update app.py

app.py

@@ -431,14 +431,13 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
             agg_dict = {
                 'Start Date': 'first',
                 'End Date': 'first',
-                'Calculated Value': 'mean'
+                'Calculated Value': 'mean'  # Ensure this column is named 'Calculated Value'
             }
             if shape_type.lower() == 'point':
                 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)
             return aggregated_output.to_dict(orient='records'), processing_time
         else:
             return result_df.to_dict(orient='records'), processing_time 
@@ -748,10 +747,12 @@ if file_upload is not None:
                     m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
             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:
+                # Call the aggregation function with updated parameters
                 results, processing_time = process_aggregation(
                     locations_df,
                     start_date_str,
@@ -763,17 +764,21 @@ if st.button(f"Calculate {custom_formula}"):
                     aggregation_period,
                     original_lat_col,
                     original_lon_col,
-                    custom_formula,
-                    kernel_size,
-                    include_boundary,
+                    custom_formula=custom_formula,
+                    kernel_size=kernel_size,
+                    include_boundary=include_boundary,
                     tile_cloud_threshold=tile_cloud_threshold if "tile_cloud_threshold" in locals() else 0,
                     pixel_cloud_threshold=pixel_cloud_threshold if "pixel_cloud_threshold" in locals() else 0,
                     user_scale=user_scale
                 )
+
+                # Process and display results
                 if results:
                     result_df = pd.DataFrame(results)
                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
                     st.dataframe(result_df)
+
+                    # Download button for CSV
                     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",
@@ -781,10 +786,16 @@ if st.button(f"Calculate {custom_formula}"):
                         file_name=filename,
                         mime='text/csv'
                     )
+
+                    # Success message
                     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'  
+                        x_column = 'Date Range'
                     elif 'Date' in result_df.columns:
                         x_column = 'Date'
                     elif 'Week' in result_df.columns:
@@ -796,19 +807,139 @@ if st.button(f"Calculate {custom_formula}"):
                     else:
                         st.warning("No valid time column found for plotting.")
                         st.stop()
-                    y_column = 'Calculated Value'
-                    fig = px.line(
-                        result_df,
-                        x=x_column,
-                        y=y_column,
-                        color='Location Name',
-                        title=f"{custom_formula} Over Time"
-                    )
-                    st.plotly_chart(fig)
+
+                    # Dynamically identify the value column
+                    y_column = None
+                    if 'Calculated Value' in result_df.columns:
+                        y_column = 'Calculated Value'
+                    elif 'Aggregated Value' in result_df.columns:
+                        y_column = 'Aggregated Value'
+                    else:
+                        st.warning("No value 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")
+                        if x_column == 'Location Name':
+                            st.line_chart(result_df.set_index(x_column)[y_column])
+                        else:
+                            # Convert to datetime for better sorting
+                            result_df[x_column] = pd.to_datetime(result_df[x_column], errors='ignore')
+                            result_df = result_df.sort_values(x_column)
+                            st.line_chart(result_df.set_index(x_column)[y_column])
+                    except Exception as e:
+                        st.error(f"Error creating line chart: {str(e)}")
+
+                    # Bar Chart
+                    try:
+                        st.subheader("Bar Chart")
+                        if x_column == 'Location Name':
+                            st.bar_chart(result_df.set_index(x_column)[y_column])
+                        else:
+                            result_df[x_column] = pd.to_datetime(result_df[x_column], errors='ignore')
+                            result_df = result_df.sort_values(x_column)
+                            st.bar_chart(result_df.set_index(x_column)[y_column])
+                    except Exception as e:
+                        st.error(f"Error creating bar chart: {str(e)}")
+
+                    # Advanced Plot (Plotly)
+                    try:
+                        st.subheader("Advanced Interactive Plot (Plotly)")
+                        if x_column == 'Location Name':
+                            fig = px.bar(
+                                result_df,
+                                x=x_column,
+                                y=y_column,
+                                color='Location Name',
+                                title=f"{custom_formula} by Location"
+                            )
+                        else:
+                            fig = px.line(
+                                result_df,
+                                x=x_column,
+                                y=y_column,
+                                color='Location Name',
+                                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.")
+        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,
+#                     tile_cloud_threshold=tile_cloud_threshold if "tile_cloud_threshold" in locals() else 0,
+#                     pixel_cloud_threshold=pixel_cloud_threshold if "pixel_cloud_threshold" in locals() else 0,
+#                     user_scale=user_scale
+#                 )
+#                 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.")
+#                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
+#                     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'
+#                     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.")