optimizing for tabs to segmentation, UI upgrades, and some optimizations around segmented queries

src/database.py

@@ -5,26 +5,10 @@ import os
 
 @st.cache_resource
 def 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": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
-          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\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"
-        }
         uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
         db = client["testing_db"]
-
-        gc_con = gspread.service_account_from_dict(credentials, scope)
       
-        return gc_con, db
+        return db
     
-gcservice_account, db = init_conn()
+db = init_conn()

src/streamlit_app.py

@@ -13,84 +13,170 @@ import plotly.graph_objects as go
 import plotly.io as pio
 import certifi
 ca = certifi.where()
-from database import gcservice_account, db
+from database import db
 
 NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
 
 percentages_format = {'PG': '{:.2%}', 'SG': '{:.2%}', 'SF': '{:.2%}', 'PF': '{:.2%}', 'C': '{:.2%}'}
 
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stElementContainer [data-baseweb="button-group"] {
+        gap: 2.000rem;
+        padding: 4px;
+    }
+    .stElementContainer [kind="segmented_control"] {
+        height: 2.000rem;
+        white-space: pre-wrap;
+        background-color: #DAA520;
+        color: white;
+        border-radius: 20px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+    .stElementContainer [kind="segmented_controlActive"] {
+        height: 3.000rem;
+        background-color: #DAA520;
+        border: 3px solid #FFD700;
+        border-radius: 10px;
+        color: black;
+    }
+    .stElementContainer [kind="segmented_control"]:hover {
+        background-color: #FFD700;
+        cursor: pointer;
+    }
+
+    div[data-baseweb="select"] > div {
+        background-color: #DAA520;
+        color: white;
+    }
+
+</style>""", unsafe_allow_html=True)
+
 @st.cache_resource(ttl = 599)
-def init_baselines():
-    sh = gcservice_account.open_by_url(NBA_Data)
-    collection = db["gamelog"] 
-    cursor = collection.find()  # Finds all documents in the collection
-    
-    raw_display = pd.DataFrame(list(cursor))
-    gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
-    gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'GAME_ID', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
-                                   'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
-                                   'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM']]
-    gamelog_table['assists'].replace("", 0, inplace=True)
-    gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
-    gamelog_table['passes'].replace("", 0, inplace=True)
-    gamelog_table['touches'].replace("", 0, inplace=True)
-    gamelog_table['MIN'].replace("", 0, inplace=True)
-    gamelog_table['Fantasy'].replace("", 0, inplace=True)
-    gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
-    gamelog_table['FPPM'].replace("", 0, inplace=True)
-    gamelog_table['REB'] = gamelog_table['REB'].astype(int)
-    gamelog_table['assists'] = gamelog_table['assists'].astype(int)
-    gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
-    gamelog_table['passes'] = gamelog_table['passes'].astype(int)
-    gamelog_table['touches'] = gamelog_table['touches'].astype(int)
-    gamelog_table['MIN'] = gamelog_table['MIN'].astype(int)
-    gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
-    gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
-    gamelog_table['FPPM'] = gamelog_table['FPPM'].astype(float)
-    gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
-    gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
-    gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN']
-    gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
-    gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
-    data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
-    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    gamelog_table['team_score'] = gamelog_table.groupby(['TEAM_NAME', 'GAME_ID'], sort=False)['PTS'].transform('sum')
-    gamelog_table['opp_score'] = gamelog_table.groupby(['GAME_ID'], sort=False)['PTS'].transform('sum') - gamelog_table['team_score']
-    gamelog_table['spread'] = (gamelog_table['opp_score'] - gamelog_table['team_score']).abs()
-    gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
-    
-    spread_dict = dict(zip(gamelog_table['GAME_ID'], gamelog_table['spread']))
-    
-    gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'game_id', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
-                                            'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
-                                            'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM',
-                                            'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch', 'team_score', 'opp_score', 'spread'], axis=1)
-    
-    worksheet = sh.worksheet('Rotations')
-    raw_display = pd.DataFrame(worksheet.get_values())
-    raw_display.columns = raw_display.iloc[0]
-    raw_display = raw_display[1:]
-    raw_display = raw_display.reset_index(drop=True)
-    rot_table = raw_display[raw_display['Player'] != ""]
-    rot_table = rot_table[['Player', 'Team', 'PG', 'SG', 'SF', 'PF', 'C', 'Given_Pos']]
-    data_cols = ['PG', 'SG', 'SF', 'PF', 'C']
-    rot_table[data_cols] = rot_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    rot_table = rot_table[rot_table['Player'] != 0]
-    
-    collection = db["rotations"] 
-    cursor = collection.find()  # Finds all documents in the collection
-    
-    raw_display = pd.DataFrame(list(cursor))
-    game_rot = raw_display[raw_display['PLAYER_NAME'] != ""]
-    data_cols = game_rot.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_ABBREVIATION', 'OPP_ABBREVIATION', 'TEAM_NAME', 'OPP_NAME', 'GAME_DATE',
-                                       'MATCHUP', 'WL', 'backlog_lookup', 'Task', 'game_players'])
-    game_rot[data_cols] = game_rot[data_cols].apply(pd.to_numeric, errors='coerce')
-    game_rot['spread'] = game_rot['GAME_ID'].map(spread_dict)
-    game_rot['GAME_DATE'] = pd.to_datetime(game_rot['GAME_DATE']).dt.date
-    
-    timestamp = gamelog_table['Date'].max()
+def init_baselines(data_req: str):
+    if data_req == 'gamelogs':
+        collection = db["gamelog"] 
+        cursor = collection.find()  # Finds all documents in the collection
+        
+        raw_display = pd.DataFrame(list(cursor))
+        gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
+        gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'GAME_ID', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
+                                    'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
+                                    'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM']]
+        gamelog_table['assists'].replace("", 0, inplace=True)
+        gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
+        gamelog_table['passes'].replace("", 0, inplace=True)
+        gamelog_table['touches'].replace("", 0, inplace=True)
+        gamelog_table['MIN'].replace("", 0, inplace=True)
+        gamelog_table['Fantasy'].replace("", 0, inplace=True)
+        gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
+        gamelog_table['FPPM'].replace("", 0, inplace=True)
+        gamelog_table['REB'] = gamelog_table['REB'].astype(int)
+        gamelog_table['assists'] = gamelog_table['assists'].astype(int)
+        gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
+        gamelog_table['passes'] = gamelog_table['passes'].astype(int)
+        gamelog_table['touches'] = gamelog_table['touches'].astype(int)
+        gamelog_table['MIN'] = gamelog_table['MIN'].astype(int)
+        gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
+        gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
+        gamelog_table['FPPM'] = gamelog_table['FPPM'].astype(float)
+        gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
+        gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
+        gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN']
+        gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
+        gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
+        data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
+        gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
+        gamelog_table['team_score'] = gamelog_table.groupby(['TEAM_NAME', 'GAME_ID'], sort=False)['PTS'].transform('sum')
+        gamelog_table['opp_score'] = gamelog_table.groupby(['GAME_ID'], sort=False)['PTS'].transform('sum') - gamelog_table['team_score']
+        gamelog_table['spread'] = (gamelog_table['opp_score'] - gamelog_table['team_score']).abs()
+        gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
+        
+        spread_dict = dict(zip(gamelog_table['GAME_ID'], gamelog_table['spread']))
+        
+        gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'game_id', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
+                                                'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                                'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM',
+                                                'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch', 'team_score', 'opp_score', 'spread'], axis=1)
+        game_rot = None
+        timestamp = gamelog_table['Date'].max()
+    elif data_req == 'game_rotations':
+        collection = db["rotations"] 
+        cursor = collection.find()  # Finds all documents in the collection
+        
+        raw_display = pd.DataFrame(list(cursor))
+        game_rot = raw_display[raw_display['PLAYER_NAME'] != ""]
+        data_cols = game_rot.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_ABBREVIATION', 'OPP_ABBREVIATION', 'TEAM_NAME', 'OPP_NAME', 'GAME_DATE',
+                                        'MATCHUP', 'WL', 'backlog_lookup', 'Task', 'game_players'])
+        game_rot[data_cols] = game_rot[data_cols].apply(pd.to_numeric, errors='coerce')
+        game_rot['spread'] = game_rot['GAME_ID'].map(spread_dict)
+        game_rot['GAME_DATE'] = pd.to_datetime(game_rot['GAME_DATE']).dt.date
+
+        gamelog_table = None
+        timestamp = None
+    elif data_req == 'all':
+        collection = db["gamelog"] 
+        cursor = collection.find()  # Finds all documents in the collection
+        
+        raw_display = pd.DataFrame(list(cursor))
+        gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
+        gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'GAME_ID', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
+                                    'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
+                                    'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM']]
+        gamelog_table['assists'].replace("", 0, inplace=True)
+        gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
+        gamelog_table['passes'].replace("", 0, inplace=True)
+        gamelog_table['touches'].replace("", 0, inplace=True)
+        gamelog_table['MIN'].replace("", 0, inplace=True)
+        gamelog_table['Fantasy'].replace("", 0, inplace=True)
+        gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
+        gamelog_table['FPPM'].replace("", 0, inplace=True)
+        gamelog_table['REB'] = gamelog_table['REB'].astype(int)
+        gamelog_table['assists'] = gamelog_table['assists'].astype(int)
+        gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
+        gamelog_table['passes'] = gamelog_table['passes'].astype(int)
+        gamelog_table['touches'] = gamelog_table['touches'].astype(int)
+        gamelog_table['MIN'] = gamelog_table['MIN'].astype(int)
+        gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
+        gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
+        gamelog_table['FPPM'] = gamelog_table['FPPM'].astype(float)
+        gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
+        gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
+        gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN']
+        gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
+        gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
+        data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
+        gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
+        gamelog_table['team_score'] = gamelog_table.groupby(['TEAM_NAME', 'GAME_ID'], sort=False)['PTS'].transform('sum')
+        gamelog_table['opp_score'] = gamelog_table.groupby(['GAME_ID'], sort=False)['PTS'].transform('sum') - gamelog_table['team_score']
+        gamelog_table['spread'] = (gamelog_table['opp_score'] - gamelog_table['team_score']).abs()
+        gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
+        
+        spread_dict = dict(zip(gamelog_table['GAME_ID'], gamelog_table['spread']))
+        
+        gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'game_id', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
+                                                'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                                'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', 'FPPM',
+                                                'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch', 'team_score', 'opp_score', 'spread'], axis=1)
+
+        timestamp = gamelog_table['Date'].max()
+
+        collection = db["rotations"] 
+        cursor = collection.find()  # Finds all documents in the collection
+        
+        raw_display = pd.DataFrame(list(cursor))
+        game_rot = raw_display[raw_display['PLAYER_NAME'] != ""]
+        data_cols = game_rot.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_ABBREVIATION', 'OPP_ABBREVIATION', 'TEAM_NAME', 'OPP_NAME', 'GAME_DATE',
+                                        'MATCHUP', 'WL', 'backlog_lookup', 'Task', 'game_players'])
+        game_rot[data_cols] = game_rot[data_cols].apply(pd.to_numeric, errors='coerce')
+        game_rot['spread'] = game_rot['GAME_ID'].map(spread_dict)
+        game_rot['GAME_DATE'] = pd.to_datetime(game_rot['GAME_DATE']).dt.date
     
-    return gamelog_table, rot_table, game_rot, timestamp
+    return gamelog_table, game_rot, timestamp
 
 @st.cache_data(show_spinner=False)
 def seasonlong_build(data_sample):
@@ -192,39 +278,46 @@ def split_frame(input_df, rows):
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-t_stamp = f"Updated through: " + str(timestamp) + f" CST"
-basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-basic_season_cols = ['Pos', 'Team', 'Min']
-data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
-             'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
-             'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
-             'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
-                    'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
-                    'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
-                    'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                 'Fantasy', 'FD_Fantasy']
-indv_teams = gamelog_table.drop_duplicates(subset='Team')
-total_teams = indv_teams.Team.values.tolist()
-indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-total_rot_teams = indv_rot_teams.Team.values.tolist()
-indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-indv_players = gamelog_table.drop_duplicates(subset='Player')
-total_players = indv_players.Player.values.tolist()
-total_dates = gamelog_table.Date.values.tolist()
+# gamelog_table, game_rot, timestamp = init_baselines('all')
+# t_stamp = f"Updated through: " + str(timestamp) + f" CST"
+# basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
+# basic_season_cols = ['Pos', 'Team', 'Min']
+# data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
+#              'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+#              'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+#              'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
+# season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+#                     'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+#                     'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+#                     'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
+# game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
+#                  'Fantasy', 'FD_Fantasy']
+# indv_teams = gamelog_table.drop_duplicates(subset='Team')
+# total_teams = indv_teams.Team.values.tolist()
+# indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
+# total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
+# indv_players = gamelog_table.drop_duplicates(subset='Player')
+# total_players = indv_players.Player.values.tolist()
+# total_dates = gamelog_table.Date.values.tolist()
 
-tab1, tab2, tab3, tab4, tab5 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Positional Percentages', 'Game Rotations'])
+# tab1, tab2, tab3, tab4, tab5 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Positional Percentages', 'Game Rotations'])
 
-with tab1:
-    st.info(t_stamp)
+selected_tab = st.segmented_control(
+    "Select Tab",
+    options=['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Game Rotations'],
+    selection_mode='single',
+    default='Gamelogs',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+
+with selected_tab == 'Gamelogs':
     col1, col2 = st.columns([1, 9])
     with col1:
         if st.button("Reset Data", key='reset1'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
+                  gamelog_table, game_rot, timestamp = init_baselines('gamelogs')
                   basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
                   basic_season_cols = ['Pos', 'Team', 'Min']
                   data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
@@ -239,8 +332,6 @@ with tab1:
                                    'Fantasy', 'FD_Fantasy']
                   indv_teams = gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
                   indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
                   total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
                   indv_players = gamelog_table.drop_duplicates(subset='Player')
@@ -347,13 +438,12 @@ with tab1:
                     mime='text/csv',
             )
             
-with tab2:
-    st.info(t_stamp)
+with selected_tab == 'Correlation Matrix':
     col1, col2 = st.columns([1, 9])
     with col1:
         if st.button("Reset Data", key='reset2'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
+                  gamelog_table, game_rot, timestamp = init_baselines('gamelogs')
                   basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
                   basic_season_cols = ['Pos', 'Team', 'Min']
                   data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
@@ -368,8 +458,6 @@ with tab2:
                                    'Fantasy', 'FD_Fantasy']
                   indv_teams = gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
                   indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
                   total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
                   indv_players = gamelog_table.drop_duplicates(subset='Player')
@@ -442,13 +530,12 @@ with tab2:
                 mime='text/csv',
         )
 
-with tab3:
-    st.info(t_stamp)
+with selected_tab == 'Position vs. Opp':
     col1, col2 = st.columns([1, 9])
     with col1:
         if st.button("Reset Data", key='reset3'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
+                  gamelog_table, game_rot, timestamp = init_baselines('gamelogs')
                   basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
                   basic_season_cols = ['Pos', 'Team', 'Min']
                   data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
@@ -463,8 +550,6 @@ with tab3:
                                    'Fantasy', 'FD_Fantasy']
                   indv_teams = gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
                   indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
                   total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
                   indv_players = gamelog_table.drop_duplicates(subset='Player')
@@ -529,64 +614,12 @@ with tab3:
                 mime='text/csv',
         )
 
-with tab4:
-    st.info(t_stamp)
-    col1, col2 = st.columns([1, 9])
-    with col1:
-        if st.button("Reset Data", key='reset4'):
-                  st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
-                               'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
-                               'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
-                                      'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
-                                      'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
-                  total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
-        
-        split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
-        
-        if split_var5 == 'Specific Teams':
-            team_var4 = st.multiselect('Which teams would you like to view?', options = total_rot_teams, key='team_var4')
-        elif split_var5 == 'All':
-            team_var4 = total_rot_teams
-        
-    
-    with col2:
-        working_data = rot_table
-        rot_display = working_data[working_data['Team'].isin(team_var4)]
-        display = st.container()
-
-        # rot_display = rot_display.set_index('Player')
-        display.dataframe(rot_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), height=500, use_container_width=True)
-        st.download_button(
-                label="Export Rotations Model",
-                data=convert_df_to_csv(rot_display),
-                file_name='Rotations_NBA_View.csv',
-                mime='text/csv',
-        )
-
-with tab5:
-    st.info(t_stamp)
+with selected_tab == 'Game Rotations':
     col1, col2 = st.columns([1, 9])
     with col1:
         if st.button("Reset Data", key='reset5'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
+                  gamelog_table, game_rot, timestamp = init_baselines('game_rotations')
                   basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
                   basic_season_cols = ['Pos', 'Team', 'Min']
                   data_cols = ['team_score', 'opp_score', 'spread', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M',
@@ -601,8 +634,6 @@ with tab5:
                                    'Fantasy', 'FD_Fantasy']
                   indv_teams = gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
                   indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
                   total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
                   indv_players = gamelog_table.drop_duplicates(subset='Player')