update app.py

app.py

@@ -11,6 +11,8 @@ from shapely.geometry import base
 from xml.etree import ElementTree as XET
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import time
+import matplotlib.pyplot as plt
+import plotly.express as px
 
 # Set up the page layout
 st.set_page_config(layout="wide")
@@ -641,6 +643,45 @@ 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..."):
@@ -662,8 +703,12 @@ if st.button(f"Calculate {custom_formula}"):
                 )
                 if results:
                     result_df = pd.DataFrame(results)
+                    
+                    # Display processed results table
                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
                     st.dataframe(result_df)
+                    
+                    # Download button for results
                     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",
@@ -671,10 +716,56 @@ 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)
+                    
+                    # Allow user to select graph type
+                    graph_type = st.selectbox(
+                        "Select Graph Type",
+                        ["Line Chart", "Bar Chart"],
+                        index=0
+                    )
+                    
+                    # Filter data for plotting
+                    if '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'
+                    
+                    # Plot using Streamlit's built-in charts
+                    if graph_type == "Line Chart":
+                        st.line_chart(result_df.set_index(x_column)[y_column])
+                    elif graph_type == "Bar Chart":
+                        st.bar_chart(result_df.set_index(x_column)[y_column])
+                    
+                    # Optional: Use Matplotlib or Plotly for more advanced plots
+                    if st.checkbox("Show Advanced 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: