Update app.py

app.py

@@ -1,8 +1,36 @@
 import streamlit as st
 import requests
+import requests
+import pandas as pd
+from pandas import DataFrame
+import numpy as np
+import gspread
+import pytz
+from datetime import datetime
+from datetime import date, timedelta
+import time
+from discordwebhook import Discord
 
 st.set_page_config(layout="wide")
 
+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)
+
 purge_cache = "https://sheetdb.io/api/v1/svino07zkd6j6/cache/purge/f8fc41b2"
 
 traderater = "https://www.fantasylife.com/api/projections/v1/nfl/ratemytrade/season/update"
@@ -24,11 +52,14 @@ dev_agg_url = "https://fantasylife.dev.spotlightsportsb2b.com/api/projections/v1
 freedman_nfl_game_model = "https://www.fantasylife.com/api/projections/v1/nfl-odds/james/game/update"
 thor_ncaaf_game_model = "https://www.fantasylife.com/api/projections/v1/ncaafb-odds/james/game/update"
 
+NCAAF_model_url = 'https://docs.google.com/spreadsheets/d/17QUsCEMVAFbOteenUbi18H2kgVYx4cwBGs9dFCi4ri4/edit?pli=1&gid=1637459210#gid=1637459210'
+pff_url = 'https://www.pff.com/api/scoreboard/schedule?league=ncaa&season=2024'
+
 headers = {
     'Authorization': 'Bearer 6984da1f-2c81-4140-8206-d018af38533f',
 }
 
-tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(['Season Long (Live Site)', 'Season Long (Dev Site)', 'Weekly', 'Game Model', 'Trade Rater', 'Rest of Season'])
+tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Season Long (Live Site)', 'Season Long (Dev Site)', 'Weekly', 'Game Model', 'Trade Rater', 'Rest of Season', 'NCAAF Script'])
 
 with tab1:
     with st.container():
@@ -144,4 +175,246 @@ with tab6:
             if st.button("Rest of Season Update", key='reset13'):
                   response = requests.post(ros_james_url, headers=headers)
                   if response.status_code == 200:
-                      st.write("Uploading!")
+                      st.write("Uploading!")
+
+with tab7:
+    with st.container():
+        col1, col2, col3 = st.columns([3, 3, 3])
+    
+        with col1:
+            st.info("Update NCAAF schedule and ranks")
+            if st.button("Update NCAAF", key='reset14'):
+                sh = gc.open_by_url(NCAAF_model_url)
+                worksheet = sh.worksheet('ATLranks')
+                ranks_df = DataFrame(worksheet.get_all_records())
+                ranks_dict = dict(zip(ranks_df.Team, ranks_df.ATL))
+                conf_dict = dict(zip(ranks_df.Team, ranks_df.Conference))
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('HFA')
+                hfa_df = DataFrame(worksheet.get_all_records())
+                hfa_dict = dict(zip(hfa_df.Team, hfa_df.HFA))
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('Odds')
+                odds_df = DataFrame(worksheet.get_all_records())
+                odds_dict = dict(zip(odds_df.Point_Spread, odds_df.Favorite_Win_Chance))
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('Acronyms')
+                acros_df = DataFrame(worksheet.get_all_records())
+                right_acro = acros_df['Team'].tolist()
+                wrong_acro = acros_df['Acro'].tolist()
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('Add games')
+                add_games_df = DataFrame(worksheet.get_all_records())
+                add_games_df.replace('', np.nan, inplace=True)
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('Completed games')
+                comp_games_df = DataFrame(worksheet.get_all_records())
+                comp_games_df.replace('', np.nan, inplace=True)
+
+                time.sleep(.5)
+
+                worksheet = sh.worksheet('LY_scoring')
+                lyscore_df = DataFrame(worksheet.get_all_records())
+                for checkVar in range(len(wrong_acro)):
+                    lyscore_df['Team'] = lyscore_df['Team'].replace(wrong_acro, right_acro)
+
+                PFA_dict = dict(zip(lyscore_df.Team, lyscore_df.PF_G_adj))
+                PAA_dict = dict(zip(lyscore_df.Team, lyscore_df.PA_G_adj))
+
+                # Send a GET request to the API
+                response = requests.get(pff_url)
+
+                # Check if the request was successful
+                if response.status_code == 200:
+                    # Parse the JSON content
+                    data = response.json()
+                    
+                    # Extract the "weeks" object
+                    weeks = data.get('weeks', [])
+                    
+                    # Initialize an empty list to store game data
+                    games_list = []
+                    team_list = []
+
+                    # Iterate over each week and its games
+                    for week in weeks:
+                        week_number = week.get('week')
+                        for game in week.get('games', []):
+                            # Add week number to the game dictionary
+                            game['week'] = week_number
+                            away_franchise = game.get('away_franchise', {})
+                            away_franchise_groups = away_franchise.get('groups', {})
+                            away_conf = away_franchise_groups[0]['name']
+                            home_franchise = game.get('home_franchise', {})
+                            home_franchise_groups = home_franchise.get('groups', {})
+                            home_conf = home_franchise_groups[0]['name']
+                            
+                            # Flatten the away and home franchise data
+                            game_data = {
+                                'game_id': game.get('external_game_id'),
+                                'Day': game.get('kickoff_date'),
+                                'CST': game.get('kickoff_raw'),
+                                'away_id': away_franchise.get('abbreviation'),
+                                'Away': away_franchise.get('city'),
+                                'home_id': home_franchise.get('abbreviation'),
+                                'Home': home_franchise.get('city')
+                            }
+                            
+                            home_data = {
+                                'team': home_franchise.get('city'),
+                                'conf': home_conf
+                            }
+                            
+                            away_data = {
+                                'team': away_franchise.get('city'),
+                                'conf': away_conf
+                            }
+                            
+                            merged_data = game_data | game
+                            team_data = home_data | away_data
+                            games_list.append(merged_data)
+                            team_list.append(home_data)
+                            team_list.append(away_data)
+
+                    # Create a DataFrame from the games list
+                    df = pd.DataFrame(games_list)
+                    team_df = pd.DataFrame(team_list)
+                    team_df = team_df.drop_duplicates(subset=['team', 'conf'])
+
+                    # Display the DataFrame
+                    print(df)
+                else:
+                    print(f"Failed to retrieve data. HTTP Status code: {response.status_code}")
+                    
+                df_raw = df[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'point_spread', 'over_under', 'Day', 'CST']]
+                df_raw['conf_game'] = np.nan
+                df_raw['Away_ATL'] = np.nan
+                df_raw['Home_ATL'] = np.nan
+                df_raw['Home Spread'] = np.nan
+                df_raw['Proj Total'] = np.nan
+                df_raw['Neutral'] = np.nan
+                df_raw['Notes'] = np.nan
+                df_raw['over_under'].fillna("", inplace=True)
+                df_raw['over_under'] = pd.to_numeric(df_raw['over_under'], errors='coerce')
+                df_raw = df_raw[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'conf_game', 'Away_ATL', 'Home_ATL', 'point_spread', 'Home Spread', 'over_under', 'Proj Total', 'Day', 'CST', 'Neutral', 'Notes']]
+                add_games_merge = add_games_df
+                comp_games_merge = comp_games_df
+                conf_adj = dict(zip(add_games_merge['game_id'], add_games_merge['conf_game']))
+                df_merge_1 = pd.concat([add_games_merge, df_raw])
+                df_cleaned = pd.concat([comp_games_merge, df_merge_1])
+                df_cleaned = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'point_spread', 'over_under', 'Day', 'CST']]
+                df_cleaned = df_cleaned.drop_duplicates(subset=['game_id'])
+
+                def cond_away_PFA(row, df):
+                    mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
+                    return df.loc[mask, 'Away_PFA'].mean()
+
+                def cond_home_PFA(row, df):
+                    mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
+                    return df.loc[mask, 'Home_PFA'].mean()
+
+                def cond_away_PAA(row, df):
+                    mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
+                    return df.loc[mask, 'Away_PAA'].mean()
+
+                def cond_home_PAA(row, df):
+                    mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
+                    return df.loc[mask, 'Home_PAA'].mean()
+
+                for checkVar in range(len(wrong_acro)):
+                    df_cleaned['Away'] = df_cleaned['Away'].replace(wrong_acro, right_acro)
+                    df_cleaned['Home'] = df_cleaned['Home'].replace(wrong_acro, right_acro)
+                df_cleaned['Away_conf'] = df_cleaned['Away'].map(conf_dict)
+                df_cleaned['Home_conf'] = df_cleaned['Home'].map(conf_dict)
+                df_cleaned['conf_game_var'] = np.where((df_cleaned['Away_conf'] == df_cleaned['Home_conf']), 1, 0)
+                df_cleaned['conf_game'] = df_cleaned.apply(lambda row: conf_adj.get(row['game_id'], row['conf_game_var']), axis=1)
+                df_cleaned['Away_ATL'] = df_cleaned['Away'].map(ranks_dict)
+                df_cleaned['Home_ATL'] = df_cleaned['Home'].map(ranks_dict)
+                df_cleaned['Away_PFA'] = df_cleaned['Away'].map(PFA_dict)
+                df_cleaned['Home_PFA'] = df_cleaned['Home'].map(PFA_dict)
+                df_cleaned['Away_PAA'] = df_cleaned['Away'].map(PAA_dict)
+                df_cleaned['Home_PAA'] = df_cleaned['Home'].map(PAA_dict)
+
+                # Apply the function to each row in the DataFrame
+                df_cleaned['cond_away_PFA'] = df_cleaned.apply(lambda row: cond_away_PFA(row, df_cleaned), axis=1)
+                df_cleaned['cond_home_PFA'] = df_cleaned.apply(lambda row: cond_home_PFA(row, df_cleaned), axis=1)
+                df_cleaned['cond_away_PAA'] = df_cleaned.apply(lambda row: cond_away_PAA(row, df_cleaned), axis=1)
+                df_cleaned['cond_home_PAA'] = df_cleaned.apply(lambda row: cond_home_PAA(row, df_cleaned), axis=1)
+
+                df_cleaned['cond_away_PFA'] = np.where((df_cleaned['Away_ATL'] <= 0), 18, df_cleaned['cond_away_PFA'])
+                df_cleaned['cond_away_PAA'] = np.where((df_cleaned['Away_ATL'] <= 0), 36, df_cleaned['cond_away_PAA'])
+                df_cleaned['cond_home_PFA'] = np.where((df_cleaned['Home_ATL'] <= 0), 18, df_cleaned['cond_home_PFA'])
+                df_cleaned['cond_home_PAA'] = np.where((df_cleaned['Home_ATL'] <= 0), 36, df_cleaned['cond_home_PAA'])
+
+                df_cleaned['Away_PFA'] = df_cleaned['Away_PFA'].fillna(df_cleaned['cond_away_PFA'])
+                df_cleaned['Away_PAA'] = df_cleaned['Away_PAA'].fillna(df_cleaned['cond_away_PAA'])
+                df_cleaned['Home_PFA'] = df_cleaned['Home_PFA'].fillna(df_cleaned['cond_home_PFA'])
+                df_cleaned['Home_PAA'] = df_cleaned['Home_PAA'].fillna(df_cleaned['cond_home_PAA'])
+
+                df_cleaned['Away_PFA_adj'] = (df_cleaned['Away_PFA'] * .75 + df_cleaned['Home_PAA'] * .25)
+                df_cleaned['Home_PFA_adj'] = (df_cleaned['Home_PFA'] * .75 + df_cleaned['Away_PAA'] * .25)
+                df_cleaned['Away_PFA_cond'] = (df_cleaned['cond_away_PFA'] * .75 + df_cleaned['cond_home_PAA'] * .25)
+                df_cleaned['Home_PFA_cond'] = (df_cleaned['cond_home_PFA'] * .75 + df_cleaned['cond_away_PAA'] * .25)
+
+                df_cleaned['HFA'] = df_cleaned['Home'].map(hfa_dict)
+                df_cleaned['Neutral'] = np.nan
+                df_cleaned['Home Spread'] = ((df_cleaned['Home_ATL'] - df_cleaned['Away_ATL']) + df_cleaned['HFA']) * -1
+                df_cleaned['Win Prob'] = df_cleaned['Home Spread'].map(odds_dict)
+                df_cleaned['Spread Adj'] = np.nan
+                df_cleaned['Final Spread'] = np.nan
+                df_cleaned['Proj Total'] = df_cleaned['Away_PFA_adj'] + df_cleaned['Home_PFA_adj']
+                df_cleaned['Proj Total (adj)'] = np.where(df_cleaned['over_under'] != np.nan, (df_cleaned['over_under'] * .66 + df_cleaned['Proj Total'] * .34), df_cleaned['Proj Total'])
+                df_cleaned['Proj Total (adj)'] = df_cleaned['Proj Total (adj)'].fillna(df_cleaned['Proj Total'])
+                df_cleaned['Total Adj'] = np.nan
+                df_cleaned['Final Total'] = np.nan
+                df_cleaned['Notes'] = np.nan
+
+                export_df_1 = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'conf_game', 'Away_ATL', 'Home_ATL', 'point_spread', 'Home Spread',
+                                        'over_under', 'Proj Total (adj)', 'Day', 'CST', 'Neutral', 'Notes']]
+
+
+                export_df_1.rename(columns={"pff_week": "week", "point_spread": "Vegas Spread", "over_under": "Vegas Total", "Proj Total (adj)": "Proj Total"}, inplace = True)
+                export_df_2 = add_games_df
+                export_df = export_df_1
+                export_df['week'] = pd.to_numeric(export_df['week'], errors='coerce')
+                export_df = export_df.drop_duplicates(subset=['week', 'Away', 'Home'])
+                export_df = export_df.sort_values(by='week', ascending=True)
+
+                export_df['Vegas Spread'] = pd.to_numeric(export_df['Vegas Spread'], errors='coerce')
+                export_df['Vegas Total'] = pd.to_numeric(export_df['Vegas Total'], errors='coerce')
+                export_df['Proj Total'] = pd.to_numeric(export_df['Proj Total'], errors='coerce')
+                export_df['Home Spread'] = pd.to_numeric(export_df['Home Spread'], errors='coerce')
+                export_df.replace([np.nan, np.inf, -np.inf], '', inplace=True)
+                export_df = export_df.drop_duplicates(subset=['week', 'away_id', 'home_id'])
+
+                sh = gc.open_by_url(NCAAF_model_url)
+                worksheet = sh.worksheet('Master_sched')
+                worksheet.batch_clear(['A:P'])
+                worksheet.update([export_df.columns.values.tolist()] + export_df.values.tolist())
+
+                sheet_list = ['W0', 'W1', 'W2', 'W3', 'W4', 'W5', 'W6', 'W7', 'W8', 'W9', 'W10', 'W11', 'W12', 'W13', 'W14']
+                # sheet_list = ['W0']
+                counter = 0
+
+                for sheet_name in sheet_list:
+                    export_cull = export_df[export_df['week'] == str(counter)]
+                    sh = gc.open_by_url(NCAAF_model_url)
+                    worksheet = sh.worksheet(sheet_name)
+                    worksheet.batch_clear(['A:P'])
+                    worksheet.update([export_cull.columns.values.tolist()] + export_cull.values.tolist())
+                    
+                    counter += 1
+                    
+                    time.sleep(3.76)
+                
+                st.write("Finished NCAAF script!")