updated showdown ROO feed

app.py

@@ -8,28 +8,42 @@ import time
 
 @st.cache_resource
 def init_conn():
-          scope = ['https://www.googleapis.com/auth/spreadsheets',
-                    "https://www.googleapis.com/auth/drive"]
-          
-          credentials = {
-            "type": "service_account",
-            "project_id": "sheets-api-connect-378620",
-            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
-            "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-            "client_id": "106625872877651920064",
-            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-            "token_uri": "https://oauth2.googleapis.com/token",
-            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-            "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-          }
-
-          gc = gspread.service_account_from_dict(credentials)
-          return gc
-
-st.set_page_config(layout="wide")
-
-gc = init_conn()
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": st.secrets['model_sheets_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+        
+        credentials2 = {
+          "type": "service_account",
+          "project_id": "sheets-api-connect-378620",
+          "private_key_id": st.secrets['sheets_api_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
+          "client_id": "106625872877651920064",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+        }
+     
+        NBA_Data = st.secrets['NBA_Data']
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, NBA_Data
+    
+gcservice_account, gcservice_account2, NBA_Data = init_conn()
 
 game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
               'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
@@ -42,11 +56,17 @@ player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_fi
 
 expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
 
-all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=269599640'
-
 @st.cache_resource(ttl=30)
 def init_load():
-    sh = gc.open_by_url(all_dk_player_projections)
+    sh = gc.open_by_url(NBA_Data)
+    worksheet = sh.worksheet('Player_Level_SD_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Fantasy'])
+    raw_display.rename(columns={"Name": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own']]
+    roo_raw = raw_display.loc[raw_display['Median'] > 0]
+
     worksheet = sh.worksheet('DK_SD_Build')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
@@ -79,592 +99,66 @@ def init_load():
     raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'Minutes']]
     fd_roo_raw_2 = raw_display.loc[raw_display['Median'] > 0]
 
-    worksheet = sh.worksheet('DK_SD_CSV')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"ID": "player_id"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Player'])
-    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
-    
-    worksheet = sh.worksheet('FD_SD_Build')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"Fantasy": "Median", "Name": "Player", "Raw": "player_id"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Median'])
-    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    timestamp = roo_raw['timestamp'].values[0]
 
-    return dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, dk_ids, fd_ids
+    return roo_raw, dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, timestamp
 
-dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, dkid_dict, fd_ids = init_load()
+roo_raw, dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, t_stamp = init_load()
 
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-tab1, tab2, tab3 = st.tabs(['Uploads and Info', 'Range of Outcomes', 'Optimizer'])
-
-with tab1:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', and 'Own'. For the purposes of this showdown optimizer, only include FLEX positions, salaries, and medians. The optimizer logic will handle the rest!")
-    col1, col2 = st.columns([1, 5])
-
-    with col1:
-        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
-    
-        if proj_file is not None:
-                  try:
-                            proj_dataframe = pd.read_csv(proj_file)
-                            proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0]
-                            try: 
-                                  proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float)
-                            except:
-                                  pass
-                  except:
-                            proj_dataframe = pd.read_excel(proj_file)
-                            proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0]
-                            try: 
-                                  proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float)
-                            except:
-                                  pass
-    with col2:
-        if proj_file is not None:  
-                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-
-with tab2:
-    col1, col2 = st.columns([1, 5])
-    with col1:
-        if st.button("Load/Reset Data", key='reset2'):
-              st.cache_data.clear()
-              dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, dkid_dict, fd_ids = init_load()
-        slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var2')
-        site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
-        if slate_var2 == 'User':
-            raw_baselines = proj_dataframe
-        elif slate_var2 != 'User':
-            if site_var2 == 'Draftkings':
-                if slate_var2 == 'Paydirt (Main)':
-                    raw_baselines = dk_roo_raw
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = dk_roo_raw_2
-            elif site_var2 == 'Fanduel':
-                if slate_var2 == 'Paydirt (Main)':
-                    raw_baselines = fd_roo_raw
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = fd_roo_raw_2
-    
-    with col2:
-        hold_container = st.empty()
-        if st.button('Create Range of Outcomes for Slate'):
-            with hold_container:
-                working_roo = raw_baselines
-                own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
-                opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
-                team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                total_sims = 1000
-
-                flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
-                flex_file.rename(columns={"Agg": "Median"}, inplace = True)
-                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                flex_file['STD'] = (flex_file['Median']/4)
-                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                hold_file = flex_file
-                overall_file = flex_file
-                salary_file = flex_file
-  
-                overall_players = overall_file[['Player']]
-  
-                for x in range(0,total_sims):    
-                    salary_file[x] = salary_file['Salary']
-  
-                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                salary_file.astype('int').dtypes
-  
-                salary_file = salary_file.div(1000)
-  
-                for x in range(0,total_sims):    
-                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-  
-                overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                overall_file.astype('int').dtypes
-  
-                players_only = hold_file[['Player']]
-                raw_lineups_file = players_only
-  
-                for x in range(0,total_sims):
-                    maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
-                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
-  
-                players_only=players_only.drop(['Player'], axis=1)
-                players_only.astype('int').dtypes
-  
-                salary_2x_check = (overall_file - (salary_file*2))
-                salary_3x_check = (overall_file - (salary_file*3))
-                salary_4x_check = (overall_file - (salary_file*4))
-  
-                players_only['Average_Rank'] = players_only.mean(axis=1)
-                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-  
-                players_only['Player'] = hold_file[['Player']]
-  
-                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-  
-                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
-                final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
-                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                final_Proj['LevX'] = 0
-                final_Proj['LevX'] = final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank']
-                final_Proj['CPT_Own'] = final_Proj['Own'] / 4
-                final_Proj['CPT_Proj'] = final_Proj['Median'] * 1.5 
-                final_Proj['CPT_Salary'] = final_Proj['Salary'] * 1.5       
-  
-                display_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
-                display_Proj = display_Proj.set_index('Player')
-                display_Proj = display_Proj.sort_values(by='Median', ascending=False)
-  
-            with hold_container:
-                hold_container = st.empty()
-                display_Proj = display_Proj
-                st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
-  
-            st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(final_Proj),
-                    file_name='Custom_NFL_overall_export.csv',
-                    mime='text/csv',
-            )
-
-with tab3:
-    col1, col2 = st.columns([1, 5])
-    with col1:
-        if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, dkid_dict, fd_ids = init_load()
-              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':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif slate_var1 == 'Paydirt (Main)':
-                  raw_baselines = dk_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  raw_baselines = dk_roo_raw_2
-        elif site_var1 == 'Fanduel':
-              if slate_var1 == 'User':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = proj_dataframe
-              elif slate_var1 == 'Paydirt (Main)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw_2
-        contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
-        lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
-        lock_var2 = st.multiselect("Are there any players you want to use in all lineups in the FLEX (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var2')
-        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')
-        trim_choice1 = st.selectbox("Allow overowned lineups?", options = ['Yes', 'No'])
-        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 trim_choice1 == 'Yes':
-            trim_var1 = 0
-        elif trim_choice1 == 'No':
-            trim_var1 = 1
-        if site_var1 == 'Draftkings':
-            min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
-            max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1')
-        elif site_var1 == 'Fanduel':
-            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')
-    
-        if site_var1 == 'Draftkings':
-            ownframe = raw_baselines.copy()
-            ownframe['Own'] = ownframe['Own'] * (500 / ownframe['Own'].sum())
-        elif site_var1 == 'Fanduel':
-            ownframe = raw_baselines.copy()
-            ownframe['Own'] = ownframe['Own'] * (400 / ownframe['Own'].sum())
-
-        export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
-        export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5
-        export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5
-        display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
-        display_baselines['CPT Own'] = display_baselines['Own'] / 4
-        display_baselines = display_baselines.sort_values(by='Median', ascending=False)
-        display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0)
-        display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0)
-          
-        st.session_state.display_baselines = display_baselines.copy()
-        st.session_state.export_baselines = export_baselines.copy()
-        
-        index_check = pd.DataFrame()
-        flex_proj = pd.DataFrame()
-        cpt_proj = pd.DataFrame()
-        
-        if site_var1 == 'Draftkings':
-            cpt_proj['Player'] = display_baselines['Player']
-            cpt_proj['Salary'] = display_baselines['Salary'] * 1.5
-            cpt_proj['Position'] = display_baselines['Position']
-            cpt_proj['Team'] = display_baselines['Team']
-            cpt_proj['Opp'] = display_baselines['Opp']
-            cpt_proj['Median'] = display_baselines['Median'] * 1.5
-            cpt_proj['Own'] = display_baselines['CPT Own']
-            cpt_proj['lock'] = display_baselines['cpt_lock']
-            cpt_proj['roster'] = 'CPT'
-            if len(lock_var1) > 0:
-                cpt_proj = cpt_proj[cpt_proj['lock'] == 1]
-            if len(lock_var2) > 0:
-                cpt_proj = cpt_proj[~cpt_proj['Player'].isin(lock_var2)]
-            
-            flex_proj['Player'] = display_baselines['Player']
-            flex_proj['Salary'] = display_baselines['Salary']
-            flex_proj['Position'] = display_baselines['Position']
-            flex_proj['Team'] = display_baselines['Team']
-            flex_proj['Opp'] = display_baselines['Opp']
-            flex_proj['Median'] = display_baselines['Median']
-            flex_proj['Own'] = display_baselines['Own']
-            flex_proj['lock'] = display_baselines['lock']
-            flex_proj['roster'] = 'FLEX'
-        elif site_var1 == 'Fanduel':
-            cpt_proj['Player'] = display_baselines['Player']
-            cpt_proj['Salary'] = display_baselines['Salary']
-            cpt_proj['Position'] = display_baselines['Position']
-            cpt_proj['Team'] = display_baselines['Team']
-            cpt_proj['Opp'] = display_baselines['Opp']
-            cpt_proj['Median'] = display_baselines['Median'] * 1.5
-            cpt_proj['Own'] = display_baselines['CPT Own'] *.75
-            cpt_proj['lock'] = display_baselines['cpt_lock']
-            cpt_proj['roster'] = 'CPT'
-            
-            flex_proj['Player'] = display_baselines['Player']
-            flex_proj['Salary'] = display_baselines['Salary']
-            flex_proj['Position'] = display_baselines['Position']
-            flex_proj['Team'] = display_baselines['Team']
-            flex_proj['Opp'] = display_baselines['Opp']
-            flex_proj['Median'] = display_baselines['Median']
-            flex_proj['Own'] = display_baselines['Own']
-            flex_proj['lock'] = display_baselines['lock']
-            flex_proj['roster'] = 'FLEX'
+col1, col2 = st.columns([1, 9])
+
+with col1:
+    st.info(t_stamp)
+    if st.button("Load/Reset Data", key='reset1'):
+        st.cache_data.clear()
+        dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = init_load()
+        t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+        for key in st.session_state.keys():
+            del st.session_state[key]
+    site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
+    if site_var2 == 'Draftkings':
+        site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
+    elif site_var2 == 'Fanduel':
+        site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
+    slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Showdown #1', 'Showdown #2'), key='slate_split')
+    if slate_split == 'Showdown #1':
+        raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
+    elif slate_split == 'Showdown #2':
+        raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
+    split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
+    if split_var2 == 'Specific Games':
+        team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
+    elif split_var2 == 'Full Slate Run':
+        team_var2 = raw_baselines.Team.values.tolist()
+    pos_var2 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2')
+
+with col2:
+    display_container_1 = st.empty()
+    display_dl_container_1 = st.empty()
+    display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
+    display_proj = display_proj.drop(columns=['site', 'version', 'slate', 'timestamp'])
+    
+    st.session_state.display_proj = display_proj
         
-        combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True)
-    
-    with col2:
+    with display_container_1:
         display_container = st.empty()
-        display_dl_container = st.empty()
-        optimize_container = st.empty()
-        download_container = st.empty()
-        freq_container = st.empty()
-        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
-            total_own = 0
-            display_container = st.empty()
+        if 'display_proj' in st.session_state:
+            if pos_var2 == 'All':
+                st.session_state.display_proj = st.session_state.display_proj
+            elif pos_var2 != 'All':
+                st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
+            st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
+    
+    with display_dl_container_1:
             display_dl_container = st.empty()
-            optimize_container = st.empty()
-            download_container = st.empty()
-            freq_container = st.empty()
-            lineup_display = []
-            check_list = []
-            lineups = []
-            portfolio = pd.DataFrame()
-            x = 1
-    
-            with st.spinner('Wait for it...'):
-                with optimize_container:
-    
-                        while x <= linenum_var1:
-                            sorted_lineup = []
-                            p_used = []
-                            
-                            raw_proj_file = combo_file
-                            raw_flex_file = raw_proj_file.dropna(how='all')
-                            raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
-                            flex_file = raw_flex_file
-                            flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
-                            flex_file['name_var'] = flex_file['Player']
-                            flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var2), 1, 0)
-                            flex_file = flex_file[~flex_file['Player'].isin(avoid_var1)]
-                            flex_file['Player'] = np.where(flex_file['roster'] == 'CPT', flex_file['Player'] + ' - CPT', flex_file['Player'] + ' - FLEX')
-                            player_ids = flex_file.index
-    
-                            overall_players = flex_file[['Player']]
-                            overall_players['player_var_add'] = flex_file.index
-                            overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
-    
-                            player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
-                            total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
-                            player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
-                            player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
-    
-                            player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
-                            player_team = dict(zip(flex_file['Player'], flex_file['Team']))
-                            player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
-                            player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
-                            player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
-    
-                            obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-    
-                            obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                            obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
-    
-                            obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
-                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
-                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
-    
-                            if site_var1 == 'Draftkings':
-                                
-                                for flex in flex_file['lock'].unique():
-                                    sub_idx = flex_file[flex_file['lock'] == 1].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "CPT"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "FLEX"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
-                                    
-                                for playerid in player_ids:
-                                    total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
-                                                       (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
-    
-                            elif site_var1 == 'Fanduel':
-                                
-                                for flex in flex_file['lock'].unique():
-                                    sub_idx = flex_file[flex_file['lock'] == 1].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
-                                
-                                for flex in flex_file['Position'].unique():
-                                    sub_idx = flex_file[flex_file['Position'] != "Var"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "CPT"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
-                                
-                                for playerid in player_ids:
-                                    total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
-                                                       (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
-    
-                            player_count = []
-                            player_trim = []
-                            lineup_list = []
-                            
-                            if contest_var1 == 'Cash':
-                                obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
-                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
-                            elif contest_var1 != 'Cash':
-                                obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
-                            if trim_var1 == 1:
-                                total_score += pulp.lpSum([player_vars[idx]*obj_own_max[idx] for idx in flex_file.index]) <= max_own - .001
-                            
-                            total_score.solve()
-                            for v in total_score.variables():
-                                if v.varValue > 0:
-                                    lineup_list.append(v.name)
-                            df = pd.DataFrame(lineup_list)
-                            df['Names'] = df[0].map(player_match)
-                            df['Cost'] = df['Names'].map(player_sal)
-                            df['Proj'] = df['Names'].map(player_proj)
-                            df['Own'] = df['Names'].map(player_own)
-                            total_cost = sum(df['Cost'])
-                            total_own = sum(df['Own'])
-                            total_proj = sum(df['Proj'])
-                            lineup_raw = pd.DataFrame(lineup_list)
-                            lineup_raw['Names'] = lineup_raw[0].map(player_match)
-                            lineup_raw['value'] = lineup_raw[0].map(player_index_match)
-                            lineup_final = lineup_raw.sort_values(by=['value'])
-                            del lineup_final[lineup_final.columns[0]]
-                            del lineup_final[lineup_final.columns[1]]
-                            lineup_final['Team'] = lineup_final['Names'].map(player_team)
-                            lineup_final['Position'] = lineup_final['Names'].map(player_pos)
-                            lineup_final['Salary'] = lineup_final['Names'].map(player_sal)
-                            lineup_final['Proj'] = lineup_final['Names'].map(player_proj)
-                            lineup_final['Own'] = lineup_final['Names'].map(player_own)
-                            lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0)
-                            lineup_final = lineup_final.reset_index(drop=True)
-    
-                            max_proj = total_proj
-                            max_own = total_own
-                            
-                            if site_var1 == 'Draftkings':
-                                if len(lineup_final) == 7:
-                                    port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
-                                    
-                                    port_display['Cost'] = total_cost
-                                    port_display['Proj'] = total_proj
-                                    port_display['Own'] = total_own
-                                    st.table(port_display)
-        
-                                    portfolio = pd.concat([portfolio, port_display], ignore_index = True)
-                            elif site_var1 == 'Fanduel':
-                                if len(lineup_final) == 6:
-                                    port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
-                                    
-                                    port_display['Cost'] = total_cost
-                                    port_display['Proj'] = total_proj
-                                    port_display['Own'] = total_own
-                                    st.table(port_display)
-        
-                                    portfolio = pd.concat([portfolio, port_display], ignore_index = True)
-    
-                            x += 1
-    
-                        if site_var1 == 'Draftkings':
-                            portfolio.rename(columns={0: "CPT", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4", 5: "FLEX5"}, inplace = True)
-                        elif site_var1 == 'Fanduel':
-                            portfolio.rename(columns={0: "MVP", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4"}, inplace = True)
-                        portfolio = portfolio.dropna()
-                        portfolio = portfolio.reset_index()
-                        portfolio['Lineup_num'] = portfolio['index'] + 1
-                        portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
-                        portfolio = portfolio.set_index('Lineup')
-                        portfolio = portfolio.drop(columns=['index'])
-                        st.session_state.portfolio = portfolio.drop_duplicates()
-    
-                        final_outcomes = portfolio
-                        st.session_state.final_outcomes = portfolio
-                        
-                        player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:5].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        player_freq['Freq'] = player_freq['Freq'].astype(int)
-                        player_freq['Position'] = player_freq['Player'].map(player_pos)
-                        player_freq['Salary'] = player_freq['Player'].map(player_sal)
-                        player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
-                        player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
-                        player_freq['Team'] = player_freq['Player'].map(player_team)
-                        
-                        final_outcomes_export = pd.DataFrame()
-                        split_portfolio = pd.DataFrame()
-                        
-                        if site_var1 == 'Draftkings':
-                            
-                            split_portfolio[['CPT', 'CPT_ID']] = final_outcomes.CPT.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX5', 'FLEX5_ID']] = final_outcomes.FLEX5.str.split("-", n=1, expand = True)
-  
-                            split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
-                            split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                            split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                            split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                            split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                            split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
-                            
-                            final_outcomes_export['CPT'] = split_portfolio['CPT']
-                            final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
-                            final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
-                            final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
-                            final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
-                            final_outcomes_export['FLEX5'] = split_portfolio['FLEX5']
-                            
-                            final_outcomes_export['CPT'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX1'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX2'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX3'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX4'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX5'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['Salary'] = final_outcomes['Cost']
-                            final_outcomes_export['Own'] = final_outcomes['Own']
-                            final_outcomes_export['Proj'] = final_outcomes['Proj']
-                            
-                            st.session_state.final_outcomes_export = final_outcomes_export.copy()
-                            
-                        elif site_var1 == 'Fanduel':
-                            
-                            # split_portfolio[['MVP', 'CPT_ID']] = final_outcomes.MVP.str.split("-", n=1, expand = True)
-                            # split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
-                            # split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
-                            # split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
-                            # split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
-  
-                            # split_portfolio['MVP'] = split_portfolio['MVP'].str.strip()
-                            # split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                            # split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                            # split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                            # split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                            
-                            # final_outcomes_export['MVP'] = split_portfolio['MVP']
-                            # final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
-                            # final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
-                            # final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
-                            # final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
-                            
-                            # final_outcomes_export['MVP'].replace(fdid_dict, inplace=True)
-                            # final_outcomes_export['FLEX1'].replace(fdid_dict, inplace=True)
-                            # final_outcomes_export['FLEX2'].replace(fdid_dict, inplace=True)
-                            # final_outcomes_export['FLEX3'].replace(fdid_dict, inplace=True)
-                            # final_outcomes_export['FLEX4'].replace(fdid_dict, inplace=True)
-                            # final_outcomes_export['Salary'] = final_outcomes['Cost']
-                            # final_outcomes_export['Own'] = final_outcomes['Own']
-                            # final_outcomes_export['Proj'] = final_outcomes['Proj']
-                            
-                            st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
-          
-                        st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
-        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='showdown_proj_export.csv',
+            if 'display_proj' in st.session_state:
+                st.download_button(
+                            label="Export Tables",
+                            data=convert_df_to_csv(st.session_state.display_proj),
+                            file_name='NBA_SD_ROO_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='NBA_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_NBA_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)
-                
+                )