@ if st.button(f"Calculate {custom_formula}"):
                 )
                 if results:
                     result_df = pd.DataFrame(results)
+                    
+                    # Reorder columns as requested
+                    desired_columns = [
+                        'Location Name',
+                        'Start Date',
+                        'End Date',
+                        'Date Range',
+                        original_lat_col,
+                        original_lon_col,
+                        'Aggregated Value' if 'Aggregated Value' in result_df.columns else 'Calculated Value'
+                    ]
+                    
+                    # Filter to only include columns that exist in the DataFrame
+                    final_columns = [col for col in desired_columns if col in result_df.columns]
+                    result_df = result_df[final_columns]
+                    
+                    # Rename the value column to be consistent
+                    result_df = result_df.rename(columns={
+                        'Aggregated Value': 'Calculated Value',
+                        original_lat_col: 'Latitude',
+                        original_lon_col: 'Longitude'
+                    })
+                    
                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
                     st.dataframe(result_df)
                     
-                    # Debug: Print column names to verify
-                    st.write("Available columns in results:", result_df.columns.tolist())
-                    
                     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",
@@ -866,15 +995,8 @@ if st.button(f"Calculate {custom_formula}"):
                     # Graph Visualization Section
                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
                     
-                    # Dynamically identify the value column (handle both 'Calculated Value' and 'Aggregated Value')
-                    value_column = None
-                    if 'Calculated Value' in result_df.columns:
-                        value_column = 'Calculated Value'
-                    elif 'Aggregated Value' in result_df.columns:
-                        value_column = 'Aggregated Value'
-                    else:
-                        st.warning("No value column found for plotting. Available columns: " + ", ".join(result_df.columns))
-                        st.stop()
+                    # Dynamically identify the value column
+                    value_column = 'Calculated Value'
                     
                     # Dynamically identify the time column
                     if aggregation_period.lower() == 'custom (start date to end date)':
@@ -888,7 +1010,7 @@ if st.button(f"Calculate {custom_formula}"):
                     elif 'Year' in result_df.columns:
                         x_column = 'Year'
                     else:
-                        st.warning("No valid time column found for plotting. Available columns: " + ", ".join(result_df.columns))
+                        st.warning("No valid time column found for plotting.")
                         st.stop()
                         
                     # Ensure we have valid data to plot
@@ -917,7 +1039,7 @@ if st.button(f"Calculate {custom_formula}"):
                             result_df,
                             x=x_column,
                             y=value_column,
-                            color='Location Name' if 'Location Name' in result_df.columns else None,
+                            color='Location Name',
                             title=f"{custom_formula} Over Time"
                         )
                         st.plotly_chart(fig)