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

@@ -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["NFL_Database"]
+
+        return db
+    
+db = init_conn()

src/streamlit_app.py

@@ -1,40 +1,400 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+import re
+from database import db
+
+st.set_page_config(layout="wide")
+
+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.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'])
+
+    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 = init_baselines()
+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"
+
+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_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=["Pivot Finder", "User Upload"],
+    selection_mode='single',
+    default='Pivot Finder',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+
+if selected_tab == 'Pivot Finder':
+    with st.expander("Infos and Filters"):
+        st.info("Welcome to the Pivot Finder! Select a player or a set of players and run the algorithm to find the best spots to pivot for maximum relative value.")
+        app_info_column, player_select_column, micro_filter_column, macro_filter_column = st.columns(4)
+        with app_info_column:
+            st.info(t_stamp)
+            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()
+                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')
+            if site_var == 'Draftkings':
+                if data_var1 == 'User':
+                    raw_baselines = st.session_state['proj_dataframe']
+                elif data_var1 != 'User':
+                    raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
+                    raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+            elif site_var == 'Fanduel':
+                if data_var1 == 'User':
+                    raw_baselines = st.session_state['proj_dataframe']
+                elif data_var1 != 'User':
+                    raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
+                    raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+        with player_select_column:
+            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)
+        with micro_filter_column:
+            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)
+        with macro_filter_column:
+            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()
+        
+    placeholder = st.empty()
+    displayholder = st.empty()
+
+    if st.button('Simulate appropriate pivots'):
+        with placeholder:
+            if site_var == 'Draftkings':
+                working_roo = raw_baselines
+                working_roo.replace('', 0, inplace=True)
+            if site_var == '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'] * 1.75
+                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
+                flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
+                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
+                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
+                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_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'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', 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'] == 'RB', 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'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].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'] * 1.75
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
+                    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_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'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', 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'] == 'RB', 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'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].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:
+            if view_var == 'Simple':
+                display_table = st.session_state['final_Proj'][['Position', 'Team', 'Salary', 'Median', 'Own', 'LevX']]
+                st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            if view_var == 'Advanced':
+                display_table = st.session_state['final_Proj']
+                st.dataframe(display_table.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='NFL_pivot_export.csv',
+                mime='text/csv',
+            )
+        else:
+            st.write("Run some pivots my dude/dudette")
+
+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 File", 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)