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Update app.py
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app.py
CHANGED
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import
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import numpy as np
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import pandas as pd
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import streamlit as st
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import gspread
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from itertools import combinations
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credentials = {
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}
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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@@ -33,69 +43,37 @@ player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_fi
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all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
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@st.cache_data
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def
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('Site_Info')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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@st.cache_data
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def player_stat_table():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('Player_Projections')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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@st.cache_data
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def load_dk_player_projections():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('DK_ROO')
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load_display = pd.DataFrame(worksheet.get_all_records())
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load_display.replace('', np.nan, inplace=True)
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raw_display = load_display.dropna(subset=['Median'])
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@st.cache_data
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def load_fd_player_projections():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('FD_ROO')
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load_display = pd.DataFrame(worksheet.get_all_records())
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load_display.replace('', np.nan, inplace=True)
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raw_display = load_display.dropna(subset=['Median'])
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return
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@st.cache_data
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def load_dk_stacks():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('DK_Stacks')
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load_display = pd.DataFrame(worksheet.get_all_records())
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raw_display = load_display
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return raw_display
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@st.cache_data
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def load_fd_stacks():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('FD_Stacks')
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load_display = pd.DataFrame(worksheet.get_all_records())
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raw_display = load_display
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return raw_display
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@st.cache_data
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def convert_df_to_csv(df):
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return df.to_csv().encode('utf-8')
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player_stats =
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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site_slates = set_slate_teams()
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col1, col2 = st.columns([1, 5])
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tab1, tab2 = st.tabs(['Uploads and Info', 'Stack Finder'])
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st.info(t_stamp)
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if st.button("Load/Reset Data", key='reset1'):
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st.cache_data.clear()
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player_stats =
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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site_slates = set_slate_teams()
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'), key='slate_var1')
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site_var1 = st.radio("What site are you playing?", ('Draftkings', 'Fanduel'), key='site_var1')
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import streamlit as st
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st.set_page_config(layout="wide")
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import numpy as np
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import pandas as pd
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import gspread
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import pymongo
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import streamlit as st
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from itertools import combinations
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@st.cache_resource
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def init_conn():
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scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
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credentials = {
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"type": "service_account",
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"project_id": "model-sheets-connect",
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"private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
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"private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
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"client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
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"client_id": "100369174533302798535",
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"auth_uri": "https://accounts.google.com/o/oauth2/auth",
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"token_uri": "https://oauth2.googleapis.com/token",
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
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}
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uri = "mongodb+srv://multichem:[email protected]/?retryWrites=true&w=majority&appName=TestCluster"
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
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db = client["testing_db"]
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MLB_Data = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'
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gc_con = gspread.service_account_from_dict(credentials, scope)
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return gc_con, db, MLB_Data
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gc, db, MLB_Data = init_conn()
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
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@st.cache_data(ttl = 599)
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def init_baselines():
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sh = gc.open_by_url(all_dk_player_projections)
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worksheet = sh.worksheet('Site_Info')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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site_slates = raw_display
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worksheet = sh.worksheet('Player_Projections')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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player_stats = raw_display
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worksheet = sh.worksheet('DK_ROO')
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load_display = pd.DataFrame(worksheet.get_all_records())
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load_display.replace('', np.nan, inplace=True)
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raw_display = load_display.dropna(subset=['Median'])
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dk_roo_raw = raw_display
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worksheet = sh.worksheet('FD_ROO')
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load_display = pd.DataFrame(worksheet.get_all_records())
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load_display.replace('', np.nan, inplace=True)
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raw_display = load_display.dropna(subset=['Median'])
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fd_roo_raw = raw_display
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return site_slates, player_stats, dk_roo_raw, fd_roo_raw
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@st.cache_data
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def convert_df_to_csv(df):
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return df.to_csv().encode('utf-8')
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site_slates, player_stats, dk_roo_raw, fd_roo_raw = init_baselines()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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col1, col2 = st.columns([1, 5])
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tab1, tab2 = st.tabs(['Uploads and Info', 'Stack Finder'])
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st.info(t_stamp)
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if st.button("Load/Reset Data", key='reset1'):
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st.cache_data.clear()
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site_slates, player_stats, dk_roo_raw, fd_roo_raw = init_baselines()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'), key='slate_var1')
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site_var1 = st.radio("What site are you playing?", ('Draftkings', 'Fanduel'), key='site_var1')
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