Initial commit from old app

.streamlit/secrets.toml

@@ -0,0 +1 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"

Dockerfile

@@ -5,11 +5,24 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
+    software-properties-common \
     git \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt ./
 COPY src/ ./src/
+COPY .streamlit/ ./.streamlit/
+
+
+
+ENV MONGO_URI="mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user\
+	PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+RUN pip install --no-cache-dir --upgrade pip
+COPY --chown=user . $HOME/app
 
 RUN pip3 install -r requirements.txt
 

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+streamlit
+gspread
+openpyxl
+matplotlib
+streamlit-aggrid
+pulp
+docker
+plotly
+scipy
+pymongo
+gspread

src/database.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import pymongo
+import gspread
+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-----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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"
+        }
+        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
+    
+gcservice_account, db = init_conn()

src/streamlit_app.py

@@ -1,40 +1,786 @@
-import altair as alt
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+        
 import numpy as np
 import pandas as pd
 import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.io as pio
+import certifi
+ca = certifi.where()
+from database import gcservice_account, 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.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()
+    
+    return gamelog_table, rot_table, game_rot, timestamp
+
+@st.cache_data(show_spinner=False)
+def seasonlong_build(data_sample):
+    season_long_table = data_sample[['Player', 'Pos', 'Team']]
+    season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
+    season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
+    season_long_table['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) /
+                                      data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int))
+    season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
+    season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float)
+    season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float)
+    season_long_table['FG%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('sum').astype(int))
+    season_long_table['FG3M'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('mean').astype(float)
+    season_long_table['FG3A'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('mean').astype(float)
+    season_long_table['FG3%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('sum').astype(int))
+    season_long_table['FTM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('mean').astype(float)
+    season_long_table['FTA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('mean').astype(float)
+    season_long_table['FT%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('sum').astype(int))
+    season_long_table['OREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB Chance'].transform('mean').astype(float)
+    season_long_table['OREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB'].transform('mean').astype(float)
+    season_long_table['DREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB Chance'].transform('mean').astype(float)
+    season_long_table['DREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB'].transform('mean').astype(float)
+    season_long_table['REB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('mean').astype(float)
+    season_long_table['REB'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('mean').astype(float)
+    season_long_table['Passes'] = data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('mean').astype(float)
+    season_long_table['Alt Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Alt Assists'].transform('mean').astype(float)
+    season_long_table['FT Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['FT Assists'].transform('mean').astype(float)
+    season_long_table['Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('mean').astype(float)
+    season_long_table['Stl'] = data_sample.groupby(['Player', 'Season'], sort=False)['Stl'].transform('mean').astype(float)
+    season_long_table['Blk'] = data_sample.groupby(['Player', 'Season'], sort=False)['Blk'].transform('mean').astype(float)
+    season_long_table['Tov'] = data_sample.groupby(['Player', 'Season'], sort=False)['Tov'].transform('mean').astype(float)
+    season_long_table['PF'] = data_sample.groupby(['Player', 'Season'], sort=False)['PF'].transform('mean').astype(float)
+    season_long_table['DD'] = data_sample.groupby(['Player', 'Season'], sort=False)['DD'].transform('mean').astype(float)
+    season_long_table['TD'] = data_sample.groupby(['Player', 'Season'], sort=False)['TD'].transform('mean').astype(float)
+    season_long_table['Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('mean').astype(float)
+    season_long_table['FD_Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
+    season_long_table['FPPM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FPPM'].transform('mean').astype(float)
+    season_long_table['Rebound%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('sum').astype(int) /
+                                     data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int))
+    season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) /
+                                             data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int))
+    season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
+                                              data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
+    season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
+                                                 data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
+    season_long_table = season_long_table.drop_duplicates(subset='Player')
+
+    season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
+    
+    season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', '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'], axis=1)
+
+    return season_long_table
+
+@st.cache_data(show_spinner=False)
+def run_fantasy_corr(data_sample):
+    cor_testing = data_sample
+    cor_testing = cor_testing[cor_testing['Season'] == '22024']
+    date_list = cor_testing['Date'].unique().tolist()
+    player_list = cor_testing['Player'].unique().tolist()
+    corr_frame = pd.DataFrame()
+    corr_frame['DATE'] = date_list
+    for player in player_list:
+        player_testing = cor_testing[cor_testing['Player'] == player]
+        fantasy_map = dict(zip(player_testing['Date'], player_testing['Fantasy']))
+        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
+    players_fantasy = corr_frame.drop('DATE', axis=1)
+    corrM = players_fantasy.corr()
+    
+    return corrM
+
+@st.cache_data(show_spinner=False)
+def run_min_corr(data_sample):
+    cor_testing = data_sample
+    cor_testing = cor_testing[cor_testing['Season'] == '22024']
+    date_list = cor_testing['Date'].unique().tolist()
+    player_list = cor_testing['Player'].unique().tolist()
+    corr_frame = pd.DataFrame()
+    corr_frame['DATE'] = date_list
+    for player in player_list:
+        player_testing = cor_testing[cor_testing['Player'] == player]
+        fantasy_map = dict(zip(player_testing['Date'], player_testing['Min']))
+        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
+    players_fantasy = corr_frame.drop('DATE', axis=1)
+    corrM = players_fantasy.corr()
+    
+    return corrM
+
+@st.cache_data(show_spinner=False)
+def split_frame(input_df, rows):
+    df = [input_df.loc[i : i + rows - 1, :] for i in range(0, len(input_df), rows)]
+    return df
+
+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()
+
+tab1, tab2, tab3, tab4, tab5 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Positional Percentages', 'Game Rotations'])
+
+with tab1:
+    st.info(t_stamp)
+    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()
+                  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_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = total_teams
+            
+        split_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3')
+        
+        if split_var3 == 'Specific Dates':
+            low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date')
+            if low_date is not None:
+                low_date = pd.to_datetime(low_date).date()
+            high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date')
+            if high_date is not None:
+                high_date = pd.to_datetime(high_date).date()
+        elif split_var3 == 'All':
+            low_date = gamelog_table['Date'].min()
+            high_date = gamelog_table['Date'].max()
+        
+        split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='split_var4')
+        
+        if split_var4 == 'Specific Players':
+            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_players, key='player_var1')
+        elif split_var4 == 'All':
+            player_var1 = total_players
+            
+        spread_var1 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var1')
+        
+        min_var1 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1')
+    
+    with col2:
+        working_data = gamelog_table
+        if split_var1 == 'Season Logs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp = st.multiselect('Which stats would you like to view?', options = season_data_cols, default = season_data_cols, key='col_display')
+            disp_stats = basic_season_cols + choose_disp
+            display = st.container()
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['Min'] >= min_var1[0]]
+            working_data = working_data[working_data['Min'] <= min_var1[1]]
+            working_data = working_data[working_data['spread'] >= spread_var1[0]]
+            working_data = working_data[working_data['spread'] <= spread_var1[1]]
+            working_data = working_data[working_data['Team'].isin(team_var1)]
+            working_data = working_data[working_data['Player'].isin(player_var1)]
+            season_long_table = seasonlong_build(working_data)
+            season_long_table = season_long_table.set_index('Player')
+            season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns")
+            display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True)  
+            st.download_button(
+                    label="Export seasonlogs Model",
+                    data=convert_df_to_csv(season_long_table),
+                    file_name='Seasonlogs_NBA_View.csv',
+                    mime='text/csv',
+            )
+            
+        elif split_var1 == 'Gamelogs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_gamelog')
+            gamelog_disp_stats = basic_cols + choose_disp_gamelog
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['Min'] >= min_var1[0]]
+            working_data = working_data[working_data['Min'] <= min_var1[1]]
+            working_data = working_data[working_data['spread'] >= spread_var1[0]]
+            working_data = working_data[working_data['spread'] <= spread_var1[1]]
+            working_data = working_data[working_data['Team'].isin(team_var1)]
+            working_data = working_data[working_data['Player'].isin(player_var1)]
+            working_data = working_data.reset_index(drop=True)
+            gamelog_data = working_data.reindex(gamelog_disp_stats,axis="columns")
+            display = st.container()
+        
+            bottom_menu = st.columns((4, 1, 1))
+            with bottom_menu[2]:
+                batch_size = st.selectbox("Page Size", options=[25, 50, 100])
+            with bottom_menu[1]:
+                total_pages = (
+                    int(len(gamelog_data) / batch_size) if int(len(gamelog_data) / batch_size) > 0 else 1
+                )
+                current_page = st.number_input(
+                    "Page", min_value=1, max_value=total_pages, step=1
+                )
+            with bottom_menu[0]:
+                st.markdown(f"Page **{current_page}** of **{total_pages}** ")
+            
+            
+            pages = split_frame(gamelog_data, batch_size)
+            # pages = pages.set_index('Player')
+            display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
+            st.download_button(
+                    label="Export gamelogs Model",
+                    data=convert_df_to_csv(gamelog_data),
+                    file_name='Gamelogs_NBA_View.csv',
+                    mime='text/csv',
+            )
+            
+with tab2:
+    st.info(t_stamp)
+    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()
+                  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()
+        
+        corr_var = st.radio("Are you correlating fantasy or minutes?", ('Fantasy', 'Minutes'), key='corr_var')
+        
+        split_var1_t2 = st.radio("Would you like to view specific teams or specific players?", ('Specific Teams', 'Specific Players'), key='split_var1_t2')
+        
+        if split_var1_t2 == 'Specific Teams':
+            corr_var1_t2 = st.multiselect('Which teams would you like to include in the correlation?', options = total_teams, key='corr_var1_t2')
+        elif split_var1_t2 == 'Specific Players':
+            corr_var1_t2 = st.multiselect('Which players would you like to include in the correlation?', options = total_players, key='corr_var1_t2')
+            
+        split_var2_t2 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3_t2')
+        
+        if split_var2_t2 == 'Specific Dates':
+            low_date_t2 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date_t2')
+            if low_date_t2 is not None:
+                low_date_t2 = pd.to_datetime(low_date_t2).date()
+            high_date_t2 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date_t2')
+            if high_date_t2 is not None:
+                high_date_t2 = pd.to_datetime(high_date_t2).date()
+        elif split_var2_t2 == 'All':
+            low_date_t2 = gamelog_table['Date'].min()
+            high_date_t2 = gamelog_table['Date'].max()
+            
+        spread_var1_t2 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var1_t2')
+        
+        min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1_t2')
+    
+    with col2:
+        working_data = gamelog_table
+        if split_var1_t2 == 'Specific Teams':
+            display = st.container()
+            working_data = working_data.sort_values(by='Fantasy', ascending=False)
+            working_data = working_data[working_data['Date'] >= low_date_t2]
+            working_data = working_data[working_data['Date'] <= high_date_t2]
+            working_data = working_data[working_data['Min'] >= min_var1_t2[0]]
+            working_data = working_data[working_data['Min'] <= min_var1_t2[1]]
+            working_data = working_data[working_data['spread'] >= spread_var1_t2[0]]
+            working_data = working_data[working_data['spread'] <= spread_var1_t2[1]]
+            working_data = working_data[working_data['Team'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(working_data)
+            elif corr_var == 'Minutes':
+                corr_display = run_min_corr(working_data)
+            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
+            
+        elif split_var1_t2 == 'Specific Players':
+            display = st.container()
+            working_data = working_data.sort_values(by='Fantasy', ascending=False)
+            working_data = working_data[working_data['Date'] >= low_date_t2]
+            working_data = working_data[working_data['Date'] <= high_date_t2]
+            working_data = working_data[working_data['Min'] >= min_var1_t2[0]]
+            working_data = working_data[working_data['Min'] <= min_var1_t2[1]]
+            working_data = working_data[working_data['spread'] >= spread_var1_t2[0]]
+            working_data = working_data[working_data['spread'] <= spread_var1_t2[1]]
+            working_data = working_data[working_data['Player'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(working_data)
+            elif corr_var == 'Minutes':
+                corr_display = run_min_corr(working_data)
+            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+                label="Export Correlations Model",
+                data=convert_df_to_csv(corr_display),
+                file_name='Correlations_NBA_View.csv',
+                mime='text/csv',
+        )
+
+with tab3:
+    st.info(t_stamp)
+    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()
+                  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()
+        
+        team_var3 = st.selectbox('Which opponent would you like to view?', options = total_teams, key='team_var3')
+        pos_var3 = st.selectbox('Which position would you like to view?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var3')
+        disp_var3 = st.radio('Which view would you like to see?', options = ['Fantasy', 'Stats'], key='disp_var3')
+        date_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='date_var3')
+        
+        if date_var3 == 'Specific Dates':
+            low_date3 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date3')
+            if low_date3 is not None:
+                low_date3 = pd.to_datetime(low_date3).date()
+            high_date3 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date3')
+            if high_date3 is not None:
+                high_date3 = pd.to_datetime(high_date3).date()
+        elif date_var3 == 'All':
+            low_date3 = gamelog_table['Date'].min()
+            high_date3 = gamelog_table['Date'].max()
+        
+        spread_var3 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var3')
+        
+        min_var3 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var3')
+    
+    with col2:
+        if disp_var3 == 'Stats':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp_matchup = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_matchup')
+            matchup_disp_stats = basic_cols + choose_disp_matchup
+        working_data = gamelog_table
+        working_data = working_data[gamelog_table['Date'] >= low_date3]
+        working_data = working_data[gamelog_table['Date'] <= high_date3]
+        season_long_table = seasonlong_build(working_data)
+        fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['Fantasy']))
+        fd_fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['FD_Fantasy']))
+            
+        working_data = working_data[working_data['Pos'] == pos_var3]
+        working_data = working_data[working_data['Min'] >= min_var3[0]]
+        working_data = working_data[working_data['Min'] <= min_var3[1]]
+        working_data = working_data[working_data['spread'] >= spread_var3[0]]
+        working_data = working_data[working_data['spread'] <= spread_var3[1]]
+        working_data = working_data[working_data['Opp'] == team_var3]
+        working_data = working_data.reset_index(drop=True)
+        if disp_var3 == 'Fantasy':
+            gamelog_display = working_data[['Player', 'Pos', 'Team', 'Opp', 'Date', 'Min', 'Fantasy', 'FD_Fantasy']]
+        elif disp_var3 == 'Stats':
+            gamelog_data = working_data.reindex(matchup_disp_stats,axis="columns")
+            gamelog_display = gamelog_data
+        gamelog_display['Avg_Fantasy'] = gamelog_display['Player'].map(fantasy_dict)
+        gamelog_display['Avg_FD_Fantasy'] = gamelog_display['Player'].map(fd_fantasy_dict)
+        display = st.container()
+
+        # pages = pages.set_index('Player')
+        display.dataframe(gamelog_display.style.format(precision=2), height=500, use_container_width=True)
+        st.download_button(
+                label="Export Matchups Model",
+                data=convert_df_to_csv(gamelog_display),
+                file_name='Matchups_NBA_View.csv',
+                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)
+    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()
+                  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()
+        
+        game_rot_view = st.radio("What set would you like to view?", ('Team Rotations', 'Player Rotations'), key='game_rot_view')
+        
+        if game_rot_view == 'Team Rotations':
+            game_rot_team = st.selectbox("What team would you like to work with?", options = total_game_rot_teams, key='game_rot_team')
+            
+            game_rot_spread = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='game_rot_spread')
+            
+            game_rot_min = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='game_rot_min')
+            
+            game_rot_dates = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='game_rot_dates')
+            
+            if game_rot_dates == 'Specific Dates':
+                game_rot_low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='game_rot_low_date')
+                if game_rot_low_date is not None:
+                    game_rot_low_date = pd.to_datetime(low_date).date()
+                game_rot_high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='game_rot_high_date')
+                if game_rot_high_date is not None:
+                    game_rot_high_date = pd.to_datetime(high_date).date()
+            elif game_rot_dates == 'All':
+                game_rot_low_date = gamelog_table['Date'].min()
+                game_rot_high_date = gamelog_table['Date'].max()
+        elif game_rot_view == 'Player Rotations':
+            game_rot_team = st.multiselect("What players would you like to work with?", options = total_players, key='game_rot_team')
+            
+            game_rot_spread = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='game_rot_spread')
+            
+            game_rot_min = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='game_rot_min')
+            
+            game_rot_dates = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='game_rot_dates')
+            
+            if game_rot_dates == 'Specific Dates':
+                game_rot_low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='game_rot_low_date')
+                if game_rot_low_date is not None:
+                    game_rot_low_date = pd.to_datetime(game_rot_low_date).date()
+                game_rot_high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='game_rot_high_date')
+                if game_rot_high_date is not None:
+                    game_rot_high_date = pd.to_datetime(game_rot_high_date).date()
+            elif game_rot_dates == 'All':
+                game_rot_low_date = gamelog_table['Date'].min()
+                game_rot_high_date = gamelog_table['Date'].max()
+                
+            
+    with col2:
+        if game_rot_view == 'Player Rotations':
+            team_backlog = game_rot[game_rot['PLAYER_NAME'].isin(game_rot_team)]
+            team_backlog = team_backlog[pd.to_datetime(team_backlog['GAME_DATE']).dt.date >= game_rot_low_date]
+            team_backlog = team_backlog[pd.to_datetime(team_backlog['GAME_DATE']).dt.date <= game_rot_high_date]
+            team_backlog = team_backlog[team_backlog['MIN'] >= game_rot_min[0]]
+            team_backlog = team_backlog[team_backlog['MIN'] <= game_rot_min[1]]
+            team_backlog = team_backlog[team_backlog['spread'] >= game_rot_spread[0]]
+            team_backlog = team_backlog[team_backlog['spread'] <= game_rot_spread[1]]
+            working_data = game_rot
+            display = st.container()
+            stats_disp = st.container()
+            check_rotation = team_backlog.sort_values(by=['GAME_DATE', 'Finish'], ascending=[False, True])
+            
+            # Ensure Start and Finish are numeric and Task is properly set
+            check_rotation['Start'] = pd.to_numeric(check_rotation['Start'], errors='coerce')
+            check_rotation['Finish'] = pd.to_numeric(check_rotation['Finish'], errors='coerce')
+            check_rotation['delta'] = pd.to_numeric(check_rotation['delta'], errors='coerce')
+            
+            # Create figure
+            fig = go.Figure()
+            
+            # Add bars for each shift
+            for idx, row in check_rotation.iterrows():
+                fig.add_trace(go.Bar(
+                    x=[row['delta']],  # Width of bar
+                    y=[row['Task']],
+                    base=row['Start'],  # Start position of bar
+                    orientation='h',
+                    text=f"{row['delta']:.1f} Minutes",
+                    textposition='inside',
+                    showlegend=False,
+                    marker_color=px.colors.qualitative.Plotly[hash(row['PLAYER_NAME']) % len(px.colors.qualitative.Plotly)]
+                ))
+            
+            # Update layout
+            fig.update_layout(
+                barmode='overlay',
+                xaxis=dict(
+                    range=[0, 48],
+                    title='Game Time (minutes)'
+                ),
+                yaxis=dict(
+                    autorange='reversed'
+                )
+            )
+            
+            # Add quarter lines
+            fig.add_vline(x=12, line_width=3, line_dash="dash", line_color="green")
+            fig.add_vline(x=24, line_width=3, line_dash="dash", line_color="green")
+            fig.add_vline(x=36, line_width=3, line_dash="dash", line_color="green")
+
+            game_rot_stats = check_rotation.reindex(game_rot_cols,axis="columns")
+            game_rot_stats = game_rot_stats.drop_duplicates(subset='backlog_lookup')
+
+            # pages = pages.set_index('Player')
+            display.plotly_chart(fig, use_container_width=True)
+            stats_disp.dataframe(game_rot_stats.style.format(precision=2), hide_index=True, use_container_width = True)
+            
+        elif game_rot_view == 'Team Rotations':
+            team_backlog = game_rot[game_rot['TEAM_ABBREVIATION'] == game_rot_team]
+            team_backlog = team_backlog[pd.to_datetime(team_backlog['GAME_DATE']).dt.date >= game_rot_low_date]
+            team_backlog = team_backlog[pd.to_datetime(team_backlog['GAME_DATE']).dt.date <= game_rot_high_date]
+            team_backlog = team_backlog[team_backlog['MIN'] >= game_rot_min[0]]
+            team_backlog = team_backlog[team_backlog['MIN'] <= game_rot_min[1]]
+            team_backlog = team_backlog[team_backlog['spread'] >= game_rot_spread[0]]
+            team_backlog = team_backlog[team_backlog['spread'] <= game_rot_spread[1]]
+            game_id_var = st.selectbox("What game would you like to view?", options = team_backlog['backlog_lookup'].unique(), key='game_id_var')
+            working_data = game_rot
+            display = st.container()
+            stats_disp = st.container()
+            check_rotation = working_data[working_data['backlog_lookup'] == game_id_var]
+            check_rotation = check_rotation.sort_values(by='Start', ascending=True)
+            game_rot_stats = check_rotation.reindex(game_rot_cols,axis="columns")
+            game_rot_stats = game_rot_stats.drop_duplicates(subset='PLAYER_NAME')
+
+            # Create figure
+            fig = go.Figure()
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+            distinct_colors = [
+                '#1f77b4',  # blue
+                '#ff7f0e',  # orange
+                '#2ca02c',  # green
+                '#d62728',  # red
+                '#9467bd',  # purple
+                '#8c564b',  # brown
+                '#e377c2',  # pink
+                '#7f7f7f',  # gray
+                '#bcbd22',  # yellow-green
+                '#17becf',  # cyan
+                '#aec7e8',  # light blue
+                '#ffbb78',  # light orange
+                '#98df8a',  # light green
+                '#ff9896',  # light red
+                '#c5b0d5'   # light purple
+            ]
+            
+            # Create a mapping of unique tasks to colors
+            unique_tasks = check_rotation['Task'].unique()
+            color_map = dict(zip(unique_tasks, distinct_colors[:len(unique_tasks)]))
+            
+            # Add bars for each rotation shift
+            for idx, row in check_rotation.iterrows():
+                fig.add_trace(go.Bar(
+                    x=[row['Finish'] - row['Start']],  # Width of bar
+                    y=[row['Task']],
+                    base=row['Start'],  # Start position of bar
+                    orientation='h',
+                    text=f"{row['minutes']:.1f} Minutes",
+                    textposition='inside',
+                    showlegend=False,
+                    marker_color=color_map[row['Task']]  # Use mapped color for task
+                ))
+            
+            # Update layout
+            fig.update_layout(
+                barmode='overlay',
+                xaxis=dict(
+                    range=[0, 48],
+                    title='Game Time (minutes)'
+                ),
+                yaxis=dict(
+                    autorange='reversed'
+                )
+            )
+            
+            # Add quarter lines
+            fig.add_vline(x=12, line_width=3, line_dash="dash", line_color="green")
+            fig.add_vline(x=24, line_width=3, line_dash="dash", line_color="green")
+            fig.add_vline(x=36, line_width=3, line_dash="dash", line_color="green")
+            # pages = pages.set_index('Player')
+            display.plotly_chart(fig, use_container_width=True)
+            stats_disp.dataframe(game_rot_stats.style.format(precision=2), hide_index=True, use_container_width = True)