Update app.py

app.py

@@ -62,6 +62,8 @@ def grab_baseline_stuff():
     fd_raw_proj['Median'] = fd_raw_proj['Median'].astype(float)
     dk_raw_proj['Own'] = dk_raw_proj['Median'].astype(float)
     fd_raw_proj['Own'] = fd_raw_proj['Median'].astype(float)
+    dk_raw_proj['player_id'] = dk_raw_proj['player_id'].astype(str)
+    fd_raw_proj['player_id'] = fd_raw_proj['player_id'].astype(str)
     dk_raw_proj['Name_ID'] = dk_raw_proj['Player'] + ' (' + dk_raw_proj['player_id'].str[:-2] + ')'
     fd_raw_proj['Name_ID'] = fd_raw_proj['player_id'].str[:-2] + ':' + fd_raw_proj['Player']
     dk_ids = dict(zip(dk_raw_proj['Player'], dk_raw_proj['Name_ID']))
@@ -164,19 +166,16 @@ with tab2:
         raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']]
         raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
         raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0)
-        st.session_state.display_init = raw_baselines
-        st.dataframe(st.session_state.display_init.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(raw_baselines),
-                file_name='NFL_proj_export.csv',
-                mime='text/csv',
-            )
+        st.session_state.export_baselines = raw_baselines
+        st.session_state.display_baselines = raw_baselines
+        
         if st.button('Optimize'):
             max_proj = 1000
             max_own = 1000
             total_proj = 0
             total_own = 0
+            display_container = st.empty()
+            display_dl_container = st.empty()
             optimize_container = st.empty()
             download_container = st.empty()
             freq_container = st.empty()
@@ -504,6 +503,10 @@ with tab2:
                     portfolio = portfolio.set_index('Lineup')
                     portfolio = portfolio.drop(columns=['index'])
                     
+                    st.session_state.portfolio = portfolio.drop_duplicates()
+                    
+                    st.session_state.final_outcomes = portfolio
+                    
                     if site_var1 == 'Draftkings':
                         final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'Cost', 'Proj', 'Own']]
                         
@@ -530,6 +533,8 @@ with tab2:
                         final_outcomes_export['D2'].replace(dkid_dict, inplace=True)
                         final_outcomes_export['G'].replace(dkid_dict, inplace=True)
                         final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True)
+                        
+                        st.session_state.final_outcomes_export = final_outcomes_export.copy()
                     elif site_var1 == 'Fanduel':
                         final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'Cost', 'Proj', 'Own']]
                         
@@ -556,8 +561,10 @@ with tab2:
                         final_outcomes_export['UTIL1'].replace(dkid_dict, inplace=True)
                         final_outcomes_export['UTIL2'].replace(dkid_dict, inplace=True)
                         final_outcomes_export['G'].replace(dkid_dict, inplace=True)
+                        
+                        st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
                     
-                    player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:8].values, return_counts=True)),
+                    player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:8].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)
@@ -569,17 +576,46 @@ with tab2:
                     player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
                     player_freq = player_freq.set_index('Player')
     
+                with display_container:
+                    display_container = st.empty()
+                    if 'display_baselines' in st.session_state:
+                        st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        
+                with display_dl_container:
+                            display_dl_container = st.empty()
+                            if 'export_baselines' in st.session_state:
+                                st.download_button(
+                                    label="Export Projections",
+                                    data=convert_df_to_csv(st.session_state.export_baselines),
+                                    file_name='NHL_proj_export.csv',
+                                    mime='text/csv',
+                                )        
+                        
                 with optimize_container:
-                    optimize_container = st.empty()
-                    st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                            optimize_container = st.empty()
+                            if 'final_outcomes' in st.session_state:
+                                st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                
                 with download_container:
                     download_container = st.empty()
-                    st.download_button(
-                        label="Export Optimals",
-                        data=convert_df_to_csv(final_outcomes_export),
-                        file_name='NHL_optimals_export.csv',
-                        mime='text/csv',
-                    )
+                    if site_var1 == 'Draftkings':
+                        if 'final_outcomes_export' in st.session_state:
+                            st.download_button(
+                                label="Export Optimals",
+                                data=convert_df_to_csv(st.session_state.final_outcomes_export),
+                                file_name='NHL_optimals_export.csv',
+                                mime='text/csv',
+                            )
+                    elif site_var1 == 'Fanduel':
+                        if 'FD_final_outcomes_export' in st.session_state:
+                            st.download_button(
+                                label="Export Optimals",
+                                data=convert_df_to_csv(st.session_state.FD_final_outcomes_export),
+                                file_name='FD_NHL_optimals_export.csv',
+                                mime='text/csv',
+                            )
+                
                 with freq_container:
                     freq_container = st.empty()
-                    st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
+                    if 'player_freq' in st.session_state:
+                        st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)