Create app.py

app.py

@@ -0,0 +1,143 @@
+import streamlit as st
+import numpy as np
+from numpy import where as np_where
+import pandas as pd
+import gspread
+import plotly.express as px
+import scipy.stats as stats
+from pymongo import MongoClient
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": st.secrets['model_sheets_connect_pk'],
+          "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": "[email protected]",
+          "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"
+        }
+        
+        credentials2 = {
+          "type": "service_account",
+          "project_id": "sheets-api-connect-378620",
+          "private_key_id": st.secrets['sheets_api_connect_pk'],
+          "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"
+        }
+     
+        NFL_Data = st.secrets['NFL_Data']
+
+        uri = st.secrets['mongo_uri']
+        client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
+        dfs_db = client["NFL_Database"]
+        props_db = client["Props_DB"]
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, NFL_Data, props_db, dfs_db
+    
+gcservice_account, gcservice_account2, NFL_Data, props_db, dfs_db = init_conn()
+
+game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
+american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
+
+@st.cache_resource(ttl=600)
+def init_baselines():
+    collection = dfs_db["Game_Betting_Model"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    game_model = raw_display[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+    
+    collection = dfs_db["Player_Stats"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    overall_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'rush_att', 'rec', 'dropbacks', 'rush_yards', 'rush_tds', 'rec_yards', 'rec_tds', 'pass_att', 'pass_yards', 'pass_tds', 'PPR', 'Half_PPR']]
+    
+    collection = dfs_db["Prop_Trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    prop_trends = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
+    
+    collection = dfs_db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    timestamp = load_display['timestamp'][0]
+    
+    collection = dfs_db["Prop_Trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    prop_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
+    
+    collection = dfs_db['Pick6_Trends']
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    pick_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge', 'last_name', 'P6_name', 'Full_name']]
+
+    collection = props_db["NFL_Props"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']]
+    market_props['over_prop'] = market_props['Projection']
+    market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+    market_props['under_prop'] = market_props['Projection']
+    market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+
+    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+with tab1:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
+              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
+    team_frame = game_model
+    if line_var1 == 'Percentage':
+        team_frame = team_frame[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        team_frame = team_frame.set_index('Team')
+        try:
+            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), use_container_width = True)
+        except:
+            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    if line_var1 == 'American':
+        team_frame = team_frame[['Team', 'Opp', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        team_frame = team_frame.set_index('Team')
+        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+    
+    st.download_button(
+        label="Export Team Model",
+        data=convert_df_to_csv(team_frame),
+        file_name='NFL_team_betting_export.csv',
+        mime='text/csv',
+        key='team_export',
+    )