Add Streamlit app for NHL pivot analysis with MongoDB integration and deployment configuration

app.py

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+import numpy as np
+from numpy import random
+import pandas as pd
+import streamlit as st
+import pymongo
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def init_conn():
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NHL_Database"]
+
+        return db
+    
+db = init_conn()
+
+wrong_acro = ['WSH', 'AZ']
+right_acro = ['WAS', 'ARI']
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
+                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
+                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
+
+@st.cache_resource(ttl = 599)
+def player_stat_table():
+    collection = db["Player_Level_ROO"] 
+    cursor = collection.find()
+    load_display = pd.DataFrame(cursor)
+    
+    load_display.replace('', np.nan, inplace=True)
+    player_stats = load_display.copy()
+
+    dk_load_display = load_display[load_display['Site'] == 'Draftkings']
+    fd_load_display = load_display[load_display['Site'] == 'Fanduel']
+
+    dk_load_display = dk_load_display.sort_values(by='Own', ascending=False)
+    fd_load_display = fd_load_display.sort_values(by='Own', ascending=False)
+
+    dk_load_display = dk_load_display.dropna(subset=['Own'])
+    fd_load_display = fd_load_display.dropna(subset=['Own'])
+    
+    dk_roo_raw = dk_load_display
+    fd_roo_raw = fd_load_display
+
+    return player_stats, dk_roo_raw, fd_roo_raw
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+player_stats, dk_roo_raw, fd_roo_raw = player_stat_table()
+opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+
+tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info'])
+
+with tab1:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              player_stats, dk_roo_raw, fd_roo_raw = player_stat_table()
+              opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+        data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+        if site_var1 == 'Draftkings':
+              if data_var1 == 'User':
+                  raw_baselines = proj_dataframe
+              elif data_var1 != 'User':
+                  raw_baselines = dk_roo_raw[dk_roo_raw['Slate'] == 'Main Slate']
+                  raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+        elif site_var1 == 'Fanduel':
+              if data_var1 == 'User':
+                  raw_baselines = proj_dataframe
+              elif data_var1 != 'User':
+                  raw_baselines = fd_roo_raw[fd_roo_raw['Slate'] == 'Main Slate']
+                  raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+        check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq')
+        if check_seq == 'Single Player':
+            player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player')
+        elif check_seq == 'Top X Owned':
+            top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1)
+        Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
+        Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
+        pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
+        if pos_var1 == 'Specific Positions':
+            pos_var_list = st.multiselect('Which positions would you like to include?', options = raw_baselines['Position'].unique(), key='pos_var_list')
+        elif pos_var1 == 'All Positions':
+            pos_var_list = raw_baselines.Position.values.tolist()
+        split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+        if split_var1 == 'Specific Games':
+            team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1')
+        elif split_var1 == 'Full Slate Run':
+            team_var1 = raw_baselines.Team.values.tolist()
+        
+    with col2:
+        placeholder = st.empty()
+        displayholder = st.empty()
+        
+        if st.button('Simulate appropriate pivots'):
+            with placeholder:
+                if site_var1 == 'Draftkings':
+                        working_roo = raw_baselines
+                        working_roo.replace('', 0, inplace=True)
+                if site_var1 == 'Fanduel':
+                        working_roo = raw_baselines
+                        working_roo.replace('', 0, inplace=True)
+                        
+                own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                pos_dict = dict(zip(working_roo.Player, working_roo.Position))
+                total_sims = 1000
+
+                if check_seq == 'Single Player':
+                    player_var = working_roo.loc[working_roo['Player'] == player_check]
+                    player_var = player_var.reset_index()
+                    
+                    working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
+                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
+                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
+    
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
+                    flex_file['Floor_raw'] = flex_file['Median'] * .25
+                    flex_file['Ceiling_raw'] = flex_file['Median'] * 2
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+                    flex_file['STD'] = flex_file['Median'] / 3
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file.copy()
+                    overall_file = flex_file.copy()
+                    salary_file = flex_file.copy()
+    
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+                        overall_file[x] = random.normal(overall_file['Median'],overall_file['STD'])
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                    salary_file = salary_file.div(1000)
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    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)
+    
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+    
+                    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['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+    
+                    players_only['Player'] = hold_file[['Player']]
+    
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+    
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+    
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+
+                elif check_seq == 'Top X Owned':
+                    if pos_var1 == 'Specific Positions':    
+                        raw_baselines = raw_baselines[raw_baselines['Position'].isin(pos_var_list)]
+                    player_check = raw_baselines['Player'].head(top_x_var).tolist()
+                    final_proj_list = []
+                    for players in player_check:
+                        players_pos = pos_dict[players]
+                        player_var = working_roo.loc[working_roo['Player'] == players]
+                        player_var = player_var.reset_index()
+                        working_roo_temp = working_roo[working_roo['Position'] == players_pos]
+                        working_roo_temp = working_roo_temp[working_roo_temp['Team'].isin(team_var1)]
+                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)]
+                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)]
+                        
+                        flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median']]
+                        flex_file['Floor_raw'] = flex_file['Median'] * .25
+                        flex_file['Ceiling_raw'] = flex_file['Median'] * 2
+                        flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
+                        flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
+                        flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+                        flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+                        flex_file['STD'] = flex_file['Median'] / 3
+                        flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                        hold_file = flex_file.copy()
+                        overall_file = flex_file.copy()
+                        salary_file = flex_file.copy()
+                        
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):    
+                            salary_file[x] = salary_file['Salary']
+                            overall_file[x] = random.normal(overall_file['Median'],overall_file['STD'])
+
+                        salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                        salary_file = salary_file.div(1000)
+
+                        overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                        players_only = hold_file[['Player']]
+                        raw_lineups_file = players_only
+
+                        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)
+        
+                        salary_2x_check = (overall_file - (salary_file*2))
+                        salary_3x_check = (overall_file - (salary_file*3))
+                        salary_4x_check = (overall_file - (salary_file*4))
+        
+                        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['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                        players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+        
+                        players_only['Player'] = hold_file[['Player']]
+        
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+        
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                        final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                        final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                        final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                        final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                        final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                        final_Proj['LevX'] = 0
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+                        final_Proj['Pivot_source'] = players
+        
+                        final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                        
+                        final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                        final_proj_list.append(final_Proj)
+                        st.write(f'finished run for {players}')
+                
+                    # Concatenate all the final_Proj dataframes
+                    final_Proj_combined = pd.concat(final_proj_list)
+                    final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False)
+                    final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']]
+                    st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True)  # Assign the combined dataframe back to final_Proj
+            placeholder.empty()
+
+        with displayholder.container():
+            if 'final_Proj' in st.session_state:
+                st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(st.session_state.final_Proj),
+                    file_name='NHL_pivot_export.csv',
+                    mime='text/csv',
+                )
+            else:
+                st.write("Run some pivots my dude/dudette")    
+
+with tab2:
+    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+    
+        if proj_file is not None:
+                  try:
+                            proj_dataframe = pd.read_csv(proj_file)
+                  except:
+                            proj_dataframe = pd.read_excel(proj_file)
+    with col2:
+        if proj_file is not None:  
+                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 200

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+pymongo
+pulp
+docker
+plotly
+scipy