initial upload

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+hopsworks[python]
+pandas

ui.py

@@ -0,0 +1,182 @@
+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 = 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
+    )
+
+    # 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: {roi['current_balance']}")
+    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()