Update app.py

app.py

@@ -46,17 +46,6 @@ percentages_format = {'Exposure': '{:.2%}'}
 @st.cache_data(ttl = 600)
 def init_baselines():
     sh = gcservice_account.open_by_url(MLB_Data)
-    collection = db["DK_MLB_seed_frame"] 
-    cursor = collection.find()
-    
-    raw_display = pd.DataFrame(list(cursor))
-    DK_seed = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]
-
-    collection = db["FD_MLB_seed_frame"] 
-    cursor = collection.find()
-    
-    raw_display = pd.DataFrame(list(cursor))
-    FD_seed = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]
     
     worksheet = sh.worksheet('DK_Projections')
     load_display = pd.DataFrame(worksheet.get_all_records())
@@ -70,9 +59,29 @@ def init_baselines():
     
     fd_raw = load_display.dropna(subset=['Median'])
 
-    return DK_seed, FD_seed, dk_raw, fd_raw
+    return dk_raw, fd_raw
+
+@st.cache_data(ttl = 600)
+def init_DK_seed_frame():
+    collection = db["DK_MLB_seed_frame"] 
+    cursor = collection.find()
+    
+    raw_display = pd.DataFrame(list(cursor))
+    DK_seed = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]
+
+    return DK_seed
+
+@st.cache_data(ttl = 600)
+def init_FD_seed_frame():
+    collection = db["FD_MLB_seed_frame"] 
+    cursor = collection.find()
+    
+    raw_display = pd.DataFrame(list(cursor))
+    FD_seed = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]
+
+    return FD_seed
 
-DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
+dk_raw, fd_raw = init_baselines()
 
 tab1, tab2 = st.tabs(['Data Export', 'Contest Sims'])
 with tab1:
@@ -82,7 +91,7 @@ with tab1:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
+              dk_raw, fd_raw = init_baselines()
               
         slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
@@ -91,74 +100,73 @@ with tab1:
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
-                    team_var2 = st.multiselect('Which teams do you want?', options = DK_seed['Team'].unique())
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
             elif team_var1 == 'Full Slate':
-                    team_var2 = DK_seed.Team.values.tolist()
+                    team_var2 = dk_raw.Team.values.tolist()
             
             stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
             if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = DK_seed['Team_count'].unique())
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
             elif stack_var1 == 'Full Slate':
-                    stack_var2 = DK_seed.Team_count.values.tolist()
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
                     
         elif site_var1 == 'Fanduel':
             raw_baselines = fd_raw
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
-                    team_var2 = st.multiselect('Which teams do you want?', options = FD_seed['Team'].unique())
+                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
             elif team_var1 == 'Full Slate':
-                    team_var2 = FD_seed.Team.values.tolist()
+                    team_var2 = fd_raw.Team.values.tolist()
             
             stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
             if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = FD_seed['Team_count'].unique())
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
             elif stack_var1 == 'Full Slate':
-                    stack_var2 = FD_seed.Team_count.values.tolist()
+                    stack_var2 = [4, 3, 2, 1, 0]
             
-        
-
-        
-    
     with col2:
-        if site_var1 == 'Draftkings':
-            DK_seed_parse = DK_seed[DK_seed['Team'].isin(team_var2)]
-            DK_seed_parse = DK_seed_parse[DK_seed_parse['Team_count'].isin(stack_var2)]
-            st.session_state.data_export_display = DK_seed_parse.head(1000)
-            st.session_state.data_export = DK_seed_parse
-            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:9].values, return_counts=True)),
-                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
-            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(DK_seed_parse['Team']))
-            
-            if 'data_export' in st.session_state:
-                    st.download_button(
-                        label="Export optimals set",
-                        data=st.session_state.data_export.to_csv().encode('utf-8'),
-                        file_name='MLB_optimals_export.csv',
-                        mime='text/csv',
-                    )
-            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
-            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
-        elif site_var1 == 'Fanduel':
-            FD_seed_parse = FD_seed[FD_seed['Team'].isin(team_var2)]
-            FD_seed_parse = FD_seed_parse[FD_seed_parse['Team_count'].isin(stack_var2)]
-            st.session_state.data_export_display = FD_seed_parse.head(1000)
-            st.session_state.data_export = FD_seed_parse
-            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:8].values, return_counts=True)),
-                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
-            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(FD_seed_parse['Team']))
-            
-            if 'data_export' in st.session_state:
-                    st.download_button(
-                        label="Export optimals set",
-                        data=st.session_state.data_export.to_csv().encode('utf-8'),
-                        file_name='MLB_optimals_export.csv',
-                        mime='text/csv',
-                    )
-            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
-            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+        if st.button("Load Seed Frame", key='seed_frame_load'):
+            if site_var1 == 'Draftkings':
+                DK_seed = init_DK_seed_frame()
+                DK_seed_parse = DK_seed[DK_seed['Team'].isin(team_var2)]
+                DK_seed_parse = DK_seed_parse[DK_seed_parse['Team_count'].isin(stack_var2)]
+                st.session_state.data_export_display = DK_seed_parse.head(1000)
+                st.session_state.data_export = DK_seed_parse
+                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
+                st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(DK_seed_parse['Team']))
+                
+                if 'data_export' in st.session_state:
+                        st.download_button(
+                            label="Export optimals set",
+                            data=st.session_state.data_export.to_csv().encode('utf-8'),
+                            file_name='MLB_optimals_export.csv',
+                            mime='text/csv',
+                        )
+                st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
+                st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+            elif site_var1 == 'Fanduel':
+                FD_seed = init_DK_seed_frame()
+                FD_seed_parse = FD_seed[FD_seed['Team'].isin(team_var2)]
+                FD_seed_parse = FD_seed_parse[FD_seed_parse['Team_count'].isin(stack_var2)]
+                st.session_state.data_export_display = FD_seed_parse.head(1000)
+                st.session_state.data_export = FD_seed_parse
+                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
+                st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(FD_seed_parse['Team']))
+                
+                if 'data_export' in st.session_state:
+                        st.download_button(
+                            label="Export optimals set",
+                            data=st.session_state.data_export.to_csv().encode('utf-8'),
+                            file_name='MLB_optimals_export.csv',
+                            mime='text/csv',
+                        )
+                st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
+                st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
             
 with tab2:
     col1, col2 = st.columns([1, 7])