Update app.py

app.py

@@ -1,926 +1,401 @@
 import streamlit as st
 st.set_page_config(layout="wide")
-
-for name in dir():
-    if not name.startswith('_'):
-        del globals()[name]
-
 import numpy as np
 import pandas as pd
-import streamlit as st
 import gspread
-import random
-import gc
+import pymongo
+import time
 
 @st.cache_resource
 def init_conn():
-          scope = ['https://www.googleapis.com/auth/spreadsheets',
-                    "https://www.googleapis.com/auth/drive"]
-          
-          credentials = {
-            "type": "service_account",
-            "project_id": "sheets-api-connect-378620",
-            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
-            "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-            "client_id": "106625872877651920064",
-            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-            "token_uri": "https://oauth2.googleapis.com/token",
-            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-            "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-          }
-
-          gc_con = gspread.service_account_from_dict(credentials)
-          
-          return gc_con
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
+          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+        uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["testing_db"]
+     
+        NFL_Data = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit?gid=186883101#gid=186883101'
 
-gcservice_account = init_conn()
+        gc_con = gspread.service_account_from_dict(credentials, scope)
 
-freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
+        return gc_con, db, NFL_Data
+    
+gcservice_account, db, NFL_Data = init_conn()
 
-@st.cache_resource(ttl=601)
-def init_baselines():
-    sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1030253279')
-    worksheet = sh.worksheet('SD_Projections')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw = raw_display.loc[raw_display['Median'] > 0]
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
+dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']
+fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']
 
-    worksheet = sh.worksheet('FD_SD_Projections')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw = raw_display.loc[raw_display['Median'] > 0]
+@st.cache_data(ttl = 599)
+def init_DK_seed_frames():  
+    
+        collection = db["DK_NFL_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']]
+        DK_seed = raw_display.to_numpy()
 
-    worksheet = sh.worksheet('SD_Projections_2')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw_2 = raw_display.loc[raw_display['Median'] > 0]
+        return DK_seed
 
-    worksheet = sh.worksheet('FD_SD_Projections_2')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw_2 = raw_display.loc[raw_display['Median'] > 0]
+@st.cache_data(ttl = 599)
+def init_FD_seed_frames():  
     
-    worksheet = sh.worksheet('SD_Projections_3')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw_3 = raw_display.loc[raw_display['Median'] > 0]
+        collection = db["FD_NFL_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']]
+        FD_seed = raw_display.to_numpy()
 
-    worksheet = sh.worksheet('FD_SD_Projections_3')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw_3 = raw_display.loc[raw_display['Median'] > 0]
+        return FD_seed
 
+@st.cache_data(ttl = 599)
+def init_baselines():
+    sh = gcservice_account.open_by_url(NFL_Data)
+    
     worksheet = sh.worksheet('SD_Projections')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Median'])
-    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    load_display['STDev'] = load_display['PPR'] / 3
+    load_display = load_display.drop_duplicates(subset=['name'], keep='first')
+    load_display.rename(columns={"name": "Player"}, inplace = True)
+    
+    dk_raw = load_display.dropna(subset=['Median'])
     
     worksheet = sh.worksheet('FD_SD_Projections')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"Half_PPR": "Median", "name": "Player"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Median'])
-    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
-
-    return dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dk_ids, fd_ids
-
-dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dkid_dict, fdid_dict = init_baselines()
-
-static_exposure = pd.DataFrame(columns=['Player', 'count'])
-overall_exposure = pd.DataFrame(columns=['Player', 'count'])
-
-def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port):
+    load_display['STDev'] = load_display['Half_PPR'] / 3
+    load_display = load_display.drop_duplicates(subset=['name'], keep='first')
+    load_display.rename(columns={"name": "Player"}, inplace = True)
+    
+    fd_raw = load_display.dropna(subset=['Median'])
+
+    return dk_raw, fd_raw
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def calculate_DK_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :6], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array 
+
+@st.cache_data
+def calculate_FD_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :5], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array
+
+@st.cache_data
+def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
     SimVar = 1
     Sim_Winners = []
-    fp_array = FinalPortfolio.values
-    
-    if insert_port == 1:
-        up_array = CleanPortfolio.values
+    fp_array = seed_frame[:sharp_split, :]
     
     # Pre-vectorize functions
     vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
     vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
     
-    if insert_port == 1:
-        vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
-        vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
-    
     st.write('Simulating contest on frames')
     
     while SimVar <= Sim_size:
-        if insert_port == 1:
-            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
-        elif insert_port == 0:
-            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
             
         sample_arrays1 = np.c_[
             fp_random, 
             np.sum(np.random.normal(
-                loc=vec_projection_map(fp_random[:, :-5]),
-                scale=vec_stdev_map(fp_random[:, :-5])),
+                loc=vec_projection_map(fp_random[:, :-6]),
+                scale=vec_stdev_map(fp_random[:, :-6])),
             axis=1)
         ]
 
-        if insert_port == 1:
-            sample_arrays2 = np.c_[
-                up_array, 
-                np.sum(np.random.normal(
-                    loc=vec_up_projection_map(up_array[:, :-5]),
-                    scale=vec_up_stdev_map(up_array[:, :-5])),
-                axis=1)
-            ]
-            sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
-        else:
-            sample_arrays = sample_arrays1
+        sample_arrays = sample_arrays1
 
-        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+        final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]]
         best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
         Sim_Winners.append(best_lineup)
         SimVar += 1
         
     return Sim_Winners
 
-def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth):
-    RunsVar = 1
-    seed_depth_def = seed_depth1
-    Strength_var_def = Strength_var
-    strength_grow_def = strength_grow
-    Teams_used_def = Teams_used
-    Total_Runs_def = Total_Runs
-    
-    st.write('Creating Seed Frames')
-    
-    while RunsVar <= seed_depth_def:
-        if RunsVar <= 3:
-            FieldStrength = Strength_var_def
-            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
-            maps_dict.update(maps_dict2)
-        elif RunsVar > 3 and RunsVar <= 4:
-            FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
-            FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
-            FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
-        elif RunsVar > 4:
-            FieldStrength = 1
-            FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
-            FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
-            FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict5)
-            maps_dict.update(maps_dict6)
-        RunsVar += 1
-    
-    return FinalPortfolio_export, maps_dict
-
-def create_overall_dfs(pos_players, table_name, dict_name, pos):
-    pos_players = pos_players.sort_values(by='Value', ascending=False)
-    table_name_raw = pos_players.reset_index(drop=True)
-    overall_table_name = table_name_raw.head(round(len(table_name_raw)))
-    overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
-    overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
-    
-    return overall_table_name, overall_dict_name
+DK_seed = init_DK_seed_frames()
+FD_seed = init_FD_seed_frames()
+dk_raw, fd_raw = init_baselines()
 
-
-def get_overall_merged_df():
-    ref_dict = {
-        'pos':['FLEX'],
-        'pos_dfs':['FLEX_Table'],
-        'pos_dicts':['flex_dict']
-        }
-    
-    for i in range(0,1):
-        ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
-            create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
-        
-    df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
-    
-    return df_out, ref_dict
-
-def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
-    
-            O_merge, full_pos_player_dict = get_overall_merged_df()
-            Overall_Merge = O_merge[['Var', 'Player', 'Team', 'Salary', 'Median', 'Own']].copy()
+tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              DK_seed = init_DK_seed_frames()
+              FD_seed = init_FD_seed_frames()
+              dk_raw, fd_raw = init_baselines()
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Other Showdown'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        if site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+            column_names = dk_columns
             
-            # Calculate Floor, Ceiling, and STDev directly
-            Overall_Merge['Floor'] = Overall_Merge['Median'] * .25
-            Overall_Merge['Ceiling'] = Overall_Merge['Median'] + Overall_Merge['Floor']
-            Overall_Merge['STDev'] = Overall_Merge['Median'] / 4
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = dk_raw.Team.values.tolist()
             
-            # Calculate the flex range and generate unique range list
-            flex_range_var = len(Overall_Merge)
-            ranges_dict = {'flex_range': flex_range_var}
-            ranges_dict['flex_Uniques'] = list(range(0, flex_range_var))
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+                    
+        elif site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+            column_names = fd_columns
             
-            # Generate random portfolios
-            rng = np.random.default_rng()
-            all_choices = rng.choice(flex_range_var, size=(Total_Sample_Size, 6))
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = fd_raw.Team.values.tolist()
             
-            # Create RandomPortfolio DataFrame
-            RandomPortfolio = pd.DataFrame(all_choices, columns=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
-            RandomPortfolio['User/Field'] = 0
-          
-            return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
-
-def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
-    
-    sizesplit = round(Total_Sample_Size * sharp_split)
-    
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
-    
-    RandomPortfolio['CPT'] = pd.Series(list(RandomPortfolio['CPT'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX1'] = pd.Series(list(RandomPortfolio['FLEX1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX2'] = pd.Series(list(RandomPortfolio['FLEX2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX3'] = pd.Series(list(RandomPortfolio['FLEX3'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX4'] = pd.Series(list(RandomPortfolio['FLEX4'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX5'] = pd.Series(list(RandomPortfolio['FLEX5'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
-    RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
-    RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 7].drop(columns=['plyr_list','plyr_count']).\
-        reset_index(drop=True)
-        
-    RandomPortfolio['CPTs'] = RandomPortfolio['CPT'].map(maps_dict['Salary_map']).astype(np.int32) * 1.5
-    RandomPortfolio['FLEX1s'] = RandomPortfolio['FLEX1'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX2s'] = RandomPortfolio['FLEX2'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX3s'] = RandomPortfolio['FLEX3'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX4s'] = RandomPortfolio['FLEX4'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX5s'] = RandomPortfolio['FLEX5'].map(maps_dict['Salary_map']).astype(np.int32)
-    
-    RandomPortfolio['CPTp'] = RandomPortfolio['CPT'].map(maps_dict['Projection_map']).astype(np.float16) * 1.5
-    RandomPortfolio['FLEX1p'] = RandomPortfolio['FLEX1'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX2p'] = RandomPortfolio['FLEX2'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX3p'] = RandomPortfolio['FLEX3'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX4p'] = RandomPortfolio['FLEX4'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX5p'] = RandomPortfolio['FLEX5'].map(maps_dict['Projection_map']).astype(np.float16)
-    
-    RandomPortfolio['CPTo'] = RandomPortfolio['CPT'].map(maps_dict['Own_map']).astype(np.float16) / 4
-    RandomPortfolio['FLEX1o'] = RandomPortfolio['FLEX1'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX2o'] = RandomPortfolio['FLEX2'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX3o'] = RandomPortfolio['FLEX3'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX4o'] = RandomPortfolio['FLEX4'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX5o'] = RandomPortfolio['FLEX5'].map(maps_dict['Own_map']).astype(np.float16)
-    
-    portHeaderList = RandomPortfolio.columns.values.tolist()
-    portHeaderList.append('Salary')
-    portHeaderList.append('Projection')
-    portHeaderList.append('Own')
-    
-    RandomPortArray = RandomPortfolio.to_numpy()
-    
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,7:13].astype(int))]
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,13:19].astype(np.double))]
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:25].astype(np.double))]
-    
-    RandomPortArrayOut = np.delete(RandomPortArray, np.s_[7:25], axis=1)
-    RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own'])
-    RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
-          
-    if insert_port == 1:
-        CleanPortfolio['Salary'] = sum([CleanPortfolio['CPT'].map(up_dict['Salary_map']) * 1.5,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Salary_map'])
-                                        ]).astype(np.int16)
-    if insert_port == 1:
-        CleanPortfolio['Projection'] = sum([CleanPortfolio['CPT'].map(up_dict['Projection_map']) * 1.5,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Projection_map'])
-                                        ]).astype(np.float16)
-    if insert_port == 1:
-        CleanPortfolio['Own'] = sum([CleanPortfolio['CPT'].map(up_dict['Own_map']) / 4,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Own_map'])
-                                        ]).astype(np.float16)
-    
-    if site_var1 == 'Draftkings':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 49500 - (FieldStrength * 1000)].reset_index(drop=True)
-    elif site_var1 == 'Fanduel':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (FieldStrength * 1000)].reset_index(drop=True)
-    
-    RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
-    
-    RandomPortfolio = RandomPortfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own']]
-    
-    return RandomPortfolio, maps_dict
-
-def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
-    
-    sizesplit = round(Total_Sample_Size * (1-sharp_split))
-    
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
-    
-    RandomPortfolio['CPT'] = pd.Series(list(RandomPortfolio['CPT'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX1'] = pd.Series(list(RandomPortfolio['FLEX1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX2'] = pd.Series(list(RandomPortfolio['FLEX2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX3'] = pd.Series(list(RandomPortfolio['FLEX3'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX4'] = pd.Series(list(RandomPortfolio['FLEX4'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['FLEX5'] = pd.Series(list(RandomPortfolio['FLEX5'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
-    RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
-    RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
-    RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 7].drop(columns=['plyr_list','plyr_count']).\
-        reset_index(drop=True)
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [4, 3, 2, 1, 0]
         
-    RandomPortfolio['CPTs'] = RandomPortfolio['CPT'].map(maps_dict['Salary_map']).astype(np.int32) * 1.5
-    RandomPortfolio['FLEX1s'] = RandomPortfolio['FLEX1'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX2s'] = RandomPortfolio['FLEX2'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX3s'] = RandomPortfolio['FLEX3'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX4s'] = RandomPortfolio['FLEX4'].map(maps_dict['Salary_map']).astype(np.int32)
-    RandomPortfolio['FLEX5s'] = RandomPortfolio['FLEX5'].map(maps_dict['Salary_map']).astype(np.int32)
-    
-    RandomPortfolio['CPTp'] = RandomPortfolio['CPT'].map(maps_dict['Projection_map']).astype(np.float16) * 1.5
-    RandomPortfolio['FLEX1p'] = RandomPortfolio['FLEX1'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX2p'] = RandomPortfolio['FLEX2'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX3p'] = RandomPortfolio['FLEX3'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX4p'] = RandomPortfolio['FLEX4'].map(maps_dict['Projection_map']).astype(np.float16)
-    RandomPortfolio['FLEX5p'] = RandomPortfolio['FLEX5'].map(maps_dict['Projection_map']).astype(np.float16)
-    
-    RandomPortfolio['CPTo'] = RandomPortfolio['CPT'].map(maps_dict['Own_map']).astype(np.float16) / 4
-    RandomPortfolio['FLEX1o'] = RandomPortfolio['FLEX1'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX2o'] = RandomPortfolio['FLEX2'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX3o'] = RandomPortfolio['FLEX3'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX4o'] = RandomPortfolio['FLEX4'].map(maps_dict['Own_map']).astype(np.float16)
-    RandomPortfolio['FLEX5o'] = RandomPortfolio['FLEX5'].map(maps_dict['Own_map']).astype(np.float16)
-    
-    portHeaderList = RandomPortfolio.columns.values.tolist()
-    portHeaderList.append('Salary')
-    portHeaderList.append('Projection')
-    portHeaderList.append('Own')
-    
-    RandomPortArray = RandomPortfolio.to_numpy()
-    
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,7:13].astype(int))]
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,13:19].astype(np.double))]
-    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:25].astype(np.double))]
-    
-    RandomPortArrayOut = np.delete(RandomPortArray, np.s_[7:25], axis=1)
-    RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own'])
-    RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
-          
-    if insert_port == 1:
-        CleanPortfolio['Salary'] = sum([CleanPortfolio['CPT'].map(up_dict['Salary_map']) * 1.5,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Salary_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Salary_map'])
-                                        ]).astype(np.int16)
-    if insert_port == 1:
-        CleanPortfolio['Projection'] = sum([CleanPortfolio['CPT'].map(up_dict['Projection_map']) * 1.5,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Projection_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Projection_map'])
-                                        ]).astype(np.float16)
-    if insert_port == 1:
-        CleanPortfolio['Own'] = sum([CleanPortfolio['CPT'].map(up_dict['Own_map']) / 4,
-                                        CleanPortfolio['FLEX1'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX2'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX3'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX4'].map(up_dict['Own_map']),
-                                        CleanPortfolio['FLEX5'].map(up_dict['Own_map'])
-                                        ]).astype(np.float16)
-    
-    if site_var1 == 'Draftkings':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 49500 - (FieldStrength * 1000)].reset_index(drop=True)
-    elif site_var1 == 'Fanduel':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (FieldStrength * 1000)].reset_index(drop=True)
-    
-    RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
-    
-    RandomPortfolio = RandomPortfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own']]
-    
-    return RandomPortfolio, maps_dict
-
-tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
 
+        if st.button("Prepare data export", key='data_export'):
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='NFL_SD_optimals_export.csv',
+                    mime='text/csv',
+                )
+            
+    with col2:
+        if st.button("Load Data", key='load_data'):
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    st.session_state.working_seed = DK_seed.copy()
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    st.session_state.working_seed = FD_seed.copy()
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+        with st.container():
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+            
 with tab1:
-    with st.container():
-          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'. Upload your projections first to avoid an error message.")
-          col1, col2 = st.columns([3, 3])
-
-          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)
-                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
-                              except:
-                                        proj_dataframe = pd.read_excel(proj_file)
-                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
-
-                              player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
-                              player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
-                              player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
-                              player_team_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Team))
-                              
-          with col2:
-                    portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
-
-                    if portfolio_file is not None:
-                              try:
-                                        portfolio_dataframe = pd.read_csv(portfolio_file)
-                              except:
-                                        portfolio_dataframe = pd.read_excel(portfolio_file)
-                              try:
-                                  try:
-                                      portfolio_dataframe.columns=["CPT", "FLEX1", "FLEX2", "FLEX3", "FLEX4", "FLEX5"]
-                                      split_portfolio = portfolio_dataframe
-                                      split_portfolio[['CPT', 'CPT_ID']] = split_portfolio.CPT.str.split("(", n=1, expand = True)
-                                      split_portfolio[['FLEX1', 'FLEX1_ID']] = split_portfolio.FLEX1.str.split("(", n=1, expand = True)
-                                      split_portfolio[['FLEX2', 'FLEX2_ID']] = split_portfolio.FLEX2.str.split("(", n=1, expand = True)
-                                      split_portfolio[['FLEX3', 'FLEX3_ID']] = split_portfolio.FLEX3.str.split("(", n=1, expand = True)
-                                      split_portfolio[['FLEX4', 'FLEX4_ID']] = split_portfolio.FLEX4.str.split("(", n=1, expand = True)
-                                      split_portfolio[['FLEX5', 'FLEX5_ID']] = split_portfolio.FLEX5.str.split("(", n=1, expand = True)
-        
-                                      split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
-                                      split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                                      split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                                      split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                                      split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                                      split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
-        
-                                      CPT_dict = dict(zip(split_portfolio.CPT, split_portfolio.CPT_ID))
-                                      FLEX1_dict = dict(zip(split_portfolio.FLEX1, split_portfolio.FLEX1_ID))
-                                      FLEX2_dict = dict(zip(split_portfolio.FLEX2, split_portfolio.FLEX2_ID))
-                                      FLEX3_dict = dict(zip(split_portfolio.FLEX3, split_portfolio.FLEX3_ID))
-                                      FLEX4_dict = dict(zip(split_portfolio.FLEX4, split_portfolio.FLEX4_ID))
-                                      FLEX5_dict = dict(zip(split_portfolio.FLEX5, split_portfolio.FLEX5_ID))
-        
-                                      split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict) * 1.5,
-                                                split_portfolio['FLEX1'].map(player_salary_dict),
-                                                split_portfolio['FLEX2'].map(player_salary_dict),
-                                                split_portfolio['FLEX3'].map(player_salary_dict),
-                                                split_portfolio['FLEX4'].map(player_salary_dict),
-                                                split_portfolio['FLEX5'].map(player_salary_dict)])
-                                      
-                                      split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
-                                                split_portfolio['FLEX1'].map(player_proj_dict),
-                                                split_portfolio['FLEX2'].map(player_proj_dict),
-                                                split_portfolio['FLEX3'].map(player_proj_dict),
-                                                split_portfolio['FLEX4'].map(player_proj_dict),
-                                                split_portfolio['FLEX5'].map(player_proj_dict)])
-                                      
-                                      split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
-                                                split_portfolio['FLEX1'].map(player_own_dict),
-                                                split_portfolio['FLEX2'].map(player_own_dict),
-                                                split_portfolio['FLEX3'].map(player_own_dict),
-                                                split_portfolio['FLEX4'].map(player_own_dict),
-                                                split_portfolio['FLEX5'].map(player_own_dict)])
-                                      
-                                  except:
-                                      portfolio_dataframe.columns=["CPT", "FLEX1", "FLEX2", "FLEX3", "FLEX4", "FLEX5"]
-                                      split_portfolio = portfolio_dataframe
-                                      split_portfolio[['CPT_ID', 'CPT']] = split_portfolio.CPT.str.split(":", n=1, expand = True)
-                                      split_portfolio[['FLEX1_ID', 'FLEX1']] = split_portfolio.FLEX1.str.split(":", n=1, expand = True)
-                                      split_portfolio[['FLEX2_ID', 'FLEX2']] = split_portfolio.FLEX2.str.split(":", n=1, expand = True)
-                                      split_portfolio[['FLEX3_ID', 'FLEX3']] = split_portfolio.FLEX3.str.split(":", n=1, expand = True)
-                                      split_portfolio[['FLEX4_ID', 'FLEX4']] = split_portfolio.FLEX4.str.split(":", n=1, expand = True)
-                                      split_portfolio[['FLEX5_ID', 'FLEX5']] = split_portfolio.FLEX5.str.split(":", n=1, expand = True)
-        
-                                      split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
-                                      split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                                      split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                                      split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                                      split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                                      split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
-        
-                                      CPT_dict = dict(zip(split_portfolio.CPT, split_portfolio.CPT_ID))
-                                      FLEX1_dict = dict(zip(split_portfolio.FLEX1, split_portfolio.FLEX1_ID))
-                                      FLEX2_dict = dict(zip(split_portfolio.FLEX2, split_portfolio.FLEX2_ID))
-                                      FLEX3_dict = dict(zip(split_portfolio.FLEX3, split_portfolio.FLEX3_ID))
-                                      FLEX4_dict = dict(zip(split_portfolio.FLEX4, split_portfolio.FLEX4_ID))
-                                      FLEX5_dict = dict(zip(split_portfolio.FLEX5, split_portfolio.FLEX5_ID))
-        
-                                      split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict),
-                                                split_portfolio['FLEX1'].map(player_salary_dict),
-                                                split_portfolio['FLEX2'].map(player_salary_dict),
-                                                split_portfolio['FLEX3'].map(player_salary_dict),
-                                                split_portfolio['FLEX4'].map(player_salary_dict),
-                                                split_portfolio['FLEX5'].map(player_salary_dict)])
-                                      
-                                      split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
-                                                split_portfolio['FLEX1'].map(player_proj_dict),
-                                                split_portfolio['FLEX2'].map(player_proj_dict),
-                                                split_portfolio['FLEX3'].map(player_proj_dict),
-                                                split_portfolio['FLEX4'].map(player_proj_dict),
-                                                split_portfolio['FLEX5'].map(player_proj_dict)])
-                                      
-                                      split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
-                                                split_portfolio['FLEX1'].map(player_own_dict),
-                                                split_portfolio['FLEX2'].map(player_own_dict),
-                                                split_portfolio['FLEX3'].map(player_own_dict),
-                                                split_portfolio['FLEX4'].map(player_own_dict),
-                                                split_portfolio['FLEX5'].map(player_own_dict)])
-                                      
-                              except:
-                                  split_portfolio = portfolio_dataframe
-                                  
-                                  split_portfolio['CPT'] = split_portfolio['CPT'].str[:-6]
-                                  split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str[:-6]
-                                  split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str[:-6]
-                                  split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str[:-6]
-                                  split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str[:-6]
-                                  split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str[:-6]
-                                  
-                                  split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
-                                  split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                                  split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                                  split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                                  split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                                  split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
-                                  
-                                  split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict) * 1.5,
-                                            split_portfolio['FLEX1'].map(player_salary_dict),
-                                            split_portfolio['FLEX2'].map(player_salary_dict),
-                                            split_portfolio['FLEX3'].map(player_salary_dict),
-                                            split_portfolio['FLEX4'].map(player_salary_dict),
-                                            split_portfolio['FLEX5'].map(player_salary_dict)])
-                                  
-                                  split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
-                                            split_portfolio['FLEX1'].map(player_proj_dict),
-                                            split_portfolio['FLEX2'].map(player_proj_dict),
-                                            split_portfolio['FLEX3'].map(player_proj_dict),
-                                            split_portfolio['FLEX4'].map(player_proj_dict),
-                                            split_portfolio['FLEX5'].map(player_proj_dict)])
-                                  
-                                  split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
-                                            split_portfolio['FLEX1'].map(player_own_dict),
-                                            split_portfolio['FLEX2'].map(player_own_dict),
-                                            split_portfolio['FLEX3'].map(player_own_dict),
-                                            split_portfolio['FLEX4'].map(player_own_dict),
-                                            split_portfolio['FLEX5'].map(player_own_dict)])
-                                  
-                              
-                              gc.collect()
-                              
-with tab2:
     col1, col2 = st.columns([1, 7])
     with col1:
-        if st.button("Load/Reset Data", key='reset1'):
+        if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dkid_dict, fdid_dict = init_baselines()
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Third)', 'User'))
-        site_var1 = 'Draftkings'
-        if site_var1 == 'Draftkings':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
-              elif slate_var1 == 'Paydirt (Main)':
-                  raw_baselines = dk_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  raw_baselines = dk_roo_raw_2
-              elif slate_var1 == 'Paydirt (Third)':
-                  raw_baselines = dk_roo_raw_3
-        elif site_var1 == 'Fanduel':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif slate_var1 == 'Paydirt (Main)':
-                  raw_baselines = dk_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  raw_baselines = dk_roo_raw_2
-              elif slate_var1 == 'Paydirt (Third)':
-                  raw_baselines = dk_roo_raw_3
-                  
-        st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
-        insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'))
-        if insert_port1 == 'Yes':
-            insert_port = 1
-        elif insert_port1 == 'No':
-            insert_port = 0
-        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
+              DK_seed = init_DK_seed_frames()
+              FD_seed = init_FD_seed_frames()
+              dk_raw, fd_raw = init_baselines()
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+        if sim_site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+            column_names = dk_columns
+        elif sim_site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+            column_names = fd_columns
+            
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
-            Contest_Size = 500
+            Contest_Size = 1000
         elif contest_var1 == 'Medium':
-            Contest_Size = 2500
-        elif contest_var1 == 'Large':
             Contest_Size = 5000
-        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
+        elif contest_var1 == 'Custom':
+            Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
         if strength_var1 == 'Not Very':
-            sharp_split = .33
-            Strength_var = .50
-            scaling_var = 5
+            sharp_split = 500000
+        elif strength_var1 == 'Below Average':
+            sharp_split = 400000
         elif strength_var1 == 'Average':
-            sharp_split = .50
-            Strength_var = .25
-            scaling_var = 10
+            sharp_split = 300000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 200000
         elif strength_var1 == 'Very':
-            sharp_split = .75
-            Strength_var = .01
-            scaling_var = 15
-        
-        Sort_function = 'Median'
-        Sim_function = 'Projection'
-        
-        if Contest_Size <= 1000:
-            strength_grow = .01
-        elif Contest_Size > 1000 and Contest_Size <= 2500:
-            strength_grow = .025
-        elif Contest_Size > 2500 and Contest_Size <= 5000:
-            strength_grow = .05
-        elif Contest_Size > 5000 and Contest_Size <= 20000:
-            strength_grow = .075
-        elif Contest_Size > 20000:
-            strength_grow = .1
-            
-        field_growth = 100 * strength_grow
-            
-    with col2:
-        with st.container():
-            if st.button("Simulate Contest", key='sim1'):
-                with st.container():
-                    for key in st.session_state.keys():
-                        del st.session_state[key]
-                    
-                    if slate_var1 == 'User':
-                        initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                        
-                        # Define the calculation to be applied
-                        def calculate_own(position, own, mean_own, factor, max_own=75):
-                            return np.where((position == 'QB') & (own - mean_own >= 0),
-                                            own * (factor * (own - mean_own) / 100) + mean_own,
-                                            own)
-                        
-                        # Set the factors based on the contest_var1
-                        factor_qb, factor_other = {
-                            'Small': (8, 10),
-                            'Medium': (5, 5),
-                            'Large': (1.5, 1.5),
-                        }[contest_var1]
-                        
-                        # Apply the calculation to the DataFrame
-                        initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1)
-                        initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75)
-                        initial_proj['Own'] = initial_proj['Own%'] * (600 / initial_proj['Own%'].sum())
-                        
-                        # Drop unnecessary columns and create the final DataFrame
-                        Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                        
-                    elif slate_var1 != 'User':
-                        # Copy only the necessary columns
-                        initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                        
-                        # Define the calculation to be applied
-                        def calculate_own(position, own, mean_own, factor, max_own=75):
-                            return np.where((position == 'QB') & (own - mean_own >= 0),
-                                            own * (factor * (own - mean_own) / 100) + mean_own,
-                                            own)
-                        
-                        # Set the factors based on the contest_var1
-                        factor_qb, factor_other = {
-                            'Small': (8, 10),
-                            'Medium': (5, 5),
-                            'Large': (1.5, 1.5),
-                        }[contest_var1]
-                        
-                        # Apply the calculation to the DataFrame
-                        initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1)
-                        initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75)
-                        initial_proj['Own'] = initial_proj['Own%'] * (600 / initial_proj['Own%'].sum())
-                        
-                        # Drop unnecessary columns and create the final DataFrame
-                        Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    if insert_port == 1:
-                        UserPortfolio = portfolio_dataframe[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
-                    elif insert_port == 0:
-                        UserPortfolio = pd.DataFrame(columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
-            
-                    Overall_Proj.replace('', np.nan, inplace=True)
-                    Overall_Proj = Overall_Proj.dropna(subset=['Median'])
-                    Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
-                    Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
-                    Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
-                    Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
-                    Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
-            
-                    Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
-                    Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
-                    Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
-            
-                    Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
-                    Teams_used = Teams_used.reset_index()
-                    Teams_used['team_item'] = Teams_used['index'] + 1
-                    Teams_used = Teams_used.drop(columns=['index'])
-                    Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
-            
-                    team_list = Teams_used['Team'].to_list()
-                    item_list = Teams_used['team_item'].to_list()
-            
-                    FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
-                    FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
-                    
-                    if FieldStrength < 0:
-                        FieldStrength = Strength_var
-                    field_split = Strength_var
-            
-                    for checkVar in range(len(team_list)):
-                                        Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
-                    
-                    flex_raw = Overall_Proj
-                    flex_raw.dropna(subset=['Median']).reset_index(drop=True)
-                    flex_raw = flex_raw.reset_index(drop=True)
-                    flex_raw = flex_raw.sort_values(by='Own', ascending=False)
-            
-                    pos_players = flex_raw
-                    pos_players.dropna(subset=['Median']).reset_index(drop=True)
-                    pos_players = pos_players.reset_index(drop=True)
-            
-                    if insert_port == 1:
-                        try:
-                            # Initialize an empty DataFrame to store raw portfolio data
-                            Raw_Portfolio = pd.DataFrame()
-                            
-                            # Split each portfolio column and concatenate to Raw_Portfolio
-                            columns_to_process = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                            for col in columns_to_process:
-                                temp_df = UserPortfolio[col].str.split("(", n=1, expand=True)
-                                temp_df.columns = [col, 'Drop']
-                                Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
-                            
-                            # Keep only required variables and remove whitespace
-                            keep_vars = columns_to_process
-                            CleanPortfolio = Raw_Portfolio[keep_vars]
-                            CleanPortfolio = CleanPortfolio.apply(lambda x: x.str.strip())
-                            
-                            # Reset index and clean up the DataFrame
-                            CleanPortfolio.reset_index(inplace=True)
-                            CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
-                            CleanPortfolio.drop(columns=['index'], inplace=True)
-                            CleanPortfolio.replace('', np.nan, inplace=True)
-                            CleanPortfolio.dropna(subset=['CPT'], inplace=True)
-                            
-                            # Create cleaport_players DataFrame
-                            unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
-                            cleaport_players = pd.DataFrame(np.column_stack([unique_vals, counts]), columns=['Player', 'Freq']).astype({'Freq': int}).sort_values('Freq', ascending=False).reset_index(drop=True)
-                            
-                            # Merge and update nerf_frame DataFrame
-                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                            nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= .9
-                            
-                            del Raw_Portfolio
-                        except:
-                            # Reset index and perform column-wise operations
-                            CleanPortfolio = UserPortfolio.reset_index(drop=True)
-                            CleanPortfolio['User/Field'] = CleanPortfolio.index + 1
-                            CleanPortfolio.replace('', np.nan, inplace=True)
-                            CleanPortfolio.dropna(subset=['CPT'], inplace=True)
-                            
-                            # Create cleaport_players DataFrame
-                            unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
-                            cleaport_players = pd.DataFrame({'Player': unique_vals, 'Freq': counts}).sort_values('Freq', ascending=False).reset_index(drop=True).astype({'Freq': int})
-                            
-                            # Merge and update nerf_frame DataFrame
-                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                            nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= .9
-                            
-                            st.table(nerf_frame)
+            sharp_split = 100000
+
     
-                    elif insert_port == 0:
-                        CleanPortfolio = UserPortfolio
-                        cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:6].values, return_counts=True)),
-                                                   columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
-                        nerf_frame = Overall_Proj
-                        
-                    ref_dict = {
-                        'pos':['FLEX'],
-                        'pos_dfs':['FLEX_Table'],
-                        'pos_dicts':['flex_dict']
-                        }
-            
-                    maps_dict = {
-                        'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
-                        'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
-                        'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
-                        'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
-                        'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
-                        'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
-                        'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
-                        'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
-                        'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
+    with col2:
+        if st.button("Run Contest Sim"):
+            if 'working_seed' in st.session_state:
+                maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                    
-                    up_dict = {
-                        'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
-                        'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
-                        'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
-                        'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
-                        'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
-                        'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
-                        'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
-                        'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
-                        'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                
+                #st.table(Sim_Winner_Frame)
+                            
+                # Initial setup
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    st.session_state.working_seed = DK_seed.copy()
+                elif sim_site_var1 == 'Fanduel':
+                    st.session_state.working_seed = FD_seed.copy()
+                maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                    
-                    FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
-                    
-                    Sim_Winners = sim_contest(5000, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
-                    
-                    # Initial setup
-                    Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
-                    Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
-                    Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
-                    Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-                    
-                    # Type Casting
-                    type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
-                    Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                    
-                    del FinalPortfolio, insert_port, type_cast_dict
-                    
-                    # Sorting
-                    st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-                    st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-                    
-                    # Data Copying
-                    st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-                    
-                    # Data Copying
-                    st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                    
-                    del Sim_Winner_Frame, Sim_Winners
-                    
-                    st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:6].values, return_counts=True)),
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                
+                #st.table(Sim_Winner_Frame)
+                            
+                # Initial setup
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+               # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                freq_copy = st.session_state.Sim_Winner_Display
+                
+            if sim_site_var1 == 'Draftkings':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:7].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                    st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-                    st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
-                    st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map'])
-                    st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(5000)
-                    st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own']
-                    st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map'])
-                    for checkVar in range(len(team_list)):
-                                        st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list)
-                    
-                    st.session_state.cpt_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)),
+            elif sim_site_var1 == 'Fanduel':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                    st.session_state.cpt_freq['Freq'] = st.session_state.cpt_freq['Freq'].astype(int)
-                    st.session_state.cpt_freq['Position'] = st.session_state.cpt_freq['Player'].map(maps_dict['Pos_map'])
-                    st.session_state.cpt_freq['Salary'] = st.session_state.cpt_freq['Player'].map(maps_dict['Salary_map'])
-                    st.session_state.cpt_freq['Proj Own'] = (st.session_state.cpt_freq['Player'].map(maps_dict['Own_map']) / 4) / 100
-                    st.session_state.cpt_freq['Exposure'] = st.session_state.cpt_freq['Freq']/5000
-                    st.session_state.cpt_freq['Edge'] = st.session_state.cpt_freq['Exposure'] - st.session_state.cpt_freq['Proj Own']
-                    st.session_state.cpt_freq['Team'] = st.session_state.cpt_freq['Player'].map(maps_dict['Team_map'])
-                    for checkVar in range(len(team_list)):
-                                        st.session_state.cpt_freq['Team'] = st.session_state.cpt_freq['Team'].replace(item_list, team_list)
-                    
-                    st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
+            freq_working['Freq'] = freq_working['Freq'].astype(int)
+            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
+            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
+            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
+            freq_working['Exposure'] = freq_working['Freq']/(1000)
+            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.player_freq = freq_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                    st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int)
-                    st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map'])
-                    st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map'])
-                    st.session_state.flex_freq['Proj Own'] = (st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100) - ((st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 4) / 100)
-                    st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/5000
-                    st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own']
-                    st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map'])
-                    for checkVar in range(len(team_list)):
-                                        st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list)
-                
+            elif sim_site_var1 == 'Fanduel':
+                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / 100
+            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.sp_freq = cpt_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            team_working['Freq'] = team_working['Freq'].astype(int)
+            team_working['Exposure'] = team_working['Freq']/(1000)
+            st.session_state.team_freq = team_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,10:11].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            stack_working['Freq'] = stack_working['Freq'].astype(int)
+            stack_working['Exposure'] = stack_working['Freq']/(1000)
+            st.session_state.stack_freq = stack_working.copy()
+            
         with st.container():
+            if st.button("Reset Sim", key='reset_sim'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
             if 'player_freq' in st.session_state: 
                 player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
                 if player_split_var2 == 'Specific Players':
@@ -929,53 +404,62 @@ with tab2:
                           find_var2 = st.session_state.player_freq.Player.values.tolist()
     
                 if player_split_var2 == 'Specific Players':
-                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
                 if player_split_var2 == 'Full Players':
                           st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
             if 'Sim_Winner_Display' in st.session_state:
-                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
             if 'Sim_Winner_Export' in st.session_state:
-               st.download_button(
-                   label="Export Full Frame",
-                   data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                   file_name='NFL_consim_export.csv',
-                   mime='text/csv',
-               )
-        
+                st.download_button(
+                    label="Export Full Frame",
+                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                    file_name='MLB_consim_export.csv',
+                    mime='text/csv',
+                )  
+                
         with st.container():
-                tab1, tab2, tab3 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures'])
-                with tab1:
-                    if 'player_freq' in st.session_state:
-                        st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                        st.download_button(
-                            label="Export Exposures",
-                            data=st.session_state.player_freq.to_csv().encode('utf-8'),
-                            file_name='player_freq_export.csv',
-                            mime='text/csv',
-                        )
-                with tab2:
-                    if 'player_freq' in st.session_state:
-                        st.dataframe(st.session_state.cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                        st.download_button(
-                            label="Export Exposures",
-                            data=st.session_state.cpt_freq.to_csv().encode('utf-8'),
-                            file_name='cpt_freq_export.csv',
-                            mime='text/csv',
-                        )
-                with tab3:
-                    if 'player_freq' in st.session_state:
-                        st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                        st.download_button(
-                            label="Export Exposures",
-                            data=st.session_state.flex_freq.to_csv().encode('utf-8'),
-                            file_name='flex_freq_export.csv',
-                            mime='text/csv',
-                        )
-del gcservice_account
-del dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2
-del static_exposure, overall_exposure
-del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth
-del raw_baselines
-del freq_format
-
-gc.collect()
+            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'SP Exposures', 'Team Exposures', 'Stack Size Exposures'])
+            with tab1:
+                if 'player_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                        key='overall'
+                    )
+            with tab2:
+                if 'sp_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.sp_freq.to_csv().encode('utf-8'),
+                        file_name='cpt_freq.csv',
+                        mime='text/csv',
+                        key='sp'
+                    )
+            with tab3:
+                if 'team_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )
+            with tab4:
+                if 'stack_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.stack_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.stack_freq.to_csv().encode('utf-8'),
+                        file_name='stack_freq.csv',
+                        mime='text/csv',
+                        key='stack'
+                    )