Update app.py

app.py

@@ -10,8 +10,6 @@ import pandas as pd
 import streamlit as st
 import gspread
 import pymongo
-import certifi
-ca = certifi.where()
 
 @st.cache_resource
 def init_conn():
@@ -176,66 +174,6 @@ with tab2:
               for key in st.session_state.keys():
                   del st.session_state[key]
               DK_seed, FD_seed, 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'))
-    #     if site_var1 == 'Draftkings':
-    #         raw_baselines = dk_raw
-    #     elif site_var1 == 'Fanduel':
-    #         raw_baselines = fd_raw
-            
-    #     contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
-    #     if contest_var1 == 'Small':
-    #         Contest_Size = 1000
-    #     elif contest_var1 == 'Medium':
-    #         Contest_Size = 5000
-    #     elif contest_var1 == 'Large':
-    #         Contest_Size = 10000
-    #     elif contest_var1 == 'Massive':
-    #         Contest_Size = 100000
-    #     strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
-    #     if strength_var1 == 'Not Very':
-    #         sharp_split = [400000,100000] 
-    #     elif strength_var1 == 'Average':
-    #         sharp_split = [500000,200000]
-    #     elif strength_var1 == 'Very':
-    #         sharp_split = [500000,300000]
     
     with col2:
-        st.write("Things will go here")
-    #     if site_var1 == 'Draftkings':
-    #         st.session_state.Sim_Winner_Frame = DK_seed.head(Contest_Size)
-    #         st.session_state.Sim_Winner_Display = DK_seed.head(Contest_Size)
-    #         st.session_state.Sim_Winner_Export = DK_seed
-    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
-    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
-            
-    #         if 'Sim_Winner_Export' in st.session_state:
-    #                 st.download_button(
-    #                     label="Export 500k optimals",
-    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-    #                     file_name='MLB_consim_export.csv',
-    #                     mime='text/csv',
-    #                 )
-    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
-    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
-    #     elif site_var1 == 'Fanduel':
-    #         st.session_state.Sim_Winner_Frame = FD_seed.head(Contest_Size)
-    #         st.session_state.Sim_Winner_Display = FD_seed.head(Contest_Size)
-    #         st.session_state.Sim_Winner_Export = FD_seed
-    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)),
-    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
-            
-    #         if 'Sim_Winner_Export' in st.session_state:
-    #                 st.download_button(
-    #                     label="Export 500k optimals",
-    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-    #                     file_name='MLB_consim_export.csv',
-    #                     mime='text/csv',
-    #                 )
-    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
-    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+        st.write("Things will go here")