Initial Commit and modernization

.streamlit/secrets.toml

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

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 PRIVATE_KEY_ID="1005124050c80d085e2c5b344345715978dd9cc9"
+ENV CLIENT_EMAIL="gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com"
+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 +1,8 @@
-altair
-pandas
-streamlit
+streamlit
+gspread
+openpyxl
+matplotlib
+pulp
+docker
+plotly
+scipy

src/database.py

@@ -0,0 +1,23 @@
+import streamlit as st
+import pymongo
+import os
+
+import gspread
+
+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"
+}
+
+gspreadcon = gspread.service_account_from_dict(credentials)

src/streamlit_app.py

@@ -1,40 +1,122 @@
-import altair as alt
+import pulp
 import numpy as np
 import pandas as pd
 import streamlit as st
+from database import gspreadcon
 
-"""
-# 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 = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
+              '120+%': '{:.2%}','10x%': '{:.2%}','11x%': '{:.2%}','12x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}', 'CPT_Own': '{.2%}'}
+
+odds_format = {'Odds': '{:.2%}'}
+
+stat_format = {'Odds%': '{:.2%}'}
+
+map_proj_format = {'Win%': '{:.2%}'}
+
+master_hold = 'https://docs.google.com/spreadsheets/d/1dOXsbeWbvWjRyohsEEDXOiWji4-1R1J6E-Lu2CSM9AM/edit#gid=928272897'
+
+@st.cache_resource(ttl=600)
+def pull_baselines():
+    sh = gspreadcon.open_by_url(master_hold)
+    
+    worksheet = sh.worksheet('Overall_Vegas')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.loc[raw_display['Team'] != ""]
+    odds_table = raw_display[['Team', 'Vegas', 'Odds', 'Games']]
+    
+    worksheet = sh.worksheet('Overall_ROO')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    overall_roo = raw_display.loc[raw_display['Player'] != ""]
+    
+    worksheet = sh.worksheet('Win_ROO')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    win_roo = raw_display.loc[raw_display['Player'] != ""]
+    
+    worksheet = sh.worksheet('Loss_ROO')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    loss_roo = raw_display.loc[raw_display['Player'] != ""]
+    
+    worksheet = sh.worksheet('3_map_Proj')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.loc[raw_display['Player'] != ""]
+    map_proj_3 = raw_display[['Player', 'Team', 'Opponent', 'Odds', 'Win%', 'Avg Kills', 'Avg Deaths', 'Proj_Kills', 'Proj_Deaths']]
+    data_cols = map_proj_3.columns.drop(['Player', 'Team', 'Opponent', 'Win%'])
+    map_proj_3[data_cols] = map_proj_3[data_cols].apply(pd.to_numeric, errors='coerce')
+    
+    worksheet = sh.worksheet('Timestamp')
+    timestamp = worksheet.acell('A1').value
+
+    return odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+
+tab1, tab2, tab3 = st.tabs(["COD Odds Tables", "COD Range of Outcomes", "COD 3-map projections"])
+
+with tab1:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    odds_display = odds_table
+    st.dataframe(odds_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(odds_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Tables",
+        data=convert_df_to_csv(odds_display),
+        file_name='COD_Odds_Tables_export.csv',
+        mime='text/csv',
+    )
+
+with tab2:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset2'):
+             st.cache_data.clear()
+             odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
+             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
+    team_var1 = st.multiselect('View specific team?', options = overall_roo['Team'].unique(), key = 'roo_teamvar')
+    if model_choice == 'Overall':
+      hold_display = overall_roo
+    elif model_choice == 'Wins':
+      hold_display = win_roo
+    elif model_choice == 'Losses':
+      hold_display = loss_roo
+    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)
+    display = hold_display.set_index('Player')
+    export_display = display
+    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:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    team_var2 = st.multiselect('View specific team?', options = map_proj_3['Team'].unique(), key = 'stat_teamvar')
+    map_stat_display = map_proj_3
+    if team_var2:
+          map_stat_display = map_stat_display[display['Team'].isin(team_var2)]
+    st.dataframe(map_stat_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(map_proj_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Projections",
+        data=convert_df_to_csv(map_stat_display),
+        file_name='COD_Projections_export.csv',
+        mime='text/csv',
+    )