init

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README.md

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+---
+title: Football
+emoji: 📊
+colorFrom: gray
+colorTo: green
+sdk: streamlit
+sdk_version: 1.15.2
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pandas as pd
+import streamlit as st
+import json
+from datetime import datetime, timedelta
+from pathlib import Path
+import hopsworks
+
+CACHE_TTL = 60
+
+
+@st.cache(ttl=CACHE_TTL)
+def download_data():
+    project = hopsworks.login()
+    fs = project.get_feature_store()
+
+    try:
+        feature_view = fs.get_feature_view(name="fw_upcoming", version=1)
+    except:
+        fg_football = fs.get_feature_group(name="fg_upcoming", version=2)
+        query = fg_football.select_all()
+        feature_view = fs.create_feature_view(name="fw_upcoming",
+                                              version=1,
+                                              description="Read from upcoming dataset",
+                                              query=query)
+    # feature_view.delete_all_training_datasets()
+    upcoming = feature_view.get_batch_data(
+        start_time=datetime.now(),
+        end_time=datetime.now() + timedelta(days=365),
+    )
+    mr = project.get_model_registry()
+    model = mr.get_best_model(name="metrics_football", metric="date_run", direction="max")
+    model_dir = Path(model.download())
+    print(model_dir)
+    with open(model_dir / "metrics.json") as file:
+        metrics = json.load(file)
+    return metrics, upcoming, datetime.now()
+
+
+metrics, upcoming, data_updated = download_data()
+upcoming = upcoming.copy()
+
+model_trained_date = datetime.fromtimestamp(metrics['date_run'])
+win_percentage = metrics['win_percentage']
+bet_count = metrics['bet_count']
+win_count = metrics['bet_winning']
+loose_count = metrics['bet_loosing']
+backtest_start = datetime.fromtimestamp(metrics['first_date'])
+backtest_end = datetime.fromtimestamp(metrics['last_date'])
+roi = metrics['roi']
+
+st.write("""
+# Football predictions
+## Upcoming matches:
+""")
+
+buy_col = []
+calculated_at_odds = []
+upcoming['date'] = pd.to_datetime(upcoming['date'])
+upcoming['date_str'] = upcoming['date'].dt.strftime('%d/%m/%Y')
+upcoming = upcoming.sort_values(by=["date"])
+upcoming = upcoming.reset_index(drop=True)
+for row in upcoming.itertuples():
+    if row.h_buy:
+        buy_col.append(row.h_team)
+        calculated_at_odds.append(str(round(1 / row.ho_pinnacle, 2)))
+    elif row.a_buy:
+        buy_col.append(row.a_team)
+        calculated_at_odds.append(str(round(1 / row.ao_pinnacle, 2)))
+    elif row.d_buy:
+        buy_col.append("Draw")
+        calculated_at_odds.append(str(round(1 / row.do_pinnacle, 2)))
+    else:
+        buy_col.append("No bet")
+        calculated_at_odds.append("--")
+
+column_rename = {
+    "date_str": "Date",
+    "h_team": "Home Team",
+    "a_team": "Away Team",
+}
+
+table_data = upcoming[column_rename.keys()]
+table_data.columns = map(column_rename.get, table_data.columns)
+table_data["Bet on:"] = buy_col
+table_data["With odds:"] = calculated_at_odds
+table_data = table_data.style.set_properties(subset=["Bet on:"], **{'font-weight': 'bold'})
+
+st.table(table_data)
+
+st.write("""
+## Backtesting results:
+""")
+
+col1, col2, col3, col4 = st.columns(4)
+with col1:
+    st.metric(label="Bets total:", value=bet_count)
+with col2:
+    st.metric(label="Winning bets:", value=win_count)
+with col3:
+    st.metric(label="Loosing bets:", value=loose_count)
+with col4:
+    st.metric(label="ROI:", value=f"{round(roi, 1)}%")
+
+chart_data = pd.DataFrame(
+    metrics['money_chart']['money'],
+    index=[datetime.fromtimestamp(t) for t in metrics['money_chart']['date']],
+    columns=['Return %'])
+
+st.line_chart(chart_data)
+
+st.text(f"Model trained on {model_trained_date.strftime('%d/%m/%Y at %H:%M')}")
+st.text(f"Data downloaded on {data_updated.strftime('%d/%m/%Y at %H:%M')}")