m1

pages/2_Scenario_Planner.py

@@ -53,7 +53,16 @@ for k, v in st.session_state.items():
 # ======================= Functions ====================== #
 # ======================================================== #
 
-    
+def save_report_forecast(forecasted_table_df,forecasted_table_df2):
+    # Convert the DataFrame to an Excel file in memory
+    excel_file = BytesIO()
+    with pd.ExcelWriter(excel_file, engine='openpyxl') as writer:
+        forecasted_table_df.to_excel(writer, index=True, sheet_name='Forecasted Spends')
+        forecasted_table_df2.to_excel(writer, sheet_name='Monthly Breakdown',index = True)
+    # Seek to the beginning of the BytesIO buffer
+    excel_file.seek(0)
+    return excel_file
+   
 def save_ppt_file(summary_df_sorted,fig1,fig2,fig3):
     summary_df_sorted.index = summary_df_sorted["Channel_name"]
     # Initialize PowerPoint presentation
@@ -2211,48 +2220,32 @@ if auth_status == True:
                 start_date1,end_date1 = first_day_of_next_year(datetime.now())
                 # start_date1 = start_date1-  relativedelta(years=1)
                 # end_date1 = end_date1 - relativedelta(years=1)
-            st.write(f"Forecasted Spends Time Period : {start_date1.strftime('%m-%d-%Y')} to {end_date1.strftime('%m-%d-%Y')}")
-            if end_date1 < start_date1 :
-                st.error("End date cannot be less than start date")
-            forecasted_table_df2 = pd.DataFrame()
-            try:
-                
-                st.write("Forecasted Spends wrt. Channels ")
-                output_df1, output_df2 = sf.scenario_spend_forecasting(summary_df_sorted,start_date1-  relativedelta(years=1),end_date1-  relativedelta(years=1))
-                forecasted_table_df   = output_df1.copy()
-                forecasted_table_df.iloc[:2] = forecasted_table_df.iloc[:2].applymap(lambda x: "{:,.0f}".format(x))  
-                forecasted_table_df.iloc[-1] = forecasted_table_df.iloc[-1].apply(lambda x: "{:.1f}%".format(x))
-                st.dataframe(forecasted_table_df)
-
-                st.write("Monthly Breakdown Of Forecasted Spends wrt. Channels ")
-                forecasted_table_df2 = output_df2.applymap(lambda x: "{:,.0f}".format(x))
-                st.dataframe(forecasted_table_df2)
-
-
-            except:
-                st.warning("Please make sure the base data is updated")
-            
-            def save_report_forecast(forecasted_table_df,forecasted_table_df2):
-                
-                # Convert the DataFrame to an Excel file in memory
-                excel_file = BytesIO()
-                with pd.ExcelWriter(excel_file, engine='openpyxl') as writer:
-                    forecasted_table_df.to_excel(writer, index=True, sheet_name='Forecasted Spends')
-                    forecasted_table_df2.to_excel(writer, sheet_name='Monthly Breakdown',index = True)
-                # Seek to the beginning of the BytesIO buffer
-                excel_file.seek(0)
-                return excel_file
-                
-            st.subheader("Download Report")
-            report_name = st.text_input(
-                    "Report name",
-                    key="report_input",
-                    placeholder="Report name",
-                    label_visibility="collapsed",
-                )
-
-            
-            st.download_button(
+            if st.button('Generate Forecasts'):
+                st.write(f"Forecasted Spends Time Period : {start_date1.strftime('%m-%d-%Y')} to {end_date1.strftime('%m-%d-%Y')}")
+                if end_date1 < start_date1 :
+                    st.error("End date cannot be less than start date")
+                forecasted_table_df2 = pd.DataFrame()
+                try:
+                    
+                    st.write("Forecasted Spends wrt. Channels ")
+                    output_df1, output_df2 = sf.scenario_spend_forecasting(summary_df_sorted,start_date1-  relativedelta(years=1),end_date1-  relativedelta(years=1))
+                    forecasted_table_df   = output_df1.copy()
+                    # forecasted_table_df.iloc[:2] = forecasted_table_df.iloc[:2].applymap(lambda x: "{:,.0f}".format(x))  
+                    # forecasted_table_df.iloc[-1] = forecasted_table_df.iloc[-1].apply(lambda x: "{:.1f}%".format(x))
+                    st.dataframe(forecasted_table_df)
+
+                    st.write("Monthly Breakdown Of Forecasted Spends wrt. Channels ")
+                    # forecasted_table_df2 = output_df2.applymap(lambda x: "{:,.0f}".format(x))
+                    st.dataframe(output_df2)
+                    
+                    st.subheader("Download Report")
+                    report_name = st.text_input(
+                            "Report name",
+                            key="report_input",
+                            placeholder="Report name",
+                            label_visibility="collapsed",
+                        )
+                    st.download_button(
                     "Download Report",
                     data = save_report_forecast(output_df1,output_df2),
                     file_name = report_name+".xlsx",
@@ -2260,6 +2253,16 @@ if auth_status == True:
                     # on_click=lambda: save_report_forecast(forecasted_table_df,report_name),
                     disabled=len(st.session_state["report_input"]) == 0,#use_container_width=True
                     )
+
+                except:
+                    st.warning("Please make sure the base data is updated")
+            
+           
+                
+            
+
+            
+            
             
             
 

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:576e0d173ca100c08b5b592f57ac43bc52a32ffaa46cd1b542e5cbe1690f045c
+oid sha256:3903995f3ab1eb9b34db90db9d8177955cff0a37af45969c97543cc82909a170
 size 1822