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import gradio as gr
import hopsworks
import json
from datetime import datetime, timedelta
import pandas as pd
import os
import hopsworks
from hsfs.feature_store import FeatureStore
def login(project="ID2223_Project") -> tuple[hopsworks.project.Project, FeatureStore]:
project = hopsworks.login(
api_key_value=os.environ["HOPSWORKS_API_KEY"],
project=project,
)
fs = project.get_feature_store()
return project, fs
def get_hist_roi():
project, fs = login()
# Initial bank balance, wager amount and which league
starting_bank = 0
wager = 1
league = "E0"
# Get feature groups
main_fg = fs.get_feature_group(
name=f"football_{league.lower()}",
version=1,
)
pred_fg = fs.get_feature_group(
name=f"football_{league.lower()}_predictions",
version=1,
)
# Query necessary features
query = pred_fg.select(["datetime", "predictions", "hometeam", "awayteam"]).join(
main_fg.select(["ftour", "avg_gt_2_5", "avg_lt_2_5"]),
on=["datetime", "hometeam", "awayteam"],
)
df: pd.DataFrame = query.read()
df = df.sort_values(["datetime", "hometeam", "awayteam"], ignore_index=True)
# Encode the full time over/under results
df["ftour_encoded"] = df["ftour"].apply(
lambda x: int(x.lower() == "o") if not pd.isna(x) else pd.NA
)
df.dropna(inplace=True)
# Determine the odds based on predictions
df["odds"] = df.apply(
lambda row: row["avg_gt_2_5"] if row["predictions"] == 1 else row["avg_lt_2_5"],
axis=1,
)
# Calculate profit/loss for each game
df["profit"] = df.apply(
lambda row: (
wager * (row["odds"] - 1)
if row["predictions"] == row["ftour_encoded"]
else -wager
),
axis=1,
)
# Calculate cumulative bank balance
df["bank_balance"] = df["profit"].cumsum() + starting_bank
df.drop(columns="odds", inplace=True)
df['date'] = df['datetime'].dt.date
daily_aggregated = df.groupby('date').agg({
'profit': 'sum', # Total profit
'bank_balance': 'last', # Last bank balance of the day
}).reset_index()
daily_aggregated['date'] = daily_aggregated['date'].astype(str)
total_best = len(df)
bets_won = (df['profit'] > 0).sum()
bets_lost = (df['profit'] < 0).sum()
current_balance = df.iloc[-1]['bank_balance']
return {"total_bets": total_best, "bets_won": bets_won, "bets_lost": bets_lost, "current_balance": current_balance, "data": daily_aggregated}
def logout():
hopsworks.logout()
def get_todays_predictions():
project, fs = login()
fg_pred = fs.get_feature_group('football_e0_predictions', version=1)
# Query the latest row from main_fg based
main_fg_query = fg_pred.select(
[
"datetime",
"hometeam",
"awayteam",
"predictions"
]
).filter(fg_pred.datetime >= datetime.today().strftime("%Y-%m-%d"))
main_df = main_fg_query.read(online=False)
main_df["predictions"] = main_df["predictions"].map(lambda x: "Under" if x == 0 else "Over")
return main_df
def get_daily_predictions():
project, fs = login()
fg_pred = fs.get_feature_group('football_e0_predictions', version=1)
league = "E0"
# Get feature groups
main_fg = fs.get_feature_group(
name=f"football_{league.lower()}",
version=1,
)
pred_fg = fs.get_feature_group(
name=f"football_{league.lower()}_predictions",
version=1,
)
# Query necessary features
query = pred_fg.select(["datetime", "predictions", "hometeam", "awayteam"]).join(
main_fg.select(["ftour"]),
on=["datetime", "hometeam", "awayteam"],
)
main_df = query.read()
main_df["predictions"] = main_df["predictions"].map(lambda x: "Under" if x == 0 else "Over")
main_df["ftour"] = main_df["ftour"].map(lambda x: "Under" if x == "U" else "Over")
main_df.rename(columns={"ftour": "Result"}, inplace=True)
return main_df.sort_values(by="datetime", ascending=False).head(10)
def get_schedule():
with open('./schedule.json', 'r') as handle:
parsed = json.load(handle)
return pd.DataFrame([{
"date": datetime.strptime(game['sport_event']['start_time'], "%Y-%m-%dT%H:%M:%S+00:00"),
"home_team": game['sport_event']['competitors'][0]['name'],
"away_team": game['sport_event']['competitors'][1]['name'],
}
for game in parsed["schedules"]
])
def get_next10games(schedule):
return schedule.loc[schedule['date'] > today].sort_values(by='date').head(10)
today = datetime.today()
roi = get_hist_roi()
with gr.Blocks() as demo:
with gr.Row():
gr.Label(f"Total bets: {roi['total_bets']}")
gr.Label(f"Bets won: {roi['bets_won']}")
gr.Label(f"Bets lost: {roi['bets_lost']}")
gr.Label(f"Current balance: {round(roi['current_balance'], 2)}")
gr.LinePlot(roi["data"], x="date", y="bank_balance", title="Bank balance over time", y_title="Bank balance", x_title="Date")
gr.Label("Today's predictions")
gr.DataFrame(get_todays_predictions,
headers=["Date", "Home Team", "Away Team", "Prediction"],
every=7200) # 2hrs
gr.Label("Last 10 games")
gr.DataFrame(get_daily_predictions,
headers=["Date", "Home Team", "Away Team", "Prediction"],
every=7200) # 2hrs
gr.Label("Upcoming games")
next10 = gr.DataFrame(get_next10games(get_schedule()),
label=None,
headers=["Date", "Home Team", "Away Team"],
interactive=False,
every=7200) # 2hrs
demo.launch() |