Initial commit for new app structure

.streamlit/secrets.toml

@@ -0,0 +1 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"

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"
+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 +1,16 @@
+import streamlit as st
+import pymongo
+import os
+
+@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()

src/streamlit_app.py

@@ -1,40 +1,318 @@
-import altair as alt
 import numpy as np
 import pandas as pd
 import streamlit as st
+import pymongo
+from database import db
 
-"""
-# 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")
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}',
+                   '6x%': '{:.2%}','GPP%': '{:.2%}'}
+
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+        padding: 4px;
+    }
+
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        background-color: #FFD700;
+        color: white;
+        border-radius: 10px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+
+    .stTabs [aria-selected="true"] {
+        background-color: #DAA520;
+        color: white;
+    }
+
+    .stTabs [data-baseweb="tab"]:hover {
+        background-color: #DAA520;
+        cursor: pointer;
+    }
+</style>""", unsafe_allow_html=True)
+
+@st.cache_resource(ttl = 60)
+def init_stat_load():
+    collection = db["Player_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
+    raw_display = raw_display.rename(columns={'Minutes Proj': 'Minutes'})
+    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own', 'site', 'slate', 'timestamp']]
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    proj_raw = raw_display.sort_values(by='Median', ascending=False)
+
+    timestamp = proj_raw['timestamp'].iloc[0]
+
+    return proj_raw, timestamp
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+proj_raw, timestamp = init_stat_load()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+
+st.header("NBA DFS Pivot Tool")
+with st.expander("Info and Filters"):
+    st.info(t_stamp)
+    if st.button("Load/Reset Data", key='reset1'):
+            st.cache_data.clear()
+            proj_raw, timestamp = init_stat_load()
+            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+    site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+    slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
+    if site_var1 == 'Draftkings':
+        raw_baselines = proj_raw[proj_raw['site'] == 'Draftkings']
+        if slate_var1 == 'Main Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
+        elif slate_var1 == 'Secondary Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate']
+        raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+    elif site_var1 == 'Fanduel':
+        raw_baselines = proj_raw[proj_raw['site'] == 'Fanduel']
+        if slate_var1 == 'Main Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
+        elif slate_var1 == 'Secondary Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary 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 = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
+    elif pos_var1 == 'All Positions':
+        pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C']
+    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()
+        
+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))
+        pos_dict = dict(zip(working_roo.Player, working_roo.Position))
+        min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
+        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'].apply(lambda x: any(pos in x.split('/') for pos in 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', 'Minutes']]
+            flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+            flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+            flex_file['STD'] = (flex_file['Median']/4)
+            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']
+
+            salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+            salary_file = salary_file.div(1000)
+
+            for x in range(0,total_sims):    
+                overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+            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_4x_check = (overall_file - (salary_file*4))
+            salary_5x_check = (overall_file - (salary_file*5))
+            salary_6x_check = (overall_file - (salary_file*6))
+            gpp_check = (overall_file - ((salary_file*5)+10))
+
+            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['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['GPP%'] = salary_4x_check[gpp_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+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+            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+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+            final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+            final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+            final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+            final_Proj['Own'] = final_Proj['Own'].astype('float')
+            final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own']]
+            final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+            final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+            final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+            final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+            final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+            final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+
+            final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+            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'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))]
+            player_check = raw_baselines['Player'].head(top_x_var).tolist()
+            st.write(player_check)
+            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['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', 'Minutes']]
+                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                flex_file['STD'] = (flex_file['Median']/4)
+                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']
+
+                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                salary_file = salary_file.div(1000)
+
+                for x in range(0,total_sims):    
+                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                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_4x_check = (overall_file - (salary_file*4))
+                salary_5x_check = (overall_file - (salary_file*5))
+                salary_6x_check = (overall_file - (salary_file*6))
+                gpp_check = (overall_file - ((salary_file*5)+10))
+
+                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['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['GPP%'] = salary_4x_check[gpp_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+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+                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+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                final_Proj['Own'] = final_Proj['Own'].astype('float')
+                final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+                final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+                final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+                final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+                final_Proj['Pivot_source'] = players
+
+                final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+                
+                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='NBA_pivot_export.csv',
+            mime='text/csv',
+        )
+    else:
+        st.write("Run some pivots my dude/dudette")