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import streamlit as st
import numpy as np
import pandas as pd
import pymongo
st.set_page_config(layout="wide")
@st.cache_resource
def init_conn():
uri = st.secrets['mongo_uri']
client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
db = client["MLB_Database"]
return db
db = init_conn()
st.markdown("""
<style>
/* Tab styling */
.stTabs [data-baseweb="tab-list"] {
gap: 8px;
padding: 4px;
}
.stTabs [data-baseweb="tab"] {
height: 50px;
white-space: pre-wrap;
background-color: #DAA520;
color: white;
border-radius: 10px;
gap: 1px;
padding: 10px 20px;
font-weight: bold;
transition: all 0.3s ease;
}
.stTabs [aria-selected="true"] {
background-color: #DAA520;
border: 3px solid #FFD700;
color: white;
}
.stTabs [data-baseweb="tab"]:hover {
background-color: #FFD700;
cursor: pointer;
}
div[data-baseweb="select"] > div {
background-color: #DAA520;
color: white;
}
div{
box-sizing: content-box !important;
}
</style>""", unsafe_allow_html=True)
@st.cache_resource(ttl = 61)
def init_baselines():
db_pulls = ['Bullpen_Data', 'Hitter_Agg_Merge', 'Hitter_Long_Merge', 'Hitter_Short_Merge', 'Pitcher_Agg_Merge', 'Pitcher_Long_Merge', 'Pitcher_Short_Merge',
'Slate_Hitters_Merge', 'Slate_Team_Merge', 'Starting_Pitchers', 'True_AVG_Split', 'Pitcher_Info', 'Hitter_Info']
for table in db_pulls:
collection = db[table]
cursor = collection.find()
df = pd.DataFrame(cursor)
if table == 'Bullpen_Data':
bp_data = df
elif table == 'Hitter_Agg_Merge':
hitter_agg = df
elif table == 'Hitter_Long_Merge':
hitter_long = df
elif table == 'Hitter_Short_Merge':
hitter_short = df
elif table == 'Pitcher_Agg_Merge':
pitcher_agg = df
elif table == 'Pitcher_Long_Merge':
pitcher_long = df
elif table == 'Pitcher_Short_Merge':
pitcher_short = df
elif table == 'Slate_Hitters_Merge':
slate_hitters = df
elif table == 'Slate_Team_Merge':
slate_team = df
elif table == 'Starting_Pitchers':
starting_pitchers = df
elif table == 'True_AVG_Split':
true_avg_split = df
elif table == 'Pitcher_Info':
pitcher_info = df
elif table == 'Hitter_Info':
hitter_info = df
return bp_data, hitter_agg, hitter_long, hitter_short, pitcher_agg, pitcher_long, pitcher_short, slate_hitters, slate_team, starting_pitchers, true_avg_split, pitcher_info, hitter_info
bp_data, hitter_agg, hitter_long, hitter_short, pitcher_agg, pitcher_long, pitcher_short, slate_hitters, slate_team, starting_pitchers, true_avg_split, pitcher_info, hitter_info = init_baselines()
pitcher_tab, hitter_tab, team_tab = st.tabs(['Pitchers', 'Hitters', 'Team'])
with pitcher_tab:
with st.expander('Info and Display Options'):
st.info('Note: Splits options are available for all baseline tables, they do not apply to True AVG, HWSr, or the Overview tables')
col1, col2, col3 = st.columns(3)
with col1:
site_var_sp = st.selectbox('Site', ['DraftKings', 'FanDuel'], key = 'site_var_sp')
with col2:
table_var_sp = st.selectbox('Table', ['True AVG Splits', 'HWSr Splits', 'Current Slate Overview', 'Active Baselines', 'League Aggregate Baselines', 'League Short Term Baselines', 'League Long Term Baselines'], key = 'table_var_sp')
with col3:
splits_var_sp = st.selectbox('Splits', ['Overall', 'RHH', 'LHH'], key = 'splits_var_sp')
if table_var_sp == 'True AVG Splits':
st.dataframe(true_avg_split)
elif table_var_sp == 'HWSr Splits':
st.dataframe(true_avg_split)
elif table_var_sp == 'Current Slate Overview':
st.dataframe(starting_pitchers)
elif table_var_sp == 'Active Baselines':
st.dataframe(pitcher_info)
elif table_var_sp == 'League Aggregate Baselines':
st.dataframe(pitcher_agg)
elif table_var_sp == 'League Short Term Baselines':
st.dataframe(pitcher_short)
elif table_var_sp == 'League Long Term Baselines':
st.dataframe(pitcher_long)
with hitter_tab:
with st.expander('Info and Display Options'):
st.info('Note: Splits options are available for all baseline tables')
col1, col2, col3 = st.columns(3)
with col1:
site_var_hitter = st.selectbox('Site', ['DraftKings', 'FanDuel'], key = 'site_var_hitter')
with col2:
table_var_hitter = st.selectbox('Table', ['Active Baselines', 'League Aggregate Baselines', 'League Short Term Baselines', 'League Long Term Baselines'], key = 'table_var_hitter')
with col3:
splits_var_hitter = st.selectbox('Splits', ['Overall', 'RHP', 'LHP'], key = 'splits_var_hitter')
if table_var_hitter == 'Current Slate Overview':
st.dataframe(starting_pitchers)
elif table_var_hitter == 'Active Baselines':
st.dataframe(hitter_info)
elif table_var_hitter == 'League Aggregate Baselines':
st.dataframe(hitter_agg)
elif table_var_hitter == 'League Short Term Baselines':
st.dataframe(hitter_short)
elif table_var_hitter == 'League Long Term Baselines':
st.dataframe(hitter_long)
with team_tab:
with st.expander('Info and Display Options'):
col1, col2, col3 = st.columns(3)
with col1:
site_var_team= st.selectbox('Site', ['DraftKings', 'FanDuel'], key = 'site_var_team')
with col2:
table_var_team = st.selectbox('Table', ['Team Baselines', 'Bullpen Baselines'], key = 'table_var_team')
if table_var_team == 'Team Baselines':
st.dataframe(slate_team)
elif table_var_team == 'Bullpen Baselines':
st.dataframe(bp_data) |