app.py

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 gc
import pymongo

@st.cache_resource
def init_conn():
        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["PGA_Database"]

        return db
    
db = init_conn()

dk_player_url = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624'
CSV_URL = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624'

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '100+%': '{:.2%}', '10x%': '{:.2%}', '11x%': '{:.2%}',
                   '12x%': '{:.2%}','LevX': '{:.2%}'}

@st.cache_resource(ttl = 600)
def init_baselines():
    collection = db["PGA_Range_of_Outcomes"] 
    cursor = collection.find()
    player_frame = pd.DataFrame(cursor)
    data_cols = player_frame.columns.drop('Player')
    player_frame[data_cols] = player_frame[data_cols].apply(pd.to_numeric, errors='coerce')
    roo_data = player_frame
    
    return roo_data

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

roo_data = init_baselines()
hold_display = roo_data
lineup_display = []
check_list = []
rand_player = 0
boost_player = 0
salaryCut = 0

tab1, tab2 = st.tabs(["Player Overall Projections", "Not Ready Yet"])

with tab1:
    if st.button("Reset Data", key='reset1'):
              # Clear values from *all* all in-memory and on-disk data caches:
              # i.e. clear values from both square and cube
              st.cache_data.clear()
              roo_data = init_baselines()
              hold_display = roo_data
              lineup_display = []
              check_list = []
              rand_player = 0
              boost_player = 0
              salaryCut = 0
    
    hold_container = st.empty()
    display = hold_display.set_index('Player')
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True)
    st.download_button(
        label="Export Projections",
        data=convert_df_to_csv(display),
        file_name='PGA_DFS_export.csv',
        mime='text/csv',
    )

with tab2:
     st.write("Not Ready Yet")