Spaces:
Running
Running
James McCool
commited on
Commit
·
d69c0ed
1
Parent(s):
a3a9ac4
Initial Commit
Browse files- app.py +589 -0
- app.yaml +10 -0
- requirements.txt +10 -0
app.py
ADDED
@@ -0,0 +1,589 @@
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1 |
+
import streamlit as st
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2 |
+
import numpy as np
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3 |
+
import pandas as pd
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4 |
+
import gspread
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5 |
+
import pymongo
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6 |
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7 |
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st.set_page_config(layout="wide")
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8 |
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@st.cache_resource
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10 |
+
def init_conn():
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uri = st.secrets['mongo_uri']
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
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db = client["MLB_Database"]
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db2 = client["MLB_DFS"]
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return db, db2
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db, db2 = init_conn()
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
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'4x%': '{:.2%}'}
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+
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dk_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
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27 |
+
fd_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
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+
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@st.cache_resource(ttl = 60)
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30 |
+
def init_baselines():
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+
collection = db["Player_Range_Of_Outcomes"]
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32 |
+
cursor = collection.find()
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33 |
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player_frame = pd.DataFrame(cursor)
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34 |
+
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roo_data = player_frame.drop(columns=['_id'])
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36 |
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roo_data['Salary'] = roo_data['Salary'].astype(int)
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37 |
+
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38 |
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dk_roo = roo_data[roo_data['Site'] == 'Draftkings']
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fd_roo = roo_data[roo_data['Site'] == 'Fanduel']
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+
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collection = db["Player_SD_Range_Of_Outcomes"]
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42 |
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cursor = collection.find()
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43 |
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player_frame = pd.DataFrame(cursor)
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44 |
+
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45 |
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sd_roo_data = player_frame.drop(columns=['_id'])
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46 |
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sd_roo_data['Salary'] = sd_roo_data['Salary'].astype(int)
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47 |
+
sd_roo_data = sd_roo_data.rename(columns={'Own': 'Own%'})
|
48 |
+
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49 |
+
collection = db["Scoring_Percentages"]
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50 |
+
cursor = collection.find()
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51 |
+
team_frame = pd.DataFrame(cursor)
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52 |
+
scoring_percentages = team_frame.drop(columns=['_id'])
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53 |
+
scoring_percentages = scoring_percentages[['Names', 'Avg First Inning', 'First Inning Lead Percentage', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage', 'Avg Score', '8+ runs', 'Win Percentage']]
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54 |
+
scoring_percentages['8+ runs'] = scoring_percentages['8+ runs'].replace('%', '', regex=True).astype(float)
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55 |
+
scoring_percentages['Win Percentage'] = scoring_percentages['Win Percentage'].replace('%', '', regex=True).astype(float)
|
56 |
+
dk_hitters_only = dk_roo[dk_roo['pos_group'] != 'Pitchers']
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57 |
+
dk_team_ownership = dk_hitters_only.groupby('Team')['Own%'].sum().reset_index()
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58 |
+
fd_hitters_only = fd_roo[fd_roo['pos_group'] != 'Pitchers']
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59 |
+
fd_team_ownership = fd_hitters_only.groupby('Team')['Own%'].sum().reset_index()
|
60 |
+
scoring_percentages = scoring_percentages.merge(dk_team_ownership, left_on='Names', right_on='Team', how='left')
|
61 |
+
scoring_percentages.rename(columns={'Own%': 'DK Own%'}, inplace=True)
|
62 |
+
scoring_percentages.drop('Team', axis=1, inplace=True)
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63 |
+
scoring_percentages = scoring_percentages.merge(fd_team_ownership, left_on='Names', right_on='Team', how='left')
|
64 |
+
scoring_percentages.rename(columns={'Own%': 'FD Own%'}, inplace=True)
|
65 |
+
scoring_percentages.drop('Team', axis=1, inplace=True)
|
66 |
+
|
67 |
+
return roo_data, sd_roo_data, scoring_percentages, dk_roo, fd_roo
|
68 |
+
|
69 |
+
@st.cache_data(ttl = 60)
|
70 |
+
def init_DK_lineups(type_var, slate_var):
|
71 |
+
|
72 |
+
if type_var == 'Regular':
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73 |
+
if slate_var == 'Main':
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74 |
+
collection = db['DK_MLB_name_map']
|
75 |
+
cursor = collection.find()
|
76 |
+
raw_data = pd.DataFrame(list(cursor))
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77 |
+
names_dict = dict(zip(raw_data['key'], raw_data['value']))
|
78 |
+
|
79 |
+
collection = db['DK_MLB_seed_frame']
|
80 |
+
cursor = collection.find().limit(10000)
|
81 |
+
|
82 |
+
raw_display = pd.DataFrame(list(cursor))
|
83 |
+
raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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84 |
+
dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
|
85 |
+
# Map names
|
86 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
|
87 |
+
elif slate_var == 'Secondary':
|
88 |
+
collection = db['DK_MLB_Secondary_name_map']
|
89 |
+
cursor = collection.find()
|
90 |
+
raw_data = pd.DataFrame(list(cursor))
|
91 |
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names_dict = dict(zip(raw_data['key'], raw_data['value']))
|
92 |
+
|
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+
collection = db['DK_MLB_Secondary_seed_frame']
|
94 |
+
cursor = collection.find().limit(10000)
|
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+
|
96 |
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raw_display = pd.DataFrame(list(cursor))
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+
raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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98 |
+
dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
|
99 |
+
# Map names
|
100 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
|
101 |
+
elif slate_var == 'Auxiliary':
|
102 |
+
collection = db['DK_MLB_Turbo_name_map']
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103 |
+
cursor = collection.find()
|
104 |
+
raw_data = pd.DataFrame(list(cursor))
|
105 |
+
names_dict = dict(zip(raw_data['key'], raw_data['value']))
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106 |
+
|
107 |
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collection = db['DK_MLB_Turbo_seed_frame']
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108 |
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cursor = collection.find().limit(10000)
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109 |
+
|
110 |
+
raw_display = pd.DataFrame(list(cursor))
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111 |
+
raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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112 |
+
dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
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113 |
+
# Map names
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114 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
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115 |
+
elif type_var == 'Showdown':
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116 |
+
if slate_var == 'Main':
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117 |
+
collection = db2['DK_MLB_SD1_seed_frame']
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118 |
+
cursor = collection.find().limit(10000)
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119 |
+
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120 |
+
raw_display = pd.DataFrame(list(cursor))
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121 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
|
122 |
+
elif slate_var == 'Secondary':
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123 |
+
collection = db2['DK_MLB_SD2_seed_frame']
|
124 |
+
cursor = collection.find().limit(10000)
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125 |
+
|
126 |
+
raw_display = pd.DataFrame(list(cursor))
|
127 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
|
128 |
+
elif slate_var == 'Auxiliary':
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129 |
+
collection = db2['DK_MLB_SD3_seed_frame']
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130 |
+
cursor = collection.find().limit(10000)
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131 |
+
|
132 |
+
raw_display = pd.DataFrame(list(cursor))
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133 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
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134 |
+
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135 |
+
DK_seed = raw_display.to_numpy()
|
136 |
+
|
137 |
+
return DK_seed
|
138 |
+
|
139 |
+
@st.cache_data(ttl = 60)
|
140 |
+
def init_FD_lineups(type_var,slate_var):
|
141 |
+
|
142 |
+
if type_var == 'Regular':
|
143 |
+
if slate_var == 'Main':
|
144 |
+
collection = db['FD_MLB_name_map']
|
145 |
+
cursor = collection.find()
|
146 |
+
raw_data = pd.DataFrame(list(cursor))
|
147 |
+
names_dict = dict(zip(raw_data['key'], raw_data['value']))
|
148 |
+
|
149 |
+
collection = db['FD_MLB_seed_frame']
|
150 |
+
cursor = collection.find().limit(10000)
|
151 |
+
|
152 |
+
raw_display = pd.DataFrame(list(cursor))
|
153 |
+
raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
|
154 |
+
dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
|
155 |
+
# Map names
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156 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
|
157 |
+
elif slate_var == 'Secondary':
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158 |
+
collection = db['FD_MLB_Secondary_name_map']
|
159 |
+
cursor = collection.find()
|
160 |
+
raw_data = pd.DataFrame(list(cursor))
|
161 |
+
names_dict = dict(zip(raw_data['key'], raw_data['value']))
|
162 |
+
|
163 |
+
collection = db['FD_MLB_Secondary_seed_frame']
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164 |
+
cursor = collection.find().limit(10000)
|
165 |
+
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166 |
+
raw_display = pd.DataFrame(list(cursor))
|
167 |
+
raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
|
168 |
+
dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
|
169 |
+
# Map names
|
170 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
|
171 |
+
elif slate_var == 'Auxiliary':
|
172 |
+
collection = db['FD_MLB_Turbo_name_map']
|
173 |
+
cursor = collection.find()
|
174 |
+
raw_data = pd.DataFrame(list(cursor))
|
175 |
+
names_dict = dict(zip(raw_data['key'], raw_data['value']))
|
176 |
+
|
177 |
+
collection = db['FD_MLB_Turbo_seed_frame']
|
178 |
+
cursor = collection.find().limit(10000)
|
179 |
+
|
180 |
+
raw_display = pd.DataFrame(list(cursor))
|
181 |
+
raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
|
182 |
+
dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
|
183 |
+
# Map names
|
184 |
+
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
|
185 |
+
|
186 |
+
elif type_var == 'Showdown':
|
187 |
+
if slate_var == 'Main':
|
188 |
+
collection = db2['FD_MLB_SD1_seed_frame']
|
189 |
+
cursor = collection.find().limit(10000)
|
190 |
+
|
191 |
+
raw_display = pd.DataFrame(list(cursor))
|
192 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
|
193 |
+
elif slate_var == 'Secondary':
|
194 |
+
collection = db2['FD_MLB_SD2_seed_frame']
|
195 |
+
cursor = collection.find().limit(10000)
|
196 |
+
|
197 |
+
raw_display = pd.DataFrame(list(cursor))
|
198 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
|
199 |
+
elif slate_var == 'Auxiliary':
|
200 |
+
collection = db2['FD_MLB_SD3_seed_frame']
|
201 |
+
cursor = collection.find().limit(10000)
|
202 |
+
|
203 |
+
raw_display = pd.DataFrame(list(cursor))
|
204 |
+
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
|
205 |
+
|
206 |
+
FD_seed = raw_display.to_numpy()
|
207 |
+
|
208 |
+
return FD_seed
|
209 |
+
|
210 |
+
@st.cache_data
|
211 |
+
def convert_df_to_csv(df):
|
212 |
+
return df.to_csv().encode('utf-8')
|
213 |
+
|
214 |
+
@st.cache_data
|
215 |
+
def convert_df(array):
|
216 |
+
array = pd.DataFrame(array, columns=column_names)
|
217 |
+
return array.to_csv().encode('utf-8')
|
218 |
+
|
219 |
+
col1, col2 = st.columns([1, 9])
|
220 |
+
with col1:
|
221 |
+
if st.button("Load/Reset Data", key='reset'):
|
222 |
+
st.cache_data.clear()
|
223 |
+
roo_data, sd_roo_data, scoring_percentages, dk_roo, fd_roo = init_baselines()
|
224 |
+
hold_display = roo_data
|
225 |
+
dk_lineups = init_DK_lineups('Regular', 'Main')
|
226 |
+
fd_lineups = init_FD_lineups('Regular', 'Main')
|
227 |
+
for key in st.session_state.keys():
|
228 |
+
del st.session_state[key]
|
229 |
+
with col2:
|
230 |
+
with st.container():
|
231 |
+
col1, col2 = st.columns([3, 3])
|
232 |
+
with col1:
|
233 |
+
view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_var')
|
234 |
+
with col2:
|
235 |
+
site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_var')
|
236 |
+
|
237 |
+
|
238 |
+
tab1, tab2, tab3 = st.tabs(["Scoring Percentages", "Player ROO", "Optimals"])
|
239 |
+
|
240 |
+
roo_data, sd_roo_data, scoring_percentages, dk_roo, fd_roo = init_baselines()
|
241 |
+
hold_display = roo_data
|
242 |
+
|
243 |
+
with tab1:
|
244 |
+
st.header("Scoring Percentages")
|
245 |
+
with st.expander("Info and Filters"):
|
246 |
+
with st.container():
|
247 |
+
slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1')
|
248 |
+
own_var1 = st.radio("How would you like to display team ownership?", ('Sum', 'Average'), key='own_var1')
|
249 |
+
if view_var == "Simple":
|
250 |
+
scoring_percentages = scoring_percentages[['Names', 'Avg Score', '8+ runs', 'Win Percentage']]
|
251 |
+
st.dataframe(scoring_percentages.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
252 |
+
elif view_var == "Advanced":
|
253 |
+
st.dataframe(scoring_percentages.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
254 |
+
|
255 |
+
with tab2:
|
256 |
+
st.header("Player ROO")
|
257 |
+
with st.expander("Info and Filters"):
|
258 |
+
with st.container():
|
259 |
+
slate_type_var2 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var2')
|
260 |
+
slate_var2 = st.radio("Which slate data are you loading?", ('Main', 'Secondary', 'Auxiliary'), key='slate_var2')
|
261 |
+
pos_var2 = st.radio("Which position group would you like to view?", ('All', 'Pitchers', 'Hitters'), key='pos_var2')
|
262 |
+
team_var2 = st.selectbox("Which team would you like to view?", ['All', 'Specific'], key='team_var2')
|
263 |
+
if team_var2 == 'Specific':
|
264 |
+
team_select2 = st.multiselect("Which team would you like to view?", roo_data['Team'].unique(), key='team_select2')
|
265 |
+
else:
|
266 |
+
team_select2 = None
|
267 |
+
if slate_type_var2 == 'Regular':
|
268 |
+
if site_var == 'Draftkings':
|
269 |
+
|
270 |
+
player_roo_raw = dk_roo.copy()
|
271 |
+
|
272 |
+
if pos_var2 == 'All':
|
273 |
+
pass
|
274 |
+
elif pos_var2 == 'Pitchers':
|
275 |
+
player_roo_raw = player_roo_raw[player_roo_raw['pos_group'] == 'Pitchers']
|
276 |
+
elif pos_var2 == 'Hitters':
|
277 |
+
player_roo_raw = player_roo_raw[player_roo_raw['pos_group'] == 'Hitters']
|
278 |
+
|
279 |
+
elif site_var == 'Fanduel':
|
280 |
+
|
281 |
+
player_roo_raw = fd_roo.copy()
|
282 |
+
|
283 |
+
if pos_var2 == 'All':
|
284 |
+
pass
|
285 |
+
elif pos_var2 == 'Pitchers':
|
286 |
+
player_roo_raw = player_roo_raw[player_roo_raw['pos_group'] == 'Pitchers']
|
287 |
+
elif pos_var2 == 'Hitters':
|
288 |
+
player_roo_raw = player_roo_raw[player_roo_raw['pos_group'] == 'Hitters']
|
289 |
+
|
290 |
+
if slate_var2 == 'Main':
|
291 |
+
player_roo_raw = player_roo_raw[player_roo_raw['Slate'] == 'main_slate']
|
292 |
+
elif slate_var2 == 'Secondary':
|
293 |
+
player_roo_raw = player_roo_raw[player_roo_raw['Slate'] == 'secondary_slate']
|
294 |
+
elif slate_var2 == 'Auxiliary':
|
295 |
+
player_roo_raw = player_roo_raw[player_roo_raw['Slate'] == 'turbo_slate']
|
296 |
+
|
297 |
+
elif slate_type_var2 == 'Showdown':
|
298 |
+
player_roo_raw = sd_roo_data.copy()
|
299 |
+
if site_var == 'Draftkings':
|
300 |
+
player_roo_raw['Site'] = 'Draftkings'
|
301 |
+
elif site_var == 'Fanduel':
|
302 |
+
player_roo_raw['Site'] = 'Fanduel'
|
303 |
+
|
304 |
+
if team_select2:
|
305 |
+
player_roo_raw = player_roo_raw[player_roo_raw['Team'].isin(team_select2)]
|
306 |
+
|
307 |
+
player_roo_disp = player_roo_raw
|
308 |
+
|
309 |
+
if slate_type_var2 == 'Regular':
|
310 |
+
player_roo_disp = player_roo_disp.drop(columns=['Site', 'Slate', 'pos_group', 'timestamp', 'player_ID'])
|
311 |
+
elif slate_type_var2 == 'Showdown':
|
312 |
+
player_roo_disp = player_roo_disp.drop(columns=['site', 'slate', 'version', 'timestamp'])
|
313 |
+
|
314 |
+
if view_var == "Simple":
|
315 |
+
try:
|
316 |
+
player_roo_disp = player_roo_disp[['Player', 'Position', 'Salary', 'Median', 'Ceiling', 'Own%']]
|
317 |
+
st.dataframe(player_roo_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
318 |
+
except:
|
319 |
+
st.dataframe(player_roo_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
320 |
+
elif view_var == "Advanced":
|
321 |
+
try:
|
322 |
+
st.dataframe(player_roo_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
323 |
+
except:
|
324 |
+
st.dataframe(player_roo_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True, hide_index=True)
|
325 |
+
|
326 |
+
with tab3:
|
327 |
+
st.header("Optimals")
|
328 |
+
with st.expander("Info and Filters"):
|
329 |
+
with st.container():
|
330 |
+
slate_type_var3 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var3')
|
331 |
+
slate_var3 = st.radio("Which slate data are you loading?", ('Main', 'Secondary', 'Auxiliary'), key='slate_var3')
|
332 |
+
|
333 |
+
if slate_type_var3 == 'Regular':
|
334 |
+
if site_var == 'Draftkings':
|
335 |
+
dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
|
336 |
+
elif site_var == 'Fanduel':
|
337 |
+
fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
|
338 |
+
elif slate_type_var3 == 'Showdown':
|
339 |
+
if site_var == 'Draftkings':
|
340 |
+
dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
|
341 |
+
elif site_var == 'Fanduel':
|
342 |
+
fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
|
343 |
+
lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
|
344 |
+
|
345 |
+
if slate_type_var3 == 'Regular':
|
346 |
+
raw_baselines = roo_data
|
347 |
+
elif slate_type_var3 == 'Showdown':
|
348 |
+
raw_baselines = sd_roo_data
|
349 |
+
|
350 |
+
if site_var == 'Draftkings':
|
351 |
+
if slate_type_var3 == 'Regular':
|
352 |
+
ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings']
|
353 |
+
player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
|
354 |
+
elif slate_type_var3 == 'Showdown':
|
355 |
+
player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
|
356 |
+
# Get the minimum and maximum ownership values from dk_lineups
|
357 |
+
min_own = np.min(dk_lineups[:,8])
|
358 |
+
max_own = np.max(dk_lineups[:,8])
|
359 |
+
column_names = dk_columns
|
360 |
+
|
361 |
+
player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
|
362 |
+
if player_var1 == 'Specific Players':
|
363 |
+
player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
|
364 |
+
elif player_var1 == 'Full Slate':
|
365 |
+
player_var2 = raw_baselines.Player.values.tolist()
|
366 |
+
|
367 |
+
elif site_var == 'Fanduel':
|
368 |
+
raw_baselines = hold_display
|
369 |
+
if slate_type_var3 == 'Regular':
|
370 |
+
ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel']
|
371 |
+
player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
|
372 |
+
elif slate_type_var3 == 'Showdown':
|
373 |
+
player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
|
374 |
+
min_own = np.min(fd_lineups[:,8])
|
375 |
+
max_own = np.max(fd_lineups[:,8])
|
376 |
+
column_names = fd_columns
|
377 |
+
|
378 |
+
player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
|
379 |
+
if player_var1 == 'Specific Players':
|
380 |
+
player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
|
381 |
+
elif player_var1 == 'Full Slate':
|
382 |
+
player_var2 = raw_baselines.Player.values.tolist()
|
383 |
+
|
384 |
+
if st.button("Prepare data export", key='data_export'):
|
385 |
+
data_export = st.session_state.working_seed.copy()
|
386 |
+
# if site_var == 'Draftkings':
|
387 |
+
# for col_idx in range(6):
|
388 |
+
# data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
389 |
+
# elif site_var == 'Fanduel':
|
390 |
+
# for col_idx in range(6):
|
391 |
+
# data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
392 |
+
st.download_button(
|
393 |
+
label="Export optimals set",
|
394 |
+
data=convert_df(data_export),
|
395 |
+
file_name='MLB_optimals_export.csv',
|
396 |
+
mime='text/csv',
|
397 |
+
)
|
398 |
+
|
399 |
+
if site_var == 'Draftkings':
|
400 |
+
if 'working_seed' in st.session_state:
|
401 |
+
st.session_state.working_seed = st.session_state.working_seed
|
402 |
+
if player_var1 == 'Specific Players':
|
403 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
404 |
+
elif player_var1 == 'Full Slate':
|
405 |
+
st.session_state.working_seed = dk_lineups.copy()
|
406 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
407 |
+
elif 'working_seed' not in st.session_state:
|
408 |
+
st.session_state.working_seed = dk_lineups.copy()
|
409 |
+
st.session_state.working_seed = st.session_state.working_seed
|
410 |
+
if player_var1 == 'Specific Players':
|
411 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
412 |
+
elif player_var1 == 'Full Slate':
|
413 |
+
st.session_state.working_seed = dk_lineups.copy()
|
414 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
415 |
+
|
416 |
+
elif site_var == 'Fanduel':
|
417 |
+
if 'working_seed' in st.session_state:
|
418 |
+
st.session_state.working_seed = st.session_state.working_seed
|
419 |
+
if player_var1 == 'Specific Players':
|
420 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
421 |
+
elif player_var1 == 'Full Slate':
|
422 |
+
st.session_state.working_seed = fd_lineups.copy()
|
423 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
424 |
+
elif 'working_seed' not in st.session_state:
|
425 |
+
st.session_state.working_seed = fd_lineups.copy()
|
426 |
+
st.session_state.working_seed = st.session_state.working_seed
|
427 |
+
if player_var1 == 'Specific Players':
|
428 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
429 |
+
elif player_var1 == 'Full Slate':
|
430 |
+
st.session_state.working_seed = fd_lineups.copy()
|
431 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
432 |
+
|
433 |
+
export_file = st.session_state.data_export_display.copy()
|
434 |
+
# if site_var == 'Draftkings':
|
435 |
+
# for col_idx in range(6):
|
436 |
+
# export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
437 |
+
# elif site_var == 'Fanduel':
|
438 |
+
# for col_idx in range(6):
|
439 |
+
# export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
440 |
+
|
441 |
+
with st.container():
|
442 |
+
if st.button("Reset Optimals", key='reset3'):
|
443 |
+
for key in st.session_state.keys():
|
444 |
+
del st.session_state[key]
|
445 |
+
if site_var == 'Draftkings':
|
446 |
+
st.session_state.working_seed = dk_lineups.copy()
|
447 |
+
elif site_var == 'Fanduel':
|
448 |
+
st.session_state.working_seed = fd_lineups.copy()
|
449 |
+
if 'data_export_display' in st.session_state:
|
450 |
+
st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
|
451 |
+
st.download_button(
|
452 |
+
label="Export display optimals",
|
453 |
+
data=convert_df(export_file),
|
454 |
+
file_name='MLB_display_optimals.csv',
|
455 |
+
mime='text/csv',
|
456 |
+
)
|
457 |
+
|
458 |
+
with st.container():
|
459 |
+
if 'working_seed' in st.session_state:
|
460 |
+
# Create a new dataframe with summary statistics
|
461 |
+
if site_var == 'Draftkings':
|
462 |
+
summary_df = pd.DataFrame({
|
463 |
+
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
464 |
+
'Salary': [
|
465 |
+
np.min(st.session_state.working_seed[:,10]),
|
466 |
+
np.mean(st.session_state.working_seed[:,10]),
|
467 |
+
np.max(st.session_state.working_seed[:,10]),
|
468 |
+
np.std(st.session_state.working_seed[:,10])
|
469 |
+
],
|
470 |
+
'Proj': [
|
471 |
+
np.min(st.session_state.working_seed[:,11]),
|
472 |
+
np.mean(st.session_state.working_seed[:,11]),
|
473 |
+
np.max(st.session_state.working_seed[:,11]),
|
474 |
+
np.std(st.session_state.working_seed[:,11])
|
475 |
+
],
|
476 |
+
'Own': [
|
477 |
+
np.min(st.session_state.working_seed[:,16]),
|
478 |
+
np.mean(st.session_state.working_seed[:,16]),
|
479 |
+
np.max(st.session_state.working_seed[:,16]),
|
480 |
+
np.std(st.session_state.working_seed[:,16])
|
481 |
+
]
|
482 |
+
})
|
483 |
+
elif site_var == 'Fanduel':
|
484 |
+
summary_df = pd.DataFrame({
|
485 |
+
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
486 |
+
'Salary': [
|
487 |
+
np.min(st.session_state.working_seed[:,9]),
|
488 |
+
np.mean(st.session_state.working_seed[:,9]),
|
489 |
+
np.max(st.session_state.working_seed[:,9]),
|
490 |
+
np.std(st.session_state.working_seed[:,9])
|
491 |
+
],
|
492 |
+
'Proj': [
|
493 |
+
np.min(st.session_state.working_seed[:,10]),
|
494 |
+
np.mean(st.session_state.working_seed[:,10]),
|
495 |
+
np.max(st.session_state.working_seed[:,10]),
|
496 |
+
np.std(st.session_state.working_seed[:,10])
|
497 |
+
],
|
498 |
+
'Own': [
|
499 |
+
np.min(st.session_state.working_seed[:,15]),
|
500 |
+
np.mean(st.session_state.working_seed[:,15]),
|
501 |
+
np.max(st.session_state.working_seed[:,15]),
|
502 |
+
np.std(st.session_state.working_seed[:,15])
|
503 |
+
]
|
504 |
+
})
|
505 |
+
|
506 |
+
# Set the index of the summary dataframe as the "Metric" column
|
507 |
+
summary_df = summary_df.set_index('Metric')
|
508 |
+
|
509 |
+
# Display the summary dataframe
|
510 |
+
st.subheader("Optimal Statistics")
|
511 |
+
st.dataframe(summary_df.style.format({
|
512 |
+
'Salary': '{:.2f}',
|
513 |
+
'Proj': '{:.2f}',
|
514 |
+
'Own': '{:.2f}'
|
515 |
+
}).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
|
516 |
+
|
517 |
+
with st.container():
|
518 |
+
tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
|
519 |
+
with tab1:
|
520 |
+
if 'data_export_display' in st.session_state:
|
521 |
+
if site_var == 'Draftkings':
|
522 |
+
player_columns = st.session_state.data_export_display.iloc[:, :10]
|
523 |
+
elif site_var == 'Fanduel':
|
524 |
+
player_columns = st.session_state.data_export_display.iloc[:, :9]
|
525 |
+
|
526 |
+
# Flatten the DataFrame and count unique values
|
527 |
+
value_counts = player_columns.values.flatten().tolist()
|
528 |
+
value_counts = pd.Series(value_counts).value_counts()
|
529 |
+
|
530 |
+
percentages = (value_counts / lineup_num_var * 100).round(2)
|
531 |
+
|
532 |
+
# Create a DataFrame with the results
|
533 |
+
summary_df = pd.DataFrame({
|
534 |
+
'Player': value_counts.index,
|
535 |
+
'Frequency': value_counts.values,
|
536 |
+
'Percentage': percentages.values
|
537 |
+
})
|
538 |
+
|
539 |
+
# Sort by frequency in descending order
|
540 |
+
summary_df['Salary'] = summary_df['Player'].map(player_salaries)
|
541 |
+
summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
|
542 |
+
summary_df = summary_df.sort_values('Frequency', ascending=False)
|
543 |
+
summary_df = summary_df.set_index('Player')
|
544 |
+
|
545 |
+
# Display the table
|
546 |
+
st.write("Player Frequency Table:")
|
547 |
+
st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
|
548 |
+
|
549 |
+
st.download_button(
|
550 |
+
label="Export player frequency",
|
551 |
+
data=convert_df_to_csv(summary_df),
|
552 |
+
file_name='MLB_player_frequency.csv',
|
553 |
+
mime='text/csv',
|
554 |
+
)
|
555 |
+
with tab2:
|
556 |
+
if 'working_seed' in st.session_state:
|
557 |
+
if site_var == 'Draftkings':
|
558 |
+
player_columns = st.session_state.working_seed[:, :10]
|
559 |
+
elif site_var == 'Fanduel':
|
560 |
+
player_columns = st.session_state.working_seed[:, :9]
|
561 |
+
|
562 |
+
# Flatten the DataFrame and count unique values
|
563 |
+
value_counts = player_columns.flatten().tolist()
|
564 |
+
value_counts = pd.Series(value_counts).value_counts()
|
565 |
+
|
566 |
+
percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
|
567 |
+
# Create a DataFrame with the results
|
568 |
+
summary_df = pd.DataFrame({
|
569 |
+
'Player': value_counts.index,
|
570 |
+
'Frequency': value_counts.values,
|
571 |
+
'Percentage': percentages.values
|
572 |
+
})
|
573 |
+
|
574 |
+
# Sort by frequency in descending order
|
575 |
+
summary_df['Salary'] = summary_df['Player'].map(player_salaries)
|
576 |
+
summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
|
577 |
+
summary_df = summary_df.sort_values('Frequency', ascending=False)
|
578 |
+
summary_df = summary_df.set_index('Player')
|
579 |
+
|
580 |
+
# Display the table
|
581 |
+
st.write("Seed Frame Frequency Table:")
|
582 |
+
st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
|
583 |
+
|
584 |
+
st.download_button(
|
585 |
+
label="Export seed frame frequency",
|
586 |
+
data=convert_df_to_csv(summary_df),
|
587 |
+
file_name='MLB_seed_frame_frequency.csv',
|
588 |
+
mime='text/csv',
|
589 |
+
)
|
app.yaml
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
runtime: python
|
2 |
+
env: flex
|
3 |
+
|
4 |
+
runtime_config:
|
5 |
+
python_version: 3
|
6 |
+
|
7 |
+
entrypoint: streamlit run streamlit-app.py --server.port $PORT
|
8 |
+
|
9 |
+
automatic_scaling:
|
10 |
+
max_num_instances: 200
|
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
streamlit
|
2 |
+
gspread
|
3 |
+
openpyxl
|
4 |
+
matplotlib
|
5 |
+
streamlit-aggrid
|
6 |
+
pulp
|
7 |
+
docker
|
8 |
+
plotly
|
9 |
+
scipy
|
10 |
+
pymongo
|