Update app.py

app.py

@@ -745,86 +745,86 @@ 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.")
+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)
+                    # 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()
+                    # 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'
+                    y_column = 'Calculated Value'
                     
-#                     # Line Chart
-#                     st.subheader("Line Chart")
-#                     st.line_chart(result_df.set_index(x_column)[y_column])
+                    # 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])
+                    # 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)
+                    # 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.")
+                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.")
+            except Exception as e:
+                st.error(f"An error occurred during processing: {str(e)}")
+    else:
+        st.warning("Please upload a valid file to proceed.")
 
 
 
@@ -935,141 +935,141 @@ 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)
+# 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)
                     
-                    # Reorder and rename columns
-                    column_mapping = {
-                        'Location Name': 'Location Name',
-                        'Start Date': 'Start Date',
-                        'End Date': 'End Date',
-                        'Date Range': 'Date Range',
-                        original_lat_col: 'Latitude',
-                        original_lon_col: 'Longitude',
-                        'Aggregated Value': 'Calculated Value',
-                        'Calculated Value': 'Calculated Value'
-                    }
+#                     # Reorder and rename columns
+#                     column_mapping = {
+#                         'Location Name': 'Location Name',
+#                         'Start Date': 'Start Date',
+#                         'End Date': 'End Date',
+#                         'Date Range': 'Date Range',
+#                         original_lat_col: 'Latitude',
+#                         original_lon_col: 'Longitude',
+#                         'Aggregated Value': 'Calculated Value',
+#                         'Calculated Value': 'Calculated Value'
+#                     }
                     
-                    # Keep only columns that exist in the results
-                    available_columns = [col for col in column_mapping.keys() if col in result_df.columns]
-                    result_df = result_df[available_columns]
-                    result_df = result_df.rename(columns={k:v for k,v in column_mapping.items() if k in available_columns})
+#                     # Keep only columns that exist in the results
+#                     available_columns = [col for col in column_mapping.keys() if col in result_df.columns]
+#                     result_df = result_df[available_columns]
+#                     result_df = result_df.rename(columns={k:v for k,v in column_mapping.items() if k in available_columns})
                     
-                    st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
-                    st.dataframe(result_df)
+#                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
+#                     st.dataframe(result_df)
                     
-                    # Graph Visualization Section
-                    st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
+#                     # Graph Visualization Section
+#                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
                     
-                    # Determine time column based on aggregation period
-                    time_column_map = {
-                        'custom (start date to end date)': 'Date Range',
-                        'daily': 'Date',
-                        'weekly': 'Week',
-                        'monthly': 'Month',
-                        'yearly': 'Year'
-                    }
+#                     # Determine time column based on aggregation period
+#                     time_column_map = {
+#                         'custom (start date to end date)': 'Date Range',
+#                         'daily': 'Date',
+#                         'weekly': 'Week',
+#                         'monthly': 'Month',
+#                         'yearly': 'Year'
+#                     }
                     
-                    x_column = time_column_map.get(aggregation_period.lower())
+#                     x_column = time_column_map.get(aggregation_period.lower())
                     
-                    if x_column not in result_df.columns:
-                        # Try to find any time-related column
-                        time_columns = ['Date Range', 'Date', 'Week', 'Month', 'Year', 'day', 'month', 'year']
-                        x_column = next((col for col in time_columns if col in result_df.columns), None)
+#                     if x_column not in result_df.columns:
+#                         # Try to find any time-related column
+#                         time_columns = ['Date Range', 'Date', 'Week', 'Month', 'Year', 'day', 'month', 'year']
+#                         x_column = next((col for col in time_columns if col in result_df.columns), None)
                     
-                    if x_column is None:
-                        st.warning("No time column found for plotting. Showing data without time axis.")
-                        x_column = 'Location Name'
+#                     if x_column is None:
+#                         st.warning("No time column found for plotting. Showing data without time axis.")
+#                         x_column = 'Location Name'
                     
-                    value_column = 'Calculated Value'
+#                     value_column = 'Calculated Value'
                     
-                    if value_column not in result_df.columns:
-                        st.error("No calculated values found for plotting.")
-                        st.stop()
+#                     if value_column not in result_df.columns:
+#                         st.error("No calculated values found 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)[value_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)[value_column])
-                    except Exception as e:
-                        st.error(f"Error creating line chart: {str(e)}")
+#                     # Line Chart
+#                     try:
+#                         st.subheader("Line Chart")
+#                         if x_column == 'Location Name':
+#                             st.line_chart(result_df.set_index(x_column)[value_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)[value_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)[value_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)[value_column])
-                    except Exception as e:
-                        st.error(f"Error creating bar chart: {str(e)}")
+#                     # Bar Chart
+#                     try:
+#                         st.subheader("Bar Chart")
+#                         if x_column == 'Location Name':
+#                             st.bar_chart(result_df.set_index(x_column)[value_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)[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)")
-                        if x_column == 'Location Name':
-                            fig = px.bar(
-                                result_df,
-                                x=x_column,
-                                y=value_column,
-                                color='Location Name',
-                                title=f"{custom_formula} by Location"
-                            )
-                        else:
-                            fig = px.line(
-                                result_df,
-                                x=x_column,
-                                y=value_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)}")
+#                     # Advanced Plot (Plotly)
+#                     try:
+#                         st.subheader("Advanced Interactive Plot (Plotly)")
+#                         if x_column == 'Location Name':
+#                             fig = px.bar(
+#                                 result_df,
+#                                 x=x_column,
+#                                 y=value_column,
+#                                 color='Location Name',
+#                                 title=f"{custom_formula} by Location"
+#                             )
+#                         else:
+#                             fig = px.line(
+#                                 result_df,
+#                                 x=x_column,
+#                                 y=value_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)}")
                     
-                    # Download button
-                    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.")
+#                     # Download button
+#                     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.")
+#                 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.")
+#             except Exception as e:
+#                 st.error(f"An error occurred during processing: {str(e)}")
+#     else:
+#         st.warning("Please upload a valid file to proceed.")