Initial commit and dockerization

.streamlit/secrets.toml

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

Dockerfile

@@ -1,3 +1,4 @@
+#making sure to have a good python version
 FROM python:3.13.5-slim
 
 WORKDIR /app
@@ -5,11 +6,24 @@ 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 +1,15 @@
+import streamlit as st
+import pymongo
+import os
+
+@st.cache_resource
+def init_conn():
+        uri = os.getenv('MONGO_URI')
+        if not uri:
+            uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NFL_Database"]
+
+        return db
+    
+db = init_conn()

src/streamlit_app.py

@@ -1,40 +1,445 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+from database import db
+from itertools import combinations
+
+st.set_page_config(layout="wide")
+
+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%}'}
+
+wrong_acro = ['WSH', 'AZ', 'CHW']
+right_acro = ['WAS', 'ARI', 'CWS']
+
+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: #68B1E7;
+        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: #68B1E7;
+        border: 3px solid #4FB286;
+        border-radius: 10px;
+        color: black;
+    }
+    .stElementContainer [kind="segmented_control"]:hover {
+        background-color: #4FB286;
+        cursor: pointer;
+    }
+
+    div[data-baseweb="select"] > div {
+        background-color: #68B1E7;
+        color: white;
+    }
+
+</style>""", unsafe_allow_html=True)
+
+@st.cache_resource(ttl=600)
+def init_baselines():
+
+    collection = db["Player_Baselines"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
+                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
+    player_stats = raw_display[raw_display['Position'] != 'K']
+
+    collection = db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    dk_roo_raw = load_display.dropna(subset=['Median'])
+
+    collection = db["FD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    fd_roo_raw = load_display.dropna(subset=['Median'])
+
+    collection = db["DK_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    dk_stacks_raw = raw_display.copy()
+
+    collection = db["FD_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    fd_stacks_raw = raw_display.copy()
+
+    return player_stats, dk_stacks_raw, fd_stacks_raw, 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_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = init_baselines()
+
+app_load_reset_column, app_view_site_column = st.columns([1, 9])
+with app_load_reset_column:
+    if st.button("Load/Reset Data", key='reset_data_button'):
+        st.cache_data.clear()
+        player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = init_baselines()
+        for key in st.session_state.keys():
+            del st.session_state[key]
+with app_view_site_column:
+    with st.container():
+        app_view_column, app_site_column = st.columns([3, 3])
+        with app_view_column:
+            view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_selectbox')
+        with app_site_column:
+            site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_selectbox')
+
+selected_tab = st.segmented_control(
+    "Select Tab",
+    options=["Stack Finder", "User Upload"],
+    selection_mode='single',
+    default='Stack Finder',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+
+if selected_tab == 'Stack Finder':
+    with st.expander("Info and Filters"):
+        app_info_column, slate_choice_column, filtering_column, stack_info_column = st.columns(4)
+        with app_info_column:
+            if st.button("Load/Reset Data", key='reset1'):
+                st.cache_data.clear()
+                player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = init_baselines()
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            st.info(f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST")
+        with slate_choice_column:
+            slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate', 'User Upload'), key='slate_var1')
+            if slate_var1 == 'User Upload':
+                slate_var1 = st.session_state['proj_dataframe']
+            else:
+                if site_var == 'Draftkings':
+                    raw_baselines = dk_roo_raw
+                    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']
+                    elif slate_var1 == 'Late Slate':
+                        raw_baselines = raw_baselines[raw_baselines['slate'] == 'Late Slate']
+                    elif slate_var1 == 'Thurs-Mon Slate':
+                        raw_baselines = raw_baselines[raw_baselines['slate'] == 'Thurs-Mon Slate']
+                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+                    qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+                elif site_var == 'Fanduel':
+                    raw_baselines = fd_roo_raw
+                    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']
+                    elif slate_var1 == 'Late Slate':
+                        raw_baselines = raw_baselines[raw_baselines['slate'] == 'Late Slate']
+                    elif slate_var1 == 'Thurs-Mon Slate':
+                        raw_baselines = raw_baselines[raw_baselines['slate'] == 'Thurs-Mon Slate']
+                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+                    qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+        with filtering_column:
+            split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('Full Slate Run', 'Specific Teams'), key='split_var2')
+            if split_var2 == 'Specific Teams':
+                team_var2 = st.multiselect('Which teams would you like to include in the analysis?', options = raw_baselines['Team'].unique(), key='team_var2')
+            elif split_var2 == 'Full Slate Run':
+                team_var2 = raw_baselines.Team.unique().tolist()
+            pos_var2 = st.multiselect('What Positions would you like to view?', options = ['WR', 'TE', 'RB'], default = ['WR', 'TE', 'RB'], key='pos_var2')
+        with stack_info_column:
+            if site_var == 'Draftkings':
+                max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 50000, value = 35000, step = 100, key='max_sal2')
+            elif site_var == 'Fanduel':
+                max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 35000, value = 25000, step = 100, key='max_sal2')
+            size_var2 = st.selectbox('What size of stacks are you analyzing?', options = ['QB+1', 'QB+2', 'QB+3'])
+            if size_var2 == 'QB+1':
+                stack_size = 2
+            if size_var2 == 'QB+2':
+                stack_size = 3
+            if size_var2 == 'QB+3':
+                stack_size = 4
+
+            team_dict = dict(zip(raw_baselines.Player, raw_baselines.Team))
+            proj_dict = dict(zip(raw_baselines.Player, raw_baselines.Median))
+            own_dict = dict(zip(raw_baselines.Player, raw_baselines.Own))
+            cost_dict = dict(zip(raw_baselines.Player, raw_baselines.Salary))
+            qb_dict = dict(zip(qb_lookup.Team, qb_lookup.Player))
+    if st.button("Run Stack Analysis", key='run_stack_analysis'):
+        if site_var == 'Draftkings':
+            position_limits = {
+                'QB': 1,
+                'RB': 2,
+                'WR': 3,
+                'TE': 1,
+                'UTIL': 1,
+                'DST': 1,
+            }
+            max_salary = max_sal2
+            max_players = 9
+        else:
+            position_limits = {
+                'QB': 1,
+                'RB': 2,
+                'WR': 3,
+                'TE': 1,
+                'UTIL': 1,
+                'DST': 1,
+            }
+            max_salary = max_sal2
+            max_players = 9
+
+        stack_hold_container = st.empty()
+        comb_list = []
+        raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2 + ['QB']))]
+
+        # Create a position dictionary mapping players to their eligible positions
+        pos_dict = dict(zip(raw_baselines.Player, raw_baselines.Position))
+
+        def is_valid_combination(combo):
+            # Count positions in this combination
+            position_counts = {pos: 0 for pos in position_limits.keys()}
+            
+            # For each player in the combination
+            for player in combo:
+                # Get their eligible positions
+                player_positions = pos_dict[player].split('/')
+
+                for pos in player_positions:
+                    position_counts[pos] += 1
+            
+            # Check if any position exceeds its limit
+            for pos, limit in position_limits.items():
+                if position_counts[pos] > limit:
+                    return False
+            
+            return True
+
+        # Modify the combination generation code
+        comb_list = []
+        for cur_team in team_var2:
+            working_baselines = raw_baselines
+            working_baselines = working_baselines[working_baselines['Team'] == cur_team]
+            working_baselines = working_baselines[working_baselines['Position'] != 'DST']
+            working_baselines = working_baselines[working_baselines['Position'] != 'K']
+            qb_var = qb_dict[cur_team]
+            order_list = working_baselines['Player'].unique()
+
+            comb = combinations(order_list, stack_size)
+
+            for i in list(comb):
+                if qb_var in i:
+                    comb_list.append(i)
+
+            for i in list(comb):
+                if is_valid_combination(i):
+                    comb_list.append(i)
+
+        comb_DF = pd.DataFrame(comb_list)
+
+        print(comb_DF.head(10))
+
+        if stack_size == 2:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict)])
+        elif stack_size == 3:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict)])
+        elif stack_size == 4:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict),
+                    comb_DF[3].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict),
+                    comb_DF[3].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict),
+                    comb_DF[3].map(own_dict)])
+        elif stack_size == 5:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict),
+                    comb_DF[3].map(proj_dict),
+                    comb_DF[4].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict),
+                    comb_DF[3].map(cost_dict),
+                    comb_DF[4].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict),
+                    comb_DF[3].map(own_dict),
+                    comb_DF[4].map(own_dict)])
+
+        comb_DF = comb_DF.sort_values(by='Proj', ascending=False)
+        comb_DF = comb_DF.loc[comb_DF['Salary'] <= max_sal2]
+
+        cut_var = 0
+
+        if stack_size == 2:
+                    while cut_var <= int(len(comb_DF)):
+                        try:
+                            if int(cut_var) == 0:
+                                cur_proj = float(comb_DF.iat[cut_var, 3])
+                                cur_own = float(comb_DF.iat[cut_var, 5])
+                            elif int(cut_var) >= 1:
+                                check_own = float(comb_DF.iat[cut_var, 5])
+                                if check_own > cur_own:
+                                    comb_DF = comb_DF.drop([cut_var])
+                                    cur_own = cur_own
+                                    cut_var = cut_var - 1
+                                    comb_DF = comb_DF.reset_index()
+                                    comb_DF = comb_DF.drop(['index'], axis=1)
+                                elif check_own <= cur_own:
+                                    cur_own = float(comb_DF.iat[cut_var, 5])
+                                    cut_var = cut_var
+                            cut_var += 1
+                        except:
+                            cut_var += 1
+        elif stack_size == 3:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,4])
+                        cur_own = float(comb_DF.iat[cut_var,6])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,6])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,6])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+        elif stack_size == 4:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,5])
+                        cur_own = float(comb_DF.iat[cut_var,7])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,7])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,7])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+        elif stack_size == 5:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,6])
+                        cur_own = float(comb_DF.iat[cut_var,8])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,8])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,8])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+        st.session_state['display_frame'] = comb_DF
+    if 'display_frame' in st.session_state:
+        st.dataframe(st.session_state['display_frame'].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), hide_index=True, use_container_width = True)
+        st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(st.session_state['display_frame']),
+                file_name='NFL_Stack_Options_export.csv',
+                mime='text/csv',
+        )
+    else:
+        st.info("When you run the stack analysis, the results will be displayed here. Open up the 'Info and Filters' tab to check the settings.")
+
+if selected_tab == 'User Upload':
+    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])
 
-"""
-# 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 col1:
+        proj_file = st.file_uploader("Upload Projections", key = 'proj_uploader')
+    
+        if proj_file is not None:
+                  try:
+                            st.session_state['proj_dataframe'] = pd.read_csv(proj_file)
+                  except:
+                            st.session_state['proj_dataframe'] = pd.read_excel(proj_file)
+    with col2:
+        if proj_file is not None:  
+                  st.dataframe(st.session_state['proj_dataframe'].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)