Create app.py

app.py

@@ -0,0 +1,267 @@
+import pulp
+import numpy as np
+import pandas as pd
+import random
+import sys
+import openpyxl
+import re
+import time
+import streamlit as st
+import matplotlib
+from  matplotlib.colors import LinearSegmentedColormap
+import json
+import requests
+import gspread
+import plotly.figure_factory as ff
+
+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-----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\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 = gspread.service_account_from_dict(credentials)
+
+st.set_page_config(layout="wide")
+
+dk_player_url = 'PGA_Basic_ROO'
+CSV_URL = 'https://sheetdb.io/api/v1/ckjq8yp37qxly?sheet=DK_CSV'
+
+@st.cache_data
+def load_dk_player_model(URL):
+    sh = gc.open(URL)
+    worksheet = sh.get_worksheet(0)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float)
+    raw_display['Top_finish'] = raw_display['Top_finish'].str.replace('%', '').astype(float)/100
+    raw_display['Top_5_finish'] = raw_display['Top_5_finish'].str.replace('%', '').astype(float)/100
+    raw_display['Top_10_finish'] = raw_display['Top_10_finish'].str.replace('%', '').astype(float)/100
+    raw_display['100+%'] = raw_display['100+%'].str.replace('%', '').astype(float)/100
+    raw_display['10x%'] = raw_display['10x%'].str.replace('%', '').astype(float)/100
+    raw_display['11x%'] = raw_display['11x%'].str.replace('%', '').astype(float)/100
+    raw_display['12x%'] = raw_display['12x%'].str.replace('%', '').astype(float)/100
+    raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100
+
+    return raw_display
+
+@st.cache_data
+def grab_csv_data(URL):
+    draftkings_data = pd.read_json(URL)
+    draftkings_data.rename(columns={"Name": "Player"}, inplace = True)
+    
+    return draftkings_data
+
+tab1, tab2 = st.tabs(["Player Overall Projections", "Optimizer"])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+lineup_display = []
+check_list = []
+rand_player = 0
+boost_player = 0
+salaryCut = 0
+
+with tab1:
+    if st.button("Reset Data", key='reset1'):
+              # Clear values from *all* all in-memory and on-disk data caches:
+              # i.e. clear values from both square and cube
+              st.cache_data.clear()
+    hold_display = load_dk_player_model(dk_player_url)
+    csv_data = grab_csv_data(CSV_URL)
+    csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player'])
+    id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID']))
+    hold_container = st.empty()
+    display = hold_display.set_index('Player')
+    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Projections",
+        data=convert_df_to_csv(display),
+        file_name='PGA_DFS_export.csv',
+        mime='text/csv',
+    )
+
+with tab2:
+    col1, col2 = st.columns([1, 4])
+    
+    with col1:
+      
+        max_sal = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100)
+        min_sal = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100)
+        proj_cut = st.number_input('Lowest median allowed', min_value = 0, max_value = 100, value = 50, step = 1)
+        slack_var = st.number_input('Median randomness', min_value = 0, max_value = 5, value = 0, step = 1)
+        totalRuns_raw = st.number_input('How many Lineups', min_value = 1, max_value = 1000, value = 5, step = 1)
+        
+
+    totalRuns = totalRuns_raw
+    cut_group_1 = []
+    cut_group_2 = []
+    force_group_1 = []
+    force_group_2 = []
+    avoid_players = []
+    lock_player = []
+    lineups = []
+    player_pool_raw = []
+    
+    player_pool = []
+    player_count = []
+    player_trim_pool = []
+    portfolio = pd.DataFrame()
+    x = 1
+
+    if st.button('Optimize'):
+        max_proj = 1000
+        max_own = 1000
+        total_proj = 0
+        total_own = 0
+        
+        with col2:
+            with st.spinner('Wait for it...'):
+                with hold_container.container():
+
+                        while x <= totalRuns:
+
+                            raw_proj_file = hold_display
+                            raw_flex_file = raw_proj_file.dropna(how='all')
+                            raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
+                            raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > proj_cut]
+                            flex_file = raw_flex_file
+                            flex_file = flex_file[['Player', 'Salary', 'Median', 'Own', 'LevX']]
+                            flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
+                            flex_file['name_var'] = flex_file['Player']
+                            flex_file['lock'] = flex_file['Player'].isin(lock_player)*1
+                            flex_file['force_group_1'] = flex_file['Player'].isin(force_group_1)*1
+                            flex_file['force_group_2'] = flex_file['Player'].isin(force_group_2)*1
+                            flex_file['cut_group_1'] = flex_file['Player'].isin(cut_group_1)*1
+                            flex_file['cut_group_2'] = flex_file['Player'].isin(cut_group_2)*1
+                            chalk_file = flex_file.sort_values(by='Proj DK Own%', ascending=False)
+                            chalk_group_df = chalk_file.sample(n=10)
+                            chalk_group = chalk_group_df['Player'].tolist()
+                            flex_file['chalk_group'] = flex_file['Player'].isin(chalk_group)*1
+                            flex_file['Pos'] = 'G'
+                            flex_file = flex_file[['Player', 'name_var', 'Pos', 'Salary', 'Median', 'Proj DK Own%', 'lock', 'force_group_1', 'force_group_2', 'cut_group_1', 'cut_group_2', 'chalk_group', 'LevX']]
+                            if x > 1:
+                                if slack_var > 0:
+                                    flex_file['randNumCol'] = np.random.randint(-int(slack_var),int(slack_var), flex_file.shape[0])
+                                elif slack_var ==0:
+                                    flex_file['randNumCol'] = 0
+                            elif x == 1:
+                                flex_file['randNumCol'] = 0
+                            flex_file['Median'] = flex_file['Median'] + flex_file['randNumCol']
+                            flex_file_check = flex_file
+                            check_list.append(flex_file['Median'][4])
+                            player_ids = flex_file.index
+                            
+                            overall_players = flex_file[['Player']]
+                            overall_players['player_var_add'] = flex_file.index
+                            overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
+
+                            player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
+                            total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
+                            player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
+                            player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
+                            player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
+                            player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
+                            player_lev = dict(zip(flex_file['Player'], flex_file['LevX']))
+                            player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
+
+                            obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+
+                            obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                            obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+
+                            obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
+                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal
+                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal
+
+                            for flex in flex_file['Pos'].unique():
+                                sub_idx = flex_file[flex_file['Pos'] != "Var"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 6
+
+                            player_count = []
+                            player_trim = []
+                            lineup_list = []
+
+                            total_score += pulp.lpSum([player_vars[idx]*obj_points_max[idx] for idx in flex_file.index]) <= max_proj - .01
+
+                            total_score.solve()
+                            for v in total_score.variables():
+                                if v.varValue > 0:
+                                    lineup_list.append(v.name)
+                            df = pd.DataFrame(lineup_list)
+                            df['Names'] = df[0].map(player_match)
+                            df['Cost'] = df['Names'].map(player_sal)
+                            df['Proj'] = df['Names'].map(player_proj)
+                            df['Own'] = df['Names'].map(player_own)
+                            total_cost = sum(df['Cost'])
+                            total_own = sum(df['Own'])
+                            total_proj = sum(df['Proj'])
+                            lineup_raw = pd.DataFrame(lineup_list)
+                            lineup_raw['Names'] = lineup_raw[0].map(player_match)
+                            lineup_raw['value'] = lineup_raw[0].map(player_index_match)
+                            lineup_final = lineup_raw.sort_values(by=['value'])
+                            del lineup_final[lineup_final.columns[0]]
+                            del lineup_final[lineup_final.columns[1]]
+                            lineup_final = lineup_final.reset_index(drop=True)
+                            lineup_test = lineup_final
+                            lineup_final = lineup_final.T
+                            lineup_final['Cost'] = total_cost
+                            lineup_final['Proj'] = total_proj
+                            lineup_final['Own'] = total_own
+
+                            if total_cost < 50001:
+                                lineups.append(lineup_final)
+                            
+                            lineup_test['Salary'] = lineup_test['Names'].map(player_sal)
+                            lineup_test['Proj'] = lineup_test['Names'].map(player_proj)
+                            lineup_test['Own'] = lineup_test['Names'].map(player_own)
+                            lineup_test['LevX'] = lineup_test['Names'].map(player_lev)
+                            lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0)
+                            
+                            lineup_display.append(lineup_test)
+                            
+                            with col2:
+                                with st.container():
+                                    st.table(lineup_test)
+
+                            max_proj = total_proj
+                            max_own = total_own
+
+                            check_list.append(total_proj)
+
+                            portfolio = portfolio.append(lineup_final, ignore_index = True)
+
+                            x += 1
+                        
+                        portfolio.rename(columns={0: "Player_1", 1: "Player_2", 2: "Player_3", 3: "Player_4", 4: "Player_5", 5: "Player_6"}, inplace = True)
+                        portfolio = portfolio.dropna()
+
+                        final_outcomes = portfolio
+                        final_outcomes['p1 id'] = final_outcomes['Player_1'].map(id_dict)
+                        final_outcomes['p2 id'] = final_outcomes['Player_2'].map(id_dict)
+                        final_outcomes['p3 id'] = final_outcomes['Player_3'].map(id_dict)
+                        final_outcomes['p4 id'] = final_outcomes['Player_4'].map(id_dict)
+                        final_outcomes['p5 id'] = final_outcomes['Player_5'].map(id_dict)
+                        final_outcomes['p6 id'] = final_outcomes['Player_6'].map(id_dict)
+                        final_outcomes = final_outcomes[['p1 id', 'p2 id', 'p3 id', 'p4 id', 'p5 id', 'p6 id']]
+                        with col1:
+                          st.download_button(
+                              label="Export Lineups",
+                              data=convert_df_to_csv(final_outcomes),
+                              file_name='PGA_DFS_export.csv',
+                              mime='text/csv',
+                          )
+        
+                with hold_container:
+                    hold_container = st.empty()