Initial commit for modernization

.streamlit/secrets.toml

@@ -0,0 +1 @@
+NHL_Data = "https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit?gid=1951430245#gid=1951430245"

Dockerfile

@@ -5,11 +5,23 @@ 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 NHL_Data="https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit?gid=1951430245#gid=1951430245"
+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 +1,9 @@
-altair
-pandas
-streamlit
+streamlit
+gspread
+openpyxl
+matplotlib
+streamlit-aggrid
+pulp
+docker
+plotly
+scipy

src/database.py

@@ -0,0 +1,40 @@
+import streamlit as st
+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": 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"
+        }
+     
+        NHL_Data = os.getenv('NHL_Data')
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, NHL_Data

src/streamlit_app.py

@@ -1,40 +1,348 @@
-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
+from database import init_conn
+    
+gcservice_account, gcservice_account2, NHL_Data = init_conn()
+
+@st.cache_resource(ttl = 600)
+def init_baselines():
+    sh = gcservice_account.open_by_url(NHL_Data)
+    
+    worksheet = sh.worksheet('Gamelog')
+    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)
+    gamelog_table = raw_display[raw_display['Player'] != ""]
+    gamelog_table = gamelog_table[['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'TotalAssists', 'FirstAssists', 'SecondAssists', 'TotalPoints', 'IPP',
+                                   'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'RushAttempts', 'ReboundsCreated', 'PIM', 'TotalPenalties', 'Minor',
+                                   'Major', 'PenaltiesDrawn', 'Giveaways', 'Takeaways', 'Hits', 'HitsTaken', 'ShotsBlocked', 'FaceoffsWon',
+                                   'FaceoffsLost', 'Faceoffs%']]
+    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
+                                   'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
+                                   'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                                   'Faceoffs Lost', 'Faceoffs %'], axis=1)
+    data_cols = gamelog_table.columns.drop(['Player', 'Team', 'Position', 'Date'])
+    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
+    gamelog_table['Date'] = pd.to_datetime(gamelog_table['Date']).dt.date
+    gamelog_table['dk_shots_bonus'] = np.where((gamelog_table['Shots'] >= 5), 1, 0)
+    gamelog_table['dk_blocks_bonus'] = np.where((gamelog_table['Shots Blocked'] >= 3), 1, 0)
+    gamelog_table['dk_goals_bonus'] = np.where((gamelog_table['Goals'] >= 3), 1, 0)
+    gamelog_table['dk_points_bonus'] = np.where((gamelog_table['Total Points'] >= 3), 1, 0)
+    gamelog_table['dk_fantasy'] = sum([(gamelog_table['Goals'] * 8.5), (gamelog_table['Total Assists'] * 5), (gamelog_table['Shots'] * 1.5),
+                                       (gamelog_table['Shots Blocked'] * 1.3), (gamelog_table['dk_shots_bonus'] * 3), (gamelog_table['dk_blocks_bonus'] * 3),
+                                       (gamelog_table['dk_goals_bonus'] * 3), (gamelog_table['dk_points_bonus'] * 3)]).astype(float).round(2)
+    gamelog_table['fd_fantasy'] = sum([(gamelog_table['Goals'] * 12), (gamelog_table['Total Assists'] * 8), (gamelog_table['Shots'] * 1.6),
+                                       (gamelog_table['Shots Blocked'] * 1.6)]).astype(float).round(2)
+    
+    
+    
+    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
+                                            'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
+                                            'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                                            'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                                            'dk_fantasy', 'fd_fantasy'], axis=1)
+    
+    return gamelog_table
+
+@st.cache_data(show_spinner=False)
+def seasonlong_build(data_sample):
+    season_long_table = data_sample[['Player', 'Team', 'Position']]
+    season_long_table['TOI'] = data_sample.groupby(['Player', 'Team'], sort=False)['TOI'].transform('mean').astype(float)
+    season_long_table['Goals'] = data_sample.groupby(['Player', 'Team'], sort=False)['Goals'].transform('mean').astype(float)
+    season_long_table['Total Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Assists'].transform('mean').astype(float)
+    season_long_table['First Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['First Assists'].transform('mean').astype(float)
+    season_long_table['Second Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Second Assists'].transform('mean').astype(float)
+    season_long_table['Total Points'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Points'].transform('mean').astype(float)
+    season_long_table['IPP'] = data_sample.groupby(['Player', 'Team'], sort=False)['IPP'].transform('mean').astype(float)
+    season_long_table['Shots'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots'].transform('mean').astype(float)
+    season_long_table['ixG'] = data_sample.groupby(['Player', 'Team'], sort=False)['ixG'].transform('mean').astype(float)
+    season_long_table['iCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iCF'].transform('mean').astype(float)
+    season_long_table['iFF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iFF'].transform('mean').astype(float)
+    season_long_table['iSCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iSCF'].transform('mean').astype(float)
+    season_long_table['iHDCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iHDCF'].transform('mean').astype(float)
+    season_long_table['Rush Attempts'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rush Attempts'].transform('mean').astype(float)
+    season_long_table['Rebounds Created'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rebounds Created'].transform('mean').astype(float)
+    season_long_table['PIM'] = data_sample.groupby(['Player', 'Team'], sort=False)['PIM'].transform('mean').astype(float)
+    season_long_table['Total Penalties'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Penalties'].transform('mean').astype(float)
+    season_long_table['Minor'] = data_sample.groupby(['Player', 'Team'], sort=False)['Minor'].transform('mean').astype(float)
+    season_long_table['Major'] = data_sample.groupby(['Player', 'Team'], sort=False)['Major'].transform('mean').astype(float)
+    season_long_table['Penalties Drawn'] = data_sample.groupby(['Player', 'Team'], sort=False)['Penalties Drawn'].transform('mean').astype(float)
+    season_long_table['Giveaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Giveaways'].transform('mean').astype(float)
+    season_long_table['Takeaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Takeaways'].transform('mean').astype(float)
+    season_long_table['Hits'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits'].transform('mean').astype(float)
+    season_long_table['Hits Taken'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits Taken'].transform('mean').astype(float)
+    season_long_table['Shots Blocked'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots Blocked'].transform('mean').astype(float)
+    season_long_table['Faceoffs Won'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Won'].transform('mean').astype(float)
+    season_long_table['Faceoffs Lost'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Lost'].transform('mean').astype(float)
+    season_long_table['dk_shots_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_shots_bonus'].transform('mean').astype(float)
+    season_long_table['dk_blocks_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_blocks_bonus'].transform('mean').astype(float)
+    season_long_table['dk_goals_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_goals_bonus'].transform('mean').astype(float)
+    season_long_table['dk_points_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_points_bonus'].transform('mean').astype(float)
+    season_long_table['dk_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_fantasy'].transform('mean').astype(float)
+    season_long_table['fd_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['fd_fantasy'].transform('mean').astype(float)
+    season_long_table = season_long_table.drop_duplicates(subset='Player')
+    
+    season_long_table = season_long_table.sort_values(by='dk_fantasy', ascending=False)
+    
+    
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Position', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
+                                                    'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
+                                                    'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                                                    'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                                                    'dk_fantasy', 'fd_fantasy'], axis=1)
+
+    return season_long_table
+
+@st.cache_data(show_spinner=False)
+def run_fantasy_corr(data_sample):
+    cor_testing = data_sample
+    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['dk_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
+    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['TOI']))
+        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 = init_baselines()
+basic_cols = ['Player', 'Team', 'Position', 'Date', 'TOI']
+basic_season_cols = ['Player', 'Team', 'Position', 'TOI']
+data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
+             'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
+             'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+             'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+             'dk_fantasy', 'fd_fantasy']
+season_data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
+                    'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
+                    'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                    'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                    'dk_fantasy', 'fd_fantasy']
+indv_teams = gamelog_table.drop_duplicates(subset='Team')
+total_teams = indv_teams.Team.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 = st.tabs(['Gamelogs', 'Correlation Matrix'])
 
-"""
-# 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])),
-    ))
+with tab1:
+    col1, col2 = st.columns([1, 9])
+    with col1:
+        if st.button("Reset Data", key='reset1'):
+                  st.cache_data.clear()
+                  gamelog_table = init_baselines()
+                  basic_cols = ['Player', 'Team', 'Position', 'Date', 'TOI']
+                  basic_season_cols = ['Player', 'Team', 'Position', 'TOI']
+                  data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
+                               'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
+                               'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                               'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                               'dk_fantasy', 'fd_fantasy']
+                  season_data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
+                                      'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
+                                      'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
+                                      'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                                      'dk_fantasy', 'fd_fantasy']
+                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
+                  total_teams = indv_teams.Team.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="MM/DD/YYYY", 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="MM/DD/YYYY", 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
+        
+        min_var1 = st.slider("Is there a certain TOI range you want to view?", 0, 50, (0, 50), 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['TOI'] >= min_var1[0]]
+            working_data = working_data[working_data['TOI'] <= min_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_NHL_View.csv',
+                    mime='text/csv',
+            )
+            
+        elif split_var1 == 'Gamelogs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='col_display')
+            gamelog_disp_stats = basic_cols + choose_disp
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['TOI'] >= min_var1[0]]
+            working_data = working_data[working_data['TOI'] <= min_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.sort_values(by='Date', ascending=False)
+            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:
+    col1, col2 = st.columns([1, 9])
+    with col1:
+        if st.button("Reset Data", key='reset2'):
+                  st.cache_data.clear()
+                  gamelog_table = init_baselines()
+                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
+                  total_teams = indv_teams.Team.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 TOI?", ('Fantasy', 'TOI'), 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="MM/DD/YYYY", 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="MM/DD/YYYY", 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()
+        
+        min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 50, (0, 50), key='min_var1_t2')
+    
+    with col2:
+        if split_var1_t2 == 'Specific Teams':
+            display = st.container()
+            gamelog_table = gamelog_table.sort_values(by='dk_fantasy', ascending=False)
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Team'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(gamelog_table)
+            elif corr_var == 'TOI':
+                corr_display = run_min_corr(gamelog_table)
+            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()
+            gamelog_table = gamelog_table.sort_values(by='dk_fantasy', ascending=False)
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Player'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(gamelog_table)
+            elif corr_var == 'TOI':
+                corr_display = run_min_corr(gamelog_table)
+            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)