Initial modernization

.streamlit/secret.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 +5,25 @@ 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
 

requirements.txt

@@ -1,3 +1,5 @@
 altair
 pandas
-streamlit
+streamlit
+pymongo
+matplotlib

src/database.py

@@ -0,0 +1,20 @@
+import gspread
+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 +1,266 @@
-import altair as alt
+import pulp
 import numpy as np
 import pandas as pd
 import streamlit as st
+from database import gc
 
-"""
-# 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])),
-    ))
+st.set_page_config(layout="wide")
+
+roo_format = {'Win%': '{:.2%}', 'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
+              '60+%': '{:.2%}','5x%': '{:.2%}','6x%': '{:.2%}','7x%': '{:.2%}','Own': '{:.2%}', 'Cpt_Own': '{:.2%}','LevX': '{:.2%}'}
+stat_format = {'Odds%': '{:.2%}'}
+table_format = {'Odds': '{:.2%}'}
+
+csgo_overall = 'CSGO_Overall_Proj'
+csgo_rpl = 'CSGO_RPL_Proj'
+csgo_neutral = 'CSGO_Neutral_Proj'
+csgo_wins = 'CSGO_Win_Proj'
+csgo_losses = 'CSGO_Loss_Proj'
+overall_odds = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+RPL_odds = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+csgo_bo1 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+two_map = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+csgo_bo3 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+csgo_bo5 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+player_baselines = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
+
+@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())
+    try:
+          raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float)
+    except:
+          pass
+    try:
+          raw_display['Win%'] = raw_display['Win%'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['Top_finish'] = raw_display['Top_finish'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['Top_5_finish'] = raw_display['Top_5_finish'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['Top_10_finish'] = raw_display['Top_10_finish'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try: 
+          raw_display['60+%'] = raw_display['60+%'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['5x%'] = raw_display['5x%'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['6x%'] = raw_display['6x%'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['7x%'] = raw_display['7x%'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['Own'] = raw_display['Own'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+    try:
+          raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100
+    except:
+          pass
+
+    return raw_display
+
+@st.cache_data
+def load_overall_odds(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Overall_Vegas')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display['Odds'] = raw_display['Odds'].str.replace('%', '').astype(float)/100
+
+    return raw_display
+
+@st.cache_data
+def load_rpl_odds(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('RPL_Vegas')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display['Odds'] = raw_display['Odds'].str.replace('%', '').astype(float)/100
+    raw_display['Vegas'] = raw_display['Vegas'].str.replace('%', '').astype(float)/100
+    raw_display = raw_display[['Team', 'Opponent', 'RPL', 'Opp_RPL', 'RPL_Diff', 'Vegas', 'Odds', 'P Rounds']]
+
+    return raw_display
+
+@st.cache_data
+def load_bo1_proj_model(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Overall_BO1_Projections')
+    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 = raw_display.sort_values(by='Kills', ascending=False)
+
+    return raw_display
+
+@st.cache_data
+def two_map_load(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('2_map_projections')
+    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 = 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.worksheet('Overall_BO3_Projections')
+    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 = 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.worksheet('Overall_BO5_Projections')
+    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 = raw_display.sort_values(by='Kills', ascending=False)
+          
+    return raw_display  
+
+@st.cache_data
+def load_slate_baselines(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Player_Data')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    raw_display = raw_display.sort_values(by='Kills/Round', ascending=False)
+
+    return raw_display    
+
+hold_display = load_roo_model(csgo_overall)
+
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["CSGO Odds Tables", "CSGO Range of Outcomes", "CSGO Player Stat Projections", "CSGO Slate Baselines", '2-map Projections'])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+with tab1:
+    if st.button("Reset Data", key='reset4'):
+              # 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()
+    odds_choice = st.radio("What table would you like to display?", ('Overall', 'RPL'), key='odds_table')
+    if odds_choice == 'Overall':
+        hold_display = load_overall_odds(overall_odds)
+    elif odds_choice == 'RPL':
+        hold_display = load_rpl_odds(RPL_odds)
+    display = hold_display.set_index('Team')
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(table_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Tables",
+        data=convert_df_to_csv(display),
+        file_name='CSGO_Odds_Tables_export.csv',
+        mime='text/csv',
+    )
+
+with tab2:
+    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()
+    model_choice = st.radio("What table would you like to display?", ('Overall', 'RPL', 'Neutral', 'Wins', 'Losses'), key='roo_table')
+    team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar')
+    if model_choice == 'Overall':
+      hold_display = load_roo_model(csgo_overall)
+    elif model_choice == 'RPL':
+      hold_display = load_roo_model(csgo_rpl)
+    elif model_choice == 'Neutral':
+      hold_display = load_roo_model(csgo_neutral)
+    elif model_choice == 'Wins':
+      hold_display = load_roo_model(csgo_wins)
+    elif model_choice == 'Losses':
+      hold_display = load_roo_model(csgo_losses)
+    hold_display['Cpt_Own'] = (hold_display['Own']) * ((100 - (100-hold_display['Own'])))
+    cpt_own_norm = 100 / hold_display['Cpt_Own'].sum()
+    hold_display['Cpt_Own'] = (hold_display['Cpt_Own'] * cpt_own_norm) / 100
+    hold_display['Own'] = hold_display['Own'] / 100
+    display = hold_display.set_index('Player')
+    export_display = display
+    export_display['Own'] =  export_display['Own'] *100
+    export_display['Position'] = "FLEX"
+    if team_var1:
+              display = display[display['Team'].isin(team_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(export_display),
+        file_name='CSGO_ROO_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    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()
+    gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
+    team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'stat_teamvar')
+    if gametype_choice == 'Best of 1':
+      hold_display = load_bo1_proj_model(csgo_bo1)
+    elif gametype_choice == 'Best of 3':
+      hold_display = load_bo3_proj_model(csgo_bo3)
+    elif gametype_choice == 'Best of 5':
+      hold_display = load_bo5_proj_model(csgo_bo5)
+    display = hold_display.set_index('Player')
+    if team_var2:
+          display = display[display['Team'].isin(team_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 Projections",
+        data=convert_df_to_csv(display),
+        file_name='CSGO_Projections_export.csv',
+        mime='text/csv',
+    )
+
+with tab4:
+    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()
+    hold_display = load_slate_baselines(player_baselines)
+    display = hold_display.set_index('Player')
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Baselines",
+        data=convert_df_to_csv(display),
+        file_name='CSGO_Baselines_export.csv',
+        mime='text/csv',
+    )
+
+with tab5:
+    if st.button("Reset Data", key='reset5'):
+              # 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 = two_map_load(two_map)
+    display = hold_display.set_index('Player')
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Baselines",
+        data=convert_df_to_csv(display),
+        file_name='CSGO_2_map_export.csv',
+        mime='text/csv',
+    )