Update app.py

app.py

@@ -108,35 +108,37 @@ with tab2:
               st.cache_data.clear()
               dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp = grab_baseline_stuff()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              for key in st.session_state.keys():
+                  del st.session_state[key]
               
         slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1')
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
               if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
+                  init_baselines = proj_dataframe
               elif slate_var1 != 'User':
-                  raw_baselines = dk_raw_proj
+                  init_baselines = dk_raw_proj
         elif site_var1 == 'Fanduel':
               if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
+                  init_baselines = proj_dataframe
               elif slate_var1 != 'User':
-                  raw_baselines = fd_raw_proj
+                  init_baselines = fd_raw_proj
         contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP', 'Round Robin'), key='contest_var1')
         if contest_var1 != 'Cash':
-            stack_var1 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1')
+            stack_var1 = st.selectbox('Which team are you stacking?', options = init_baselines['Team'].unique(), key='stack_var1')
             stack_size_var1 = st.selectbox('What size of stack?', options = [3, 4], key='stack_size_var1')
             line_choice_var1 = st.selectbox('Which line for main?', options = [1, 2, 3, 4], key='line_choice_var1')
-            ministack_var1 = st.selectbox('Who should be the secondary stack?', options = raw_baselines['Team'].unique(), key='ministack_var1')
+            ministack_var1 = st.selectbox('Who should be the secondary stack?', options = init_baselines['Team'].unique(), key='ministack_var1')
             ministack_size_var1 = st.selectbox('What size of secondary stack?', options = [2, 3, 4], key='ministack_size_var1')
             miniline_choice_var1 = st.selectbox('Which line for secondary?', options = [1, 2, 3, 4], key='miniline_choice_var1')
             opp_var1 = opp_dict[stack_var1]
         split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
         if split_var1 == 'Specific Games':
-            team_var1 = st.multiselect('Which teams would you like to include in the optimization?', options = raw_baselines['Team'].unique(), key='team_var1')
+            team_var1 = st.multiselect('Which teams would you like to include in the optimization?', options = init_baselines['Team'].unique(), key='team_var1')
         elif split_var1 == 'Full Slate Run':
-            team_var1 = raw_baselines.Team.values.tolist()
-        lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
-        avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var1')
+            team_var1 = init_baselines.Team.values.tolist()
+        lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = init_baselines['Player'].unique(), key='lock_var1')
+        avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = init_baselines['Player'].unique(), key='avoid_var1')
         linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1')
         if site_var1 == 'Draftkings':
             min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
@@ -145,9 +147,9 @@ with tab2:
             min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 54900, value = 54000, step = 100, key='min_sal1')
             max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 55000, value = 55000, step = 100, key='max_sal1')
     with col2:
-        raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var1)]
-        raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var1)]
-        ownframe = raw_baselines.copy()
+        init_baselines = init_baselines[init_baselines['Team'].isin(team_var1)]
+        init_baselines = init_baselines[~init_baselines['Player'].isin(avoid_var1)]
+        ownframe = init_baselines.copy()
         if contest_var1 == 'Cash':
                 ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
                 ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
@@ -166,8 +168,8 @@ 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.export_baselines = raw_baselines
-        st.session_state.display_baselines = raw_baselines
+        st.session_state.export_baselines = raw_baselines.copy()
+        st.session_state.display_baselines = raw_baselines.copy()
         
         if st.button('Optimize'):
             max_proj = 1000
@@ -576,46 +578,46 @@ with tab2:
                     st.session_state.player_freq = st.session_state.player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
                     st.session_state.player_freq = st.session_state.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()
-                            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()
-                    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 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 freq_container:
-                    freq_container = st.empty()
-                    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)
+        with optimize_container:
+                    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()
+            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()
+            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)