Update app.py

app.py

@@ -331,8 +331,7 @@ with tab3:
         elif site_var1 == 'Fanduel':
             min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1')
             max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1')
-    with col2:
-             
+    
         if contest_var1 == 'Small Field GPP':
             if site_var1 == 'Draftkings':
                 ownframe = raw_baselines.copy()
@@ -432,14 +431,17 @@ with tab3:
             flex_proj['roster'] = 'FLEX'
         
         combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True)
+    
+    with col2:
         
-        st.dataframe(display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        st.download_button(
-                label="Export Projections",
-                data=convert_df_to_csv(export_baselines),
-                file_name='NFL_proj_export.csv',
-                mime='text/csv',
-            )
+        with st.container():
+            st.dataframe(display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            st.download_button(
+                    label="Export Projections",
+                    data=convert_df_to_csv(export_baselines),
+                    file_name='NFL_proj_export.csv',
+                    mime='text/csv',
+                )
         
         with st.container():
             if 'portfolio' in st.session_state:
@@ -471,7 +473,7 @@ with tab3:
             x = 1
     
             with st.spinner('Wait for it...'):
-                with optimize_container:
+                with st.container():
     
                         while x <= linenum_var1:
                             sorted_lineup = []
@@ -589,11 +591,6 @@ with tab3:
                             lineup_final['Own'] = lineup_final['Names'].map(player_own)
                             lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0)
                             lineup_final = lineup_final.reset_index(drop=True)
-                            # lineup_final = lineup_final.set_index('Names')
-    
-                            with col2:
-                                with st.container():
-                                    st.table(lineup_final)
     
                             max_proj = total_proj
                             max_own = total_own
@@ -635,7 +632,7 @@ with tab3:
     
                         final_outcomes = portfolio
                         
-                        player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:5].values, return_counts=True)),
+                        player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:5].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                         player_freq['Freq'] = player_freq['Freq'].astype(int)
                         player_freq['Position'] = player_freq['Player'].map(player_pos)
@@ -713,6 +710,4 @@ with tab3:
                             
                             st.session_state.final_outcomes_export = final_outcomes_export.copy()
           
-                        st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
-    
-                
+                        st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]