Initial commit for bridge stage

.streamlit/secrets.toml

@@ -0,0 +1 @@
+PGA_models = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1733549835'

Dockerfile

@@ -5,11 +5,23 @@ 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 PGA_DATA='https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1733549835'
+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 +1,9 @@
-altair
-pandas
-streamlit
+streamlit
+gspread
+openpyxl
+matplotlib
+streamlit-aggrid
+pulp
+docker
+plotly
+scipy

src/database.py

@@ -0,0 +1,19 @@
+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": "1005124050c80d085e2c5b344345715978dd9cc9",
+  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\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)

src/streamlit_app.py

@@ -1,40 +1,78 @@
-import altair as alt
-import numpy as np
 import pandas as pd
 import streamlit as st
+import os
+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")
+
+PGA_models = os.getenv('PGA_DATA')
+
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stElementContainer [data-baseweb="button-group"] {
+        gap: 2.000rem;
+        padding: 4px;
+    }
+    .stElementContainer [kind="segmented_control"] {
+        height: 2.000rem;
+        white-space: pre-wrap;
+        background-color: #DAA520;
+        color: white;
+        border-radius: 20px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+    .stElementContainer [kind="segmented_controlActive"] {
+        height: 3.000rem;
+        background-color: #DAA520;
+        border: 3px solid #FFD700;
+        border-radius: 10px;
+        color: black;
+    }
+    .stElementContainer [kind="segmented_control"]:hover {
+        background-color: #FFD700;
+        cursor: pointer;
+    }
+
+    div[data-baseweb="select"] > div {
+        background-color: #DAA520;
+        color: white;
+    }
+
+</style>""", unsafe_allow_html=True)
+
+@st.cache_data
+def load_dk_player_model(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Odds_vs_Salary')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.drop(['ID', 'Roster Position', 'Cut%'], axis=1)
+    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    data_cols = raw_display.columns.drop(['Player'])
+    raw_display[data_cols] = raw_display[data_cols].apply(pd.to_numeric, errors='coerce')
+
+    return raw_display
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+format_dict = {'Cut%': '{:.2%}', 'Win Odds': '{:.2%}','T5 Odds': '{:.2%}', 'T10 Odds': '{:.2%}','T20 Odds': '{:.2%}'}
+
+
+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_dk_player_model(PGA_models)
+display = hold_display.set_index('Player')
+st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(format_dict, precision=2), use_container_width = True)
+st.download_button(
+    label="Export Model",
+    data=convert_df_to_csv(display),
+    file_name='PGA_DK_Vegas_Salary_export.csv',
+    mime='text/csv',
+    key='DK_download',
+)