Update app.py

app.py

@@ -890,6 +890,7 @@ with tab2:
             
     with col2:
         if st.button("Simulate Contest"):
+            sim_done = 0
             try:
                 del dst_freq
                 del flex_freq
@@ -1223,6 +1224,8 @@ with tab2:
                 # Data Copying
                 Sim_Winner_Export = Sim_Winner_Frame.copy()
                 
+                sim_done = 1
+                
                 # Conditional Replacement
                 columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
                 
@@ -1332,85 +1335,85 @@ with tab2:
                                     dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
 
                 dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+            if sim_done == 1:
+                with st.container():
+                    player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
+                    if player_split_var2 == 'Specific Players':
+                              find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
+                    elif player_split_var2 == 'Full Players':
+                              find_var2 = static_exposure.Player.values.tolist()
+                    if player_split_var2 == 'Specific Players':
+                              Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+                    elif player_split_var2 == 'Full Players':
+                              Sim_Winner_Frame = Sim_Winner_Frame
+                              
+                with st.container():
+                    display_winner_dataframe = Sim_Winner_Frame.copy()
+                    st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
                 
-            with st.container():
-                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
-                if player_split_var2 == 'Specific Players':
-                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
-                elif player_split_var2 == 'Full Players':
-                          find_var2 = static_exposure.Player.values.tolist()
-                if player_split_var2 == 'Specific Players':
-                          Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
-                elif player_split_var2 == 'Full Players':
-                          Sim_Winner_Frame = Sim_Winner_Frame
-                          
-            with st.container():
-                display_winner_dataframe = Sim_Winner_Frame.copy()
-                st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-            
-                st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(Sim_Winner_Export),
-                    file_name='NFL_consim_export.csv',
-                    mime='text/csv',
-                )
-            
-            with st.container():
-                freq_container = st.empty()
-                tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
-                with tab1:
-                    st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(player_freq),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab2:
-                    st.dataframe(qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(qb_freq),
-                        file_name='qb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab3:
-                    st.dataframe(rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                     st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(rb_freq),
-                        file_name='rb_freq_export.csv',
+                        label="Export Tables",
+                        data=convert_df_to_csv(Sim_Winner_Export),
+                        file_name='NFL_consim_export.csv',
                         mime='text/csv',
                     )
-                with tab4:
-                    st.dataframe(wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(wr_freq),
-                        file_name='wr_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab5:
-                    st.dataframe(te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(te_freq),
-                        file_name='te_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab6:
-                    st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(flex_freq),
-                        file_name='flex_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab7:
-                    st.dataframe(dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(dst_freq),
-                        file_name='dst_freq_export.csv',
-                        mime='text/csv',
-                    )
+                
+                with st.container():
+                    freq_container = st.empty()
+                    tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
+                    with tab1:
+                        st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(player_freq),
+                            file_name='player_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab2:
+                        st.dataframe(qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(qb_freq),
+                            file_name='qb_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab3:
+                        st.dataframe(rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(rb_freq),
+                            file_name='rb_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab4:
+                        st.dataframe(wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(wr_freq),
+                            file_name='wr_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab5:
+                        st.dataframe(te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(te_freq),
+                            file_name='te_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab6:
+                        st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(flex_freq),
+                            file_name='flex_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab7:
+                        st.dataframe(dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                        st.download_button(
+                            label="Export Exposures",
+                            data=convert_df_to_csv(dst_freq),
+                            file_name='dst_freq_export.csv',
+                            mime='text/csv',
+                        )