initial commit and modernizatrion

.streamlit/secrets.toml

@@ -0,0 +1,3 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+client_email = "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com"
+private_key_id = "1005124050c80d085e2c5b344345715978dd9cc9"

Dockerfile

@@ -5,11 +5,24 @@ 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 MONGO_URI="mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+ENV CLIENT_EMAIL="gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com"
+ENV PRIVATE_KEY_ID="1005124050c80d085e2c5b344345715978dd9cc9"
+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
 

src/database.py

@@ -0,0 +1,21 @@
+import gspread
+import streamlit as st
+import os
+
+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": os.getenv('PRIVATE_KEY_ID'),
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+  "client_email": os.getenv('CLIENT_EMAIL'),
+  "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)

src/streamlit_app.py

@@ -1,40 +1,298 @@
-import altair as alt
-import numpy as np
 import pandas as pd
 import streamlit as st
+import numpy as np
+from database import gc
+
+st.set_page_config(layout="wide")
+
+@st.cache_data
+def init_baselines():
+    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit#gid=1858245367')
+    worksheet = sh.worksheet('ROO')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Salary'] > 0]
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    roo_table = raw_display.sort_values(by='Median', ascending=False)
+    
+    # worksheet = sh.worksheet('Positional_Boosts')
+    # raw_display = pd.DataFrame(worksheet.get_all_records())
+    # raw_display.replace("", 'Welp', inplace=True)
+    # raw_display = raw_display.loc[raw_display['teamname'] != 'Welp']
+    # raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    # positional_boosts = raw_display.sort_values(by='Avg_Allowed', ascending=False)
+    
+    worksheet = sh.worksheet('Overall_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Win_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Loss_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Overall_BO1_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_bo1 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO3_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_bo3 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO5_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lck_bo5 = raw_display
+    
+    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit#gid=1858245367')
+    worksheet = sh.worksheet('Overall_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Win_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Loss_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Overall_BO1_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_bo1 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO3_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_bo3 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO5_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lcs_bo5 = raw_display
+    
+    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1oOJD_QcBeDJ1f7e9FfgUHOQEPT6kvU0Sa9hQ_4B8gqc/edit?gid=1288836099#gid=1288836099')
+    worksheet = sh.worksheet('Overall_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Win_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Loss_Stacks')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
+    
+    worksheet = sh.worksheet('Overall_BO1_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_bo1 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO3_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_bo3 = raw_display
+    
+    worksheet = sh.worksheet('Overall_BO5_Stats')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    lec_bo5 = raw_display
+
+    return roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5
+
+roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
+
+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()
+              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
+    league_choice1 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var1')
+    if league_choice1 == 'LCK/LPL':
+      league_hold = lck_overall_stacks
+    elif league_choice1 == 'LCS':
+      league_hold = lcs_overall_stacks
+    elif league_choice1 == 'LEC':
+      league_hold = lec_overall_stacks
+    display = league_hold.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()
+              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
+    with st.container():
+        col1, col2, col3, col4 = st.columns([4, 2, 2, 2])
+
+        with col1:
+            league_choice2 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var2')
+            if league_choice2 == 'LCK/LPL':
+              league_hold = roo_table[roo_table['league'] == 'LCK']
+            elif league_choice2 == 'LCS':
+              league_hold = roo_table[roo_table['league'] == 'LCS']
+            elif league_choice2 == 'LEC':
+              league_hold = roo_table[roo_table['league'] == 'LEC']
+        with col2:
+            model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
+            if model_choice == 'Overall':
+              hold_display = league_hold[league_hold['type'] == 'Overall']
+            elif model_choice == 'Wins':
+              hold_display = league_hold[league_hold['type'] == 'Wins']
+            elif model_choice == 'Losses':
+              hold_display = league_hold[league_hold['type'] == 'Losses']
+        with col3:
+            pos_var1 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'roo_posvar')
+        with col4:
+            team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar')
+    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)]
+    display = display.drop(columns=['type', 'league', 'Timestamp'])
+    display['Cpt_Own'] = (display['Own'] / 2) * ((100 - (100-display['Own']))/100)
+    display['Cpt_Own'] = np.where(display['Position'] == 'TEAM', display['Cpt_Own'].clip(upper=.25), display['Cpt_Own'])
+    scale_var = display['Cpt_Own'].sum()
+    display['Cpt_Own'] = display['Cpt_Own'] * (100 / scale_var)
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=750, 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()
+              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
+    with st.container():
+        col1, col2, col3, col4 = st.columns([4, 2, 2, 2])
 
-"""
-# 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 col1:
+            league_choice3 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var3')
+        with col2:
+            gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
+        with col3:
+            pos_var2 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'proj_posvar')
+        with col4:
+            team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'proj_teamvar')
+            
+    if league_choice3 == 'LCK/LPL':
+        if gametype_choice == 'Best of 1':
+          hold_display = lck_bo1
+        elif gametype_choice == 'Best of 3':
+          hold_display = lck_bo3
+        elif gametype_choice == 'Best of 5':
+          hold_display = lck_bo5
+        display = hold_display.set_index('Player')
+    elif league_choice3 == 'LCS':
+        if gametype_choice == 'Best of 1':
+          hold_display = lcs_bo1
+        elif gametype_choice == 'Best of 3':
+          hold_display = lcs_bo3
+        elif gametype_choice == 'Best of 5':
+          hold_display = lcs_bo5
+        display = hold_display.set_index('Player')
+    elif league_choice3 == 'LEC':
+        if gametype_choice == 'Best of 1':
+          hold_display = lec_bo1
+        elif gametype_choice == 'Best of 3':
+          hold_display = lec_bo3
+        elif gametype_choice == 'Best of 5':
+          hold_display = lec_bo5
+        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(precision=2), height=750, 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',
+    )