Update app.py

app.py

@@ -454,8 +454,8 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 agg_dict[original_lat_col] = 'first'
                 agg_dict[original_lon_col] = 'first'
             aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
-            aggregated_output['Date Range'] = aggregated_output['Start Date'] + " to " + aggregated_output['End Date']
-            aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+            # aggregated_output['Date Range'] = aggregated_output['Start Date'] + " to " + aggregated_output['End Date']
+            # aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
             return aggregated_output.to_dict(orient='records'), processing_time
         else:
             return result_df.to_dict(orient='records'), processing_time 
@@ -745,6 +745,87 @@ 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,
+#                     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)
+#                     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 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.")
+#                         st.stop()
+                        
+#                     y_column = 'Calculated Value'
+                    
+#                     # Line Chart
+#                     st.subheader("Line Chart")
+#                     st.line_chart(result_df.set_index(x_column)[y_column])
+                    
+#                     # Bar Chart
+#                     st.subheader("Bar Chart")
+#                     st.bar_chart(result_df.set_index(x_column)[y_column])
+                    
+#                     # Advanced Plot (Plotly)
+#                     st.subheader("Advanced Interactive Plot (Plotly)")
+#                     fig = px.line(
+#                         result_df,
+#                         x=x_column,
+#                         y=y_column,
+#                         color='Location Name',
+#                         title=f"{custom_formula} Over Time"
+#                     )
+#                     st.plotly_chart(fig)
+                
+#                 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..."):
@@ -769,6 +850,10 @@ if st.button(f"Calculate {custom_formula}"):
                     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",
@@ -781,6 +866,16 @@ 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 time column
                     if aggregation_period.lower() == 'custom (start date to end date)':
                         x_column = 'Date Range'  
@@ -793,29 +888,41 @@ 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.")
+                        st.warning("No valid time column found for plotting. Available columns: " + ", ".join(result_df.columns))
                         st.stop()
                         
-                    y_column = 'Calculated Value'
+                    # Ensure we have valid data to plot
+                    if result_df.empty:
+                        st.warning("No data available for plotting.")
+                        st.stop()
                     
                     # Line Chart
-                    st.subheader("Line Chart")
-                    st.line_chart(result_df.set_index(x_column)[y_column])
+                    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
-                    st.subheader("Bar Chart")
-                    st.bar_chart(result_df.set_index(x_column)[y_column])
+                    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)
-                    st.subheader("Advanced Interactive Plot (Plotly)")
-                    fig = px.line(
-                        result_df,
-                        x=x_column,
-                        y=y_column,
-                        color='Location Name',
-                        title=f"{custom_formula} Over Time"
-                    )
-                    st.plotly_chart(fig)
+                    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.")