Create app.py

app.py

@@ -0,0 +1,195 @@
+import pulp
+import numpy as np
+import pandas as pd
+import random
+import sys
+import openpyxl
+import re
+import time
+import streamlit as st
+import matplotlib
+from  matplotlib.colors import LinearSegmentedColormap
+from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
+import json
+import requests
+import gspread
+import plotly.figure_factory as ff
+
+scope = ['https://www.googleapis.com/auth/spreadsheets',
+          "https://www.googleapis.com/auth/drive"]
+
+credentials = {
+  "type": "service_account",
+  "project_id": "sheets-api-connect-378620",
+  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
+  "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"
+}
+
+gc = gspread.service_account_from_dict(credentials)
+
+st.set_page_config(layout="wide")
+
+roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
+              '40+%': '{:.2%}','3x%': '{:.2%}','4x%': '{:.2%}','5x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}'}
+stat_format = {'Odds%': '{:.2%}', 'Boosts': '{:.2%}'}
+
+overall_table = 'LOL_Overall_Proj'
+wins_table = 'LOL_Win_Proj'
+losses_table = 'LOL_Loss_Proj'
+stacks_table = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
+bo1_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
+bo3_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
+bo5_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
+
+@st.cache_data
+def load_roo_model(URL):
+    sh = gc.open(URL)
+    worksheet = sh.get_worksheet(0)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float)
+    raw_display['Top_finish'] = raw_display['Top_finish'].str.replace('%', '').astype(float)/100
+    raw_display['Top_5_finish'] = raw_display['Top_5_finish'].str.replace('%', '').astype(float)/100
+    raw_display['Top_10_finish'] = raw_display['Top_10_finish'].str.replace('%', '').astype(float)/100
+    raw_display['40+%'] = raw_display['40+%'].str.replace('%', '').astype(float)/100
+    raw_display['3x%'] = raw_display['3x%'].str.replace('%', '').astype(float)/100
+    raw_display['4x%'] = raw_display['4x%'].str.replace('%', '').astype(float)/100
+    raw_display['5x%'] = raw_display['5x%'].str.replace('%', '').astype(float)/100
+    raw_display['Own'] = raw_display['Own'].str.replace('%', '').astype(float)/100
+    raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100
+
+    return raw_display
+
+@st.cache_data
+def load_bo1_proj_model(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(13)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
+    raw_display['Boosts'] = raw_display['Boosts'].str.replace('%', '').astype(float)/100
+    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    raw_display = raw_display.sort_values(by='Kills', ascending=False)
+
+    return raw_display
+
+@st.cache_data
+def load_bo3_proj_model(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(14)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
+    raw_display['Boosts'] = raw_display['Boosts'].str.replace('%', '').astype(float)/100
+    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    raw_display = raw_display.sort_values(by='Kills', ascending=False)
+
+    return raw_display
+
+@st.cache_data
+def load_bo5_proj_model(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(15)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
+    raw_display['Boosts'] = raw_display['Boosts'].str.replace('%', '').astype(float)/100
+    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    raw_display = raw_display.sort_values(by='Kills', ascending=False)
+          
+    return raw_display  
+
+@st.cache_data
+def load_stacks_table(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(9)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.sort_values(by='Stack+', ascending=False)
+
+    return raw_display    
+  
+tab1, tab2, tab3 = st.tabs(["LOL Stacks Table", "LOL Range of Outcomes", "LOL Player Base Stats"])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+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()
+    hold_display = load_stacks_table(stacks_table)
+    display = hold_display.set_index('Team')
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Stacks",
+        data=convert_df_to_csv(display),
+        file_name='LOL_Stacks_export.csv',
+        mime='text/csv',
+    )
+  
+with tab2:
+    if st.button("Reset Data", key='reset2'):
+              # 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()
+    model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
+    pos_var1 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'roo_posvar')
+    team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar')
+    if model_choice == 'Overall':
+      hold_display = load_roo_model(overall_table)
+    elif model_choice == 'Wins':
+      hold_display = load_roo_model(wins_table)
+    elif model_choice == 'Losses':
+      hold_display = load_roo_model(losses_table)
+    display = hold_display.set_index('Player')
+    if team_var1:
+          display = display[display['Team'].isin(team_var1)]
+    if pos_var1 == 'All':
+          display = display
+    elif pos_var1 != 'All':
+          display = display[display['Position'].str.contains(pos_var1)]
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Range of Outcomes",
+        data=convert_df_to_csv(display),
+        file_name='LOL_ROO_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    if st.button("Reset Data", key='reset3'):
+              # 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()
+    gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
+    pos_var2 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'proj_posvar')
+    team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'proj_teamvar')
+    if gametype_choice == 'Best of 1':
+      hold_display = load_bo1_proj_model(bo1_player_stats)
+    elif gametype_choice == 'Best of 3':
+      hold_display = load_bo3_proj_model(bo3_player_stats)
+    elif gametype_choice == 'Best of 5':
+      hold_display = load_bo5_proj_model(bo5_player_stats)
+    display = hold_display.set_index('Player')
+    if team_var2:
+          display = display[display['Team'].isin(team_var2)]
+    if pos_var2 == 'All':
+          display = display
+    elif pos_var2 != 'All':
+          display = display[display['Position'].str.contains(pos_var2)]
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(stat_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Baselines",
+        data=convert_df_to_csv(display),
+        file_name='LOL_Baselines_export.csv',
+        mime='text/csv',
+    )