Update app.py

app.py

@@ -293,6 +293,9 @@ with tab3:
               dk_roo_raw_2 = load_dk_player_projections_2()
               fd_roo_raw = load_fd_player_projections()
               fd_roo_raw_2 = load_fd_player_projections_2()
+              dkid_dict, fdid_dict = set_export_ids()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
         slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var1')
         site_var1 = st.selectbox("What site is the showdown on?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
@@ -329,21 +332,7 @@ with tab3:
             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:
-        
-        with st.container():
-            if 'portfolio' in st.session_state:
-                st.dataframe(st.session_state.portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-            if 'final_outcomes_export' in st.session_state:            
-                st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(st.session_state.final_outcomes_export),
-                    file_name='MLB_optimals_export.csv',
-                    mime='text/csv',
-                )
-        
-        with st.container():
-            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)
+             
         if contest_var1 == 'Small Field GPP':
             if site_var1 == 'Draftkings':
                 ownframe = raw_baselines.copy()
@@ -451,7 +440,25 @@ with tab3:
                 file_name='NFL_proj_export.csv',
                 mime='text/csv',
             )
+        
+        with st.container():
+            if 'portfolio' in st.session_state:
+                st.dataframe(st.session_state.portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            if 'final_outcomes_export' in st.session_state:            
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(st.session_state.final_outcomes_export),
+                    file_name='MLB_optimals_export.csv',
+                    mime='text/csv',
+                )
+        
+        with st.container():
+            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)
+        
         if st.button('Optimize'):
+            for key in st.session_state.keys():
+                del st.session_state[key]
             max_proj = 1000
             max_own = 1000
             total_proj = 0
@@ -708,4 +715,4 @@ with tab3:
           
                         st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
     
-                
+