initial commit with app structure

.streamlit/secrets.toml

@@ -0,0 +1,2 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'

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 NBA_Data='https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
+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
+openpyxl
+matplotlib
+pulp
+docker
+plotly
+scipy
+pymongo

src/database.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import pymongo
+import os
+import gspread
+
+# @st.cache_resource
+# def init_conn():
+#          # Try to get from environment variable first, fall back to secrets
+#         uri = os.getenv('MONGO_URI')
+#         if not uri:
+#             uri = st.secrets['mongo_uri']
+#         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+#         db = client["NBA_Database"]
+
+#         return db
+    
+# db = init_conn()
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "client_id": "100369174533302798535",
+          "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%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+
+        gc_con = gspread.service_account_from_dict(credentials, scope)
+      
+        return gc_con
+
+gcservice_account = init_conn()

src/streamlit_app.py

@@ -1,40 +1,213 @@
-import altair as alt
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
 import numpy as np
 import pandas as pd
 import streamlit as st
+from database import gcservice_account
+from secrets import NBA_Data
+
+@st.cache_resource(ttl = 600)
+def init_baselines():
+    sh = gcservice_account.open_by_url(NBA_Data)
+    
+    worksheet = sh.worksheet('Trending')
+    raw_display = pd.DataFrame(worksheet.get_values())
+    raw_display.columns = raw_display.iloc[0]
+    raw_display = raw_display[1:]
+    raw_display = raw_display.reset_index(drop=True)
+    trend_table = raw_display[raw_display['PLAYER_NAME'] != ""]
+    trend_table.replace('', np.nan, inplace=True)
+    trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'Season MIN', 'Season Fantasy', 'Season FPPM', 'Season Ceiling', 'Season FD_Fantasy', 'Season FD_Ceiling', 'L10 MIN', 'L10 Fantasy', 'L10 FPPM', 'L10 Ceiling', 'L10 FD_Fantasy',
+                               'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 FPPM', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
+                               'L3 FPPM', 'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
+                               'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value',
+                               'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
+    trend_table['DK_Salary'] = trend_table['DK_Salary'].str.replace(',', '').astype(float)
+    trend_table['FD_Salary'] = trend_table['FD_Salary'].str.replace(',', '').astype(float)
+    trend_table = trend_table.dropna(subset=['Position'])
+    data_cols = trend_table.columns.drop(['PLAYER_NAME', 'Team', 'Position', 'FD_Position'])
+    trend_table[data_cols] = trend_table[data_cols].apply(pd.to_numeric, errors='coerce')
+    
+    dk_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
+    
+    fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
+    
+    dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
+    
+    fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]
+
+    dk_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
+    
+    fd_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
+    
+    dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']]
+    
+    fd_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'FD_Salary', 'FD_Proj', 'Adj FD_Median', 'FD_Avg_Val', 'Adj FD_Ceiling', 'FD_Ceiling_Value']]
+
+    return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
+
+col1, col2 = st.columns([1, 9])
+with col1:
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
+    split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'FPPM Trends', 'Slate specific', 'Overall'), key='split_var1')
+    site_var1 = st.radio("What site would you like to view?", ('Draftkings', 'Fanduel'), key='site_var1')
+    if site_var1 == 'Draftkings':
+        trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
+                                   'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
+                                   'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']]
+        minutes_table = dk_minutes_table
+        medians_table = dk_medians_table
+        fppm_table = dk_fppm_table
+        proj_medians_table = dk_proj_medians_table
+    elif site_var1 == 'Fanduel':
+        trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy',
+                                   'Trend FD_Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling',
+                                   'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
+        minutes_table = fd_minutes_table
+        medians_table = fd_medians_table
+        fppm_table = fd_fppm_table
+        proj_medians_table = fd_proj_medians_table
+    trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
+                                        'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
+                                        'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
+    minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
+    medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
+    fppm_table = fppm_table.set_axis(['PLAYER_NAME', 'Team', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM'], axis=1)
+    proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj',
+                                                      'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1)
+    if split_var1 == 'Overall':
+        view_var1 = trend_table.Team.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+        
+        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
+        if split_var3 == 'Specific Positions':
+            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
+        elif split_var3 == 'All':
+            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
+        
+        proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')
+            
+    elif split_var1 == 'Minutes Trends':
+        view_var1 = trend_table.Team.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+        
+    elif split_var1 == 'Fantasy Trends':
+        view_var1 = trend_table.Team.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+            
+    elif split_var1 == 'FPPM Trends':
+        view_var1 = trend_table.Team.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+            
+    elif split_var1 == 'Slate specific':
+        view_var1 = trend_table.Team.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+        
+        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
+        if split_var3 == 'Specific Positions':
+            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
+        elif split_var3 == 'All':
+            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
+        
+        proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')
 
-"""
-# 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 col2:
+    if split_var1 == 'Overall':
+        table_display = trend_table[trend_table['Proj'] >= proj_var1[0]]
+        table_display = table_display[table_display['Proj'] <= proj_var1[1]]
+        table_display = table_display[table_display['Team'].isin(team_var1)]
+        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
+        table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='Trending_export.csv',
+            mime='text/csv',
+        )
+            
+    elif split_var1 == 'Minutes Trends':
+        table_display = minutes_table[minutes_table['Team'].isin(team_var1)]
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='Trending_export.csv',
+            mime='text/csv',
+        )
+        
+    elif split_var1 == 'Fantasy Trends':
+        table_display = medians_table[medians_table['Team'].isin(team_var1)]
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='Trending_export.csv',
+            mime='text/csv',
+        )
+        
+    elif split_var1 == 'FPPM Trends':
+        table_display = fppm_table[fppm_table['Team'].isin(team_var1)]
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='Trending_export.csv',
+            mime='text/csv',
+        )
+        
+    elif split_var1 == 'Slate specific':
+        table_display = proj_medians_table[proj_medians_table['Proj'] >= proj_var1[0]]
+        table_display = table_display[table_display['Proj'] <= proj_var1[1]]
+        table_display = table_display[table_display['Team'].isin(team_var1)]
+        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
+        table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='NBA_Trending_export.csv',
+            mime='text/csv',
+        )