Initial commit for v2 of Streamlit app.

.streamlit/secrets.toml

@@ -0,0 +1 @@
+master_hold = 'https://docs.google.com/spreadsheets/d/1D526UlXmrz-8qxVcUKrA-u7f6FftUiBufxDnzQv980k/edit#gid=791804525'

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 MASTER_HOLD="https://docs.google.com/spreadsheets/d/1D526UlXmrz-8qxVcUKrA-u7f6FftUiBufxDnzQv980k/edit#gid=791804525"
+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 +1,24 @@
+import streamlit as st
+import gspread
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://www.googleapis.com/auth/spreadsheets',
+        "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)
+      
+        return gc_con

src/streamlit_app.py

@@ -1,40 +1,348 @@
-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
+import os
+from database import init_conn
+
+gcservice_account = init_conn()
+
+master_hold = os.getenv('MASTER_HOLD')
+
+sim_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}'}
+
+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_resource(ttl = 600)
+def init_baselines():
+    sh = gcservice_account.open_by_url(master_hold)
+    worksheet = sh.worksheet('Pitcher_Proj')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", np.nan, inplace=True)
+    pitcher_proj = raw_display.dropna()
+
+    sh = gcservice_account.open_by_url(master_hold)
+    worksheet = sh.worksheet('Hitter_Proj')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace("", np.nan, inplace=True)
+    hitter_proj = raw_display.dropna()
+    
+    sh = gcservice_account.open_by_url(master_hold)
+    worksheet = sh.worksheet('Display')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    wins_proj = raw_display.dropna()
+    
+    return pitcher_proj, hitter_proj, wins_proj
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+pitcher_proj, hitter_proj, wins_proj = init_baselines()
+total_teams = pitcher_proj['Team'].values.tolist()
+
+selected_tab = st.segmented_control(
+    "Select Tab",
+    options=["Team Win Projections", "Pitcher Projections", "Hitter Projections", "Pitcher Simulations", "Hitter Simulations"],
+    selection_mode='single',
+    default='Team Win Projections',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+
+if selected_tab == 'Team Win Projections':
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              pitcher_proj, hitter_proj, wins_proj = init_baselines()
+              total_teams = pitcher_proj['Team'].values.tolist()
+    raw_frame = wins_proj.copy()
+    export_frame_team = raw_frame[['Team', '2B', 'HR', 'SB', 'P_SO', 'P_H', 'P_R', 'P_HR', 'P_BB', 'LY Added', 'Added', 'LY Adj Wins', 'Adj Wins', 'Vegas', 'Proj wins', 'Diff']]
+    export_frame_team = export_frame_team.sort_values(by='Proj wins', ascending=False)
+    disp_frame = raw_frame[['Team', '2B', 'HR', 'SB', 'P_SO', 'P_H', 'P_R', 'P_HR', 'P_BB', 'LY Added', 'Added', 'LY Adj Wins', 'Adj Wins', 'Vegas', 'Proj wins', 'Diff']]
+    disp_frame = disp_frame.sort_values(by='Proj wins', ascending=False)
+
+    st.dataframe(disp_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+    
+    st.download_button(
+        label="Export Team Win Projections",
+        data=convert_df_to_csv(export_frame_team),
+        file_name='MLB_team_win_export.csv',
+        mime='text/csv',
+        key='team_win_export',
+    )
+
+elif selected_tab == 'Pitcher Projections':
+    if st.button("Reset Data", key='reset2'):
+              st.cache_data.clear()
+              pitcher_proj, hitter_proj, wins_proj = init_baselines()
+              total_teams = pitcher_proj['Team'].values.tolist()
+    raw_frame = pitcher_proj.copy()
+    split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
+    if split_var1 == 'Specific Teams':
+        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1')
+    elif split_var1 == 'All':
+        team_var1 = total_teams
+    
+    working_data = raw_frame[raw_frame['Team'].isin(team_var1)]
+    export_frame_sp = raw_frame[['Name', 'Team', 'TBF', 'Ceiling_var', 'True_AVG', 'Hits', 'Singles%', 'Singles', 'Doubles%', 'Doubles', 'xHR%', 'Homeruns', 'Strikeout%', 'Strikeouts',
+                              'Walk%', 'Walks', 'Runs%', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts', 'DK_fpts']]
+    disp_frame_sp = working_data[['Name', 'Team', 'TBF', 'True_AVG', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'Strikeouts',
+                            'Walks', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts', 'DK_fpts']]
+    disp_frame_sp = disp_frame_sp.sort_values(by='UD_fpts', ascending=False)
+    st.dataframe(disp_frame_sp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn_r').background_gradient(cmap='RdYlGn', subset=['TBF', 'Strikeouts', 'Wins', 'Quality_starts', 'UD_fpts', 'DK_fpts']).format(precision=2), height = 1000, use_container_width = True)
+    
+    st.download_button(
+        label="Export Pitcher Projections",
+        data=convert_df_to_csv(export_frame_sp),
+        file_name='MLB_pitcher_proj_export.csv',
+        mime='text/csv',
+        key='pitcher_proj_export',
+    )
+
+elif selected_tab == 'Hitter Projections':
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              pitcher_proj, hitter_proj, wins_proj = init_baselines()
+              total_teams = pitcher_proj['Team'].values.tolist()
+    raw_frame = hitter_proj.copy()
+    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_var2 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var2')
+    elif split_var2 == 'All':
+        team_var2 = total_teams
+    
+    working_data = raw_frame[raw_frame['Team'].isin(team_var2)]
+    export_frame_h = raw_frame[['Name', 'Team', 'PA', 'Ceiling_var', 'Walk%', 'Walks', 'xHits', 'Singles%', 'Singles', 'Doubles%', 'Doubles',
+                              'xHR%', 'Homeruns', 'Runs%', 'Runs', 'RBI%', 'RBI', 'Steal%', 'Stolen_bases', 'ADP', 'UD_fpts', 'DK_fpts']]
+    disp_frame_h = working_data[['Name', 'Team', 'PA', 'Walks', 'xHits', 'Singles', 'Doubles',
+                            'Homeruns', 'Runs', 'RBI', 'Stolen_bases', 'ADP', 'UD_fpts', 'DK_fpts']]
+    disp_frame_h = disp_frame_h.sort_values(by='UD_fpts', ascending=False)
+    st.dataframe(disp_frame_h.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['ADP']).format(precision=2), height = 1000, use_container_width = True)
+    
+    st.download_button(
+        label="Export Hitter Projections",
+        data=convert_df_to_csv(export_frame_h),
+        file_name='MLB_hitter_proj_export.csv',
+        mime='text/csv',
+        key='hitter_proj_export',
+    )
+    
+elif selected_tab == 'Pitcher Simulations':
+    if st.button("Reset Data", key='reset4'):
+              st.cache_data.clear()
+              pitcher_proj, hitter_proj, wins_proj = init_baselines()
+              total_teams = pitcher_proj['Team'].values.tolist()
+    col1, col2 = st.columns([1, 5])
+    
+    with col2:
+        df_hold_container = st.empty()
+    
+    with col1:
+        prop_type_var_sp = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Wins', 'Quality_starts'], key='prop_type_var_sp')
+
+        if st.button('Simulate Stat', key='sim_sp'):
+            with col2:
+                   
+                    with df_hold_container.container():
+
+                        df = pitcher_proj.copy()
+                        
+                        total_sims = 5000
+
+                        df.replace("", 0, inplace=True)
+
+                        if prop_type_var_sp == 'Strikeouts':
+                            df['Median'] = df['Strikeouts']
+                            stat_cap = 300
+                        elif prop_type_var_sp == 'Wins':
+                            df['Median'] = df['Wins']
+                            stat_cap = 25
+                        elif prop_type_var_sp == 'Quality_starts':
+                            df['Median'] = df['Quality_starts']
+                            stat_cap = 30
+
+                        flex_file = df.copy()
+                        flex_file.rename(columns={"Name": "Player"}, inplace = True)
+                        flex_file['Floor'] = (flex_file['Median'] * .25)
+                        flex_file['Ceiling'] = np.where((flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])) > stat_cap, stat_cap + (flex_file['Median']/10), (flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])))
+                        flex_file['STD'] = (flex_file['Median']/3)
+                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        hold_file = flex_file.copy()
+                        hold_file = hold_file.sort_values(by='Median', ascending=False)
+                        overall_file = flex_file.copy()
+                        overall_file = overall_file.sort_values(by='Median', ascending=False)
+
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):
+                            overall_file['g'] = np.random.gumbel(overall_file['Median'] * .75,overall_file['STD'])
+                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])
+
+                        check_file = overall_file.copy()
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
+                        overall_file.astype('int').dtypes
+
+                        players_only = hold_file[['Player']]
+                        raw_lineups_file = players_only.copy()
+
+                        for x in range(0,total_sims):
+                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+
+                        players_only['Average_Rank'] = players_only.mean(axis=1)
+                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]
+
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
+                        final_Proj.rename(columns={"Median": "Projection"}, inplace = True)
+                        
+                    with df_hold_container.container():     
+                        df_hold_container = st.empty()
+                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(sim_format, precision=2), use_container_width = True)
+    
+elif selected_tab == 'Hitter Simulations':
+    if st.button("Reset Data", key='reset5'):
+              st.cache_data.clear()
+              pitcher_proj, hitter_proj, wins_proj = init_baselines()
+              total_teams = pitcher_proj['Team'].values.tolist()
+    col1, col2 = st.columns([1, 5])
+    
+    with col2:
+        df_hold_container = st.empty()
+    
+    with col1:
+        prop_type_var_h = st.selectbox('Select type of prop to simulate', options = ['Hits', 'Doubles', 'Home Runs', 'RBI', 'Stolen Bases'], key='prop_type_var_h')
+        
+
+        if st.button('Simulate Stat', key='sim_h'):
+            with col2:
+                   
+                    with df_hold_container.container():
+
+                        df = hitter_proj.copy()
+                        
+                        total_sims = 5000
+
+                        df.replace("", 0, inplace=True)
+
+                        if prop_type_var_h == 'Hits':
+                            df['Median'] = df['xHits']
+                            stat_cap = 250
+                        elif prop_type_var_h == 'Doubles':
+                            df['Median'] = df['Doubles']
+                            stat_cap = 65
+                        elif prop_type_var_h == 'Home Runs':
+                            df['Median'] = df['Homeruns']
+                            stat_cap = 75
+                        elif prop_type_var_h == 'RBI':
+                            df['Median'] = df['RBI']
+                            stat_cap = 150
+                        elif prop_type_var_h == 'Stolen Bases':
+                            df['Median'] = df['Stolen_bases']
+                            stat_cap = 80
+
+                        flex_file = df.copy()
+                        flex_file.rename(columns={"Name": "Player"}, inplace = True)
+                        flex_file['Floor'] = (flex_file['Median'] * .15)
+                        flex_file['Ceiling'] = np.where((flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])) > stat_cap, stat_cap + (flex_file['Median']/20), (flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])))
+                        flex_file['STD'] = (flex_file['Median']/2)
+                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        hold_file = flex_file.copy()
+                        hold_file = hold_file.sort_values(by='Median', ascending=False)
+                        overall_file = flex_file.copy()
+                        overall_file = overall_file.sort_values(by='Median', ascending=False)
+
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):
+                            overall_file['g'] = np.random.gumbel(overall_file['Median'] * .5,overall_file['STD'])
+                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])
+
+                        check_file = overall_file.copy()
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
+                        overall_file.astype('int').dtypes
+
+                        players_only = hold_file[['Player']]
+                        raw_lineups_file = players_only.copy()
+
+                        for x in range(0,total_sims):
+                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+
+                        players_only['Average_Rank'] = players_only.mean(axis=1)
+                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]
+
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
+                        final_Proj.rename(columns={"Median": "Projection"}, inplace = True)
 
-"""
-# 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 df_hold_container.container():     
+                        df_hold_container = st.empty()
+                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(sim_format, precision=2), use_container_width = True)