app.py

import streamlit as st
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

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": 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"
}

gc = gspread.service_account_from_dict(credentials)

traderater = "https://www.fantasylife.com/api/projections/v1/nfl/ratemytrade/season/update"

ros_james_url = "https://www.fantasylife.com/api/projections/v1/nfl/james/ros/update"

dwain_url = "https://www.fantasylife.com/api/projections/v1/nfl/dwain/season/update"
freedman_url = "https://www.fantasylife.com/api/projections/v1/nfl/freedman/season/update"
agg_url = "https://www.fantasylife.com/api/projections/v1/nfl/aggregate/season/update"

weekly_dwain_url = "https://www.fantasylife.com/api/projections/v1/nfl/dwain/game/update"
weekly_freedman_url = "https://www.fantasylife.com/api/projections/v1/nfl/freedman/game/update"
weekly_agg_url = "https://www.fantasylife.com/api/projections/v1/nfl/aggregate/game/update"

dev_dwain_url = "https://fantasylife.dev.spotlightsportsb2b.com/api/projections/v1/nfl/dwain/season/update"
dev_freedman_url = "https://fantasylife.dev.spotlightsportsb2b.com/api/projections/v1/nfl/freedman/season/update"
dev_agg_url = "https://fantasylife.dev.spotlightsportsb2b.com/api/projections/v1/nfl/aggregate/season/update"

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 = st.secrets['NCAAF_model_url']
pff_url = st.secrets['pff_url']

headers = {
    'Authorization': st.secrets['FL_Authorization'],
}

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():
        col1, col2, col3 = st.columns([3, 3, 3])
        
        with col1:
            st.info("Update Dwain's LIVE SITE FantasyLife Season Long Projections")
            if st.button("Dwain Projection Update (Live Seasonal)", key='reset1'):
                  response = requests.post(dwain_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col2:
            st.info("Update Freedman's LIVE SITE FantasyLife Season Long Projections")
            if st.button("Freedman Projection Update (Live Seasonal)", key='reset2'):
                  response = requests.post(freedman_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col3:
            st.info("Update the Aggregate LIVE SITE FantasyLife Season Long Projections")
            if st.button("Aggregate Projection Update (Live Seasonal)", key='reset3'):
                  response = requests.post(agg_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

with tab2:
    with st.container():
        col1, col2, col3 = st.columns([3, 3, 3])
    
        with col1:
            st.info("Update Dwain's DEV SITE FantasyLife Season Long Projections")
            if st.button("Dwain Projection Update (Dev Seasonal)", key='reset4'):
                  response = requests.post(dev_dwain_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col2:
            st.info("Update Freedman's DEV SITE FantasyLife Season Long Projections")
            if st.button("Freedman Projection Update (Dev Seasonal)", key='reset5'):
                  response = requests.post(dev_freedman_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col3:
            st.info("Update the Aggregate DEV SITE FantasyLife Season Long Projections")
            if st.button("Aggregate Projection Update (Dev Seasonal)", key='reset6'):
                  response = requests.post(dev_agg_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

with tab3:
    with st.container():
        col1, col2, col3 = st.columns([3, 3, 3])
    
        with col1:
            st.info("Update Dwain's FantasyLife Weekly Projections")
            if st.button("Dwain Projection Update (Weekly)", key='reset7'):
                  response = requests.post(weekly_dwain_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col2:
            st.info("Update Freedman's FantasyLife Weekly Projections")
            if st.button("Freedman Projection Update (Weekly)", key='reset8'):
                  response = requests.post(weekly_freedman_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")
        with col3:
            st.info("Update the Aggregate FantasyLife Weekly Projections")
            if st.button("Aggregate Projection Update (Weekly)", key='reset9'):
                  response = requests.post(weekly_agg_url, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

with tab4:
    with st.container():
        col1, col2, col3 = st.columns([3, 3, 3])
    
        with col1:
            st.info("Update Freedman NFL Game Model")
            if st.button("Update Freedman NFL Game Model (Weekly)", key='reset10'):
                  response = requests.post(freedman_nfl_game_model, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

        with col2:
            st.info("Update Thor NCCAF Game Model")
            if st.button("Update Thor NCCAF Game Model (Weekly)", key='reset11'):
                  response = requests.post(thor_ncaaf_game_model, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

with tab5:
    with st.container():
        col1, col2, col3 = st.columns([3, 3, 3])
    
        with col1:
            st.info("Update FantasyLife Trade Rater")
            if st.button("Projection Update (Trade Rater)", key='reset12'):
                  response = requests.post(traderater, headers=headers)
                  if response.status_code == 200:
                      st.write("Uploading!")

with tab6:
    with st.container():
        col1, col2, col3 = st.columns([3, 3, 3])
    
        with col1:
            st.info("Update Rest of Season Projections")
            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!")

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'):

                st.write("Initiated")

                sh = gc.open_by_url(sheet_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('Injuries')
                injuries_df = DataFrame(worksheet.get_all_records())
                injuries_dict = dict(zip(injuries_df.Team, injuries_df.Team_Modifier))
                
                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)
                neutral_dict = dict(zip(add_games_df.game_id, add_games_df.Neutral))
                
                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(url)

                st.write("retreiving PFF data")

                # 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}")
                

                st.write("Cleaning data")
                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_inj'] = df_cleaned['Away'].map(injuries_dict)
                df_cleaned['Home_inj'] = df_cleaned['Home'].map(injuries_dict)
                df_cleaned['Away_inj'] = df_cleaned['Away_inj'].replace(['', np.nan], 0)
                df_cleaned['Home_inj'] = df_cleaned['Home_inj'].replace(['', np.nan], 0)
                df_cleaned['inj_mod'] = df_cleaned['Away_inj'] - df_cleaned['Home_inj']
                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['Neutral'] = df_cleaned['game_id'].map(neutral_dict)
                df_cleaned['HFA'] = np.where(df_cleaned['Neutral'] == 1, 0, 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['inj_mod']
                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(sheet_url)
                worksheet = sh.worksheet('Master_sched')
                worksheet.batch_clear(['A:P'])
                worksheet.update([export_df.columns.values.tolist()] + export_df.values.tolist())
                st.write("Uploaded Master Schedule")
                
                st.write("Finished NCAAF Script!")