Update app.py

app.py

@@ -67,10 +67,13 @@ def init_baslines():
     raw_display.rename(columns={"name": "Player"}, inplace = True)
     fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
 
-    return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids
+    worksheet = sh.worksheet('Timestamp')
+    timestamp = worksheet.acell('A1').value
 
-dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baslines()
-t_stamp = f"Last Update: " + "idk man recently probably" + f" CST"
+    return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids, timestamp
+
+dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict, timestamp = init_baslines()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 static_exposure = pd.DataFrame(columns=['Player', 'count'])
 overall_exposure = pd.DataFrame(columns=['Player', 'count'])
@@ -430,6 +433,7 @@ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_gro
 tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
 
 with tab1:
+    st.info("The contest sim currently only works for Draftkings, the roster formation for Fanduel is incorrect. It'll be fixed in the next couple of days!")
     with st.container():          
           col1, col2 = st.columns([3, 3])
           
@@ -616,8 +620,8 @@ with tab2:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baslines()
-              t_stamp = f"Last Update: " + "idk man recently probably" + f" CST"
+              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict, timestamp = init_baslines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
               
         slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'User'))
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
@@ -701,7 +705,7 @@ with tab2:
                         # # Apply the calculation to the DataFrame
                         # initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_c if row['Position'] == 'C' else factor_other), axis=1)
                         # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
-                        initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum())
+                        initial_proj['Own'] = initial_proj['Own'] * (900 / initial_proj['Own'].sum())
                         
                         # Drop unnecessary columns and create the final DataFrame
                         Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
@@ -737,6 +741,8 @@ with tab2:
                         UserPortfolio = pd.DataFrame(columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'])
             
                     Overall_Proj.replace('', np.nan, inplace=True)
+                    Overall_Proj = Overall_Proj.replace(',','', regex=True)
+                    Overall_Proj['Salary'] = Overall_Proj['Salary'].astype(int)
                     Overall_Proj = Overall_Proj.dropna(subset=['Median'])
                     Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
                     Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2