Update app.py

app.py

@@ -1335,85 +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)
-                
-                    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',
-                            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)
+                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)
+                    
                         st.download_button(
-                            label="Export Exposures",
-                            data=convert_df_to_csv(flex_freq),
-                            file_name='flex_freq_export.csv',
+                            label="Export Tables",
+                            data=convert_df_to_csv(Sim_Winner_Export),
+                            file_name='NFL_consim_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',
+                            )