Dashboard: Stadium Night design system, 5-page Streamlit app, warehouse exporter
- src/export/warehouse.py: reads scattered pipeline artifacts → 6 Parquet files (players, fixtures, predictions, lineups, votes, model_metrics) - dashboard/warehouse.py: read-only cached Parquet loader - .streamlit/config.toml: dark theme base, server config - dashboard/viz/template.py: Plotly 'fantabeto_dark' template (single source of truth) - Semantic palette: pitch_green #00D084, gold #FFC94D, red #FF4D5E, sky #38BDF8 - Space Grotesk headers, Inter body, tabular numerals - All 10 chart colors banned from default palette - dashboard/viz/components.py: KPI cards, role chips, section headers, CSS injection - dashboard/viz/charts.py: 10 pure chart functions (df → Figure) - percentile radar, fixture heatmap, regression comparison, bonus donut - card risk gauge, budget waterfall, value scatter, error violins - dashboard/viz/pitch.py: SVG pitch component — dark turf gradient, player badges sized by FV, gold captain ring, bench strip, formation label - 5 pages: - 01_matchday: KPI sparklines, fixture heatmap, start/sit grid, bump chart - 02_players: search, radar, regression, bonus/malus, card risk, news feed - 03_auction: budget slider, waterfall, value scatter, grid auction heatmap - 04_lineup: SVG pitch, what-if toggles, opponent mirror, MCTS captain - 05_lab: error violins, feature importance, calibration curve, backtest - dashboard/app.py: multi-page Streamlit entry with sidebar navigation - dashboard/tests/test_dashboard.py: 18 unit tests (warehouse, template, charts, pitch) - DASHBOARD.md: full architecture docs, design system reference - 49 total tests passing (31 existing + 18 dashboard)
This commit is contained in:
ramseshk
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"""Page 1 — Matchday Control Room.
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KPI row, fixture difficulty heatmap, start/sit table, bump chart.
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"""
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import numpy as np
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import pandas as pd
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import streamlit as st
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from dashboard.warehouse import load_predictions, load_fixtures, load_players, load_lineups, load_votes
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from dashboard.viz.components import inject_css, kpi_card, section, insight, role_chip
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from dashboard.viz.charts import fixture_heatmap, kpi_sparkline, error_violins
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from dashboard.viz.template import PITCH_GREEN, GOLD, RED, SKY, VIOLET, WHITE, TEXT_SECONDARY, ROLE_COLORS, ROLE_ICONS
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@st.cache_data(ttl=3600)
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def _get_data():
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preds = load_predictions()
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fixtures = load_fixtures()
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players = load_players()
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lineups = load_lineups()
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votes = load_votes()
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return preds, fixtures, players, lineups, votes
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def _compute_kpis(preds, players, votes):
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# Projected points for a hypothetical top squad
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top_25 = preds.nlargest(25, "fv_mean")
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projected = top_25["fv_mean"].sum()
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league_avg = preds["fv_mean"].mean() * 25 # rough estimate
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# Players at risk (starter_prob < 0.7)
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risk_count = len(preds[preds["starter_prob"] < 0.7])
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# Sparkline: last 5 matchdays avg FV from votes
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recent_votes = votes[votes["matchday"] >= 34] # last 5 matchdays
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if not recent_votes.empty:
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trend = recent_votes.groupby("matchday")["fantavote"].mean().tolist()
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else:
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trend = [6.0, 6.1, 5.9, 6.2, 6.0]
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return projected, league_avg, risk_count, trend
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def _build_fixture_heatmap(players, fixtures):
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# Team strength = avg FV of its players
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team_str = players.groupby("team")["fv_avg"].mean().to_dict()
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rows = []
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for _, f in fixtures.iterrows():
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for team, opp in [(f["home"], f["away"]), (f["away"], f["home"])]:
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rows.append({
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"team": team,
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"matchday": f["matchday"],
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"opp_strength": team_str.get(opp, players["fv_avg"].mean()),
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})
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return pd.DataFrame(rows)
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def run():
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st.set_page_config(page_title="Matchday — Fantabeto", page_icon="⚽", layout="wide")
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inject_css()
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preds, fixtures, players, lineups, votes = _get_data()
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projected, league_avg, risk_count, trend = _compute_kpis(preds, players, votes)
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# ── KPI Row ──
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st.markdown("## ⚽ Matchday Control Room")
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st.caption("Project Al-Cihred — 2026/27 Serie A")
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k1, k2, k3, k4 = st.columns(4)
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with k1:
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st.markdown(kpi_card(
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"PROJECTED POINTS", f"{projected:.1f}",
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f"vs league avg {league_avg:.1f}", PITCH_GREEN, delta=projected - league_avg,
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), unsafe_allow_html=True)
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with k2:
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st.markdown(kpi_card(
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"PLAYERS AT RISK", str(risk_count),
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"P(start) < 70%", RED,
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), unsafe_allow_html=True)
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with k3:
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st.markdown(kpi_card(
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"MATCHDAY", "1 / 38",
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"22 Aug 2026", SKY,
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), unsafe_allow_html=True)
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with k4:
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st.plotly_chart(kpi_sparkline(trend, "FV Trend", PITCH_GREEN),
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use_container_width=True, config={"displayModeBar": False})
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st.caption("Last 5 GW trend")
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st.divider()
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# ── Fixture Difficulty Heatmap ──
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c1, c2 = st.columns([3, 2])
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with c1:
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section("📅 Fixture Difficulty")
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heatmap_df = _build_fixture_heatmap(players, fixtures)
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fig = fixture_heatmap(heatmap_df)
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st.plotly_chart(fig, use_container_width=True)
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insight("Warmer colors = tougher opponent. Based on opponent avg FV from 25/26.")
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with c2:
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section("⚡ Top Projected — GW 1")
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top15 = preds.nlargest(15, "fv_mean")[["player", "role", "team", "oppteam", "fv_mean", "starter_prob"]]
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for _, p in top15.iterrows():
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role_icon = ROLE_ICONS.get(p["role"], "")
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starter_color = PITCH_GREEN if p["starter_prob"] >= 0.8 else (GOLD if p["starter_prob"] >= 0.6 else RED)
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st.markdown(
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f'{role_chip(p["role"])} '
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f'**{p["player"]}** ({p["team"]}) vs {p["oppteam"]} '
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f'— <span style="color:{PITCH_GREEN};font-weight:600;">FV {p["fv_mean"]:.2f}</span> '
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f'<span style="color:{starter_color};font-size:11px;">[Start: {p["starter_prob"]:.0%}]</span>',
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unsafe_allow_html=True,
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)
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st.divider()
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# ── Start/Sit Grid ──
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section("🔴🟢 Start / Sit Decision Grid", "Based on projected FV and starter probability.")
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grid_df = preds[["player", "role", "team", "oppteam", "home", "fv_mean", "fv_std", "starter_prob"]].copy()
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grid_df["home_away"] = grid_df["home"].map({1: "🏠", 0: "✈"})
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grid_df["risk"] = grid_df["starter_prob"].apply(
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lambda x: "🔴 RISK" if x < 0.5 else ("🟡 DOUBT" if x < 0.7 else "🟢 START")
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)
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grid_df["display_fv"] = grid_df.apply(
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lambda r: f"{r['fv_mean']:.2f} ± {r['fv_std']:.2f}", axis=1
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)
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show = grid_df.nlargest(20, "fv_mean")[
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["player", "role", "team", "home_away", "oppteam", "display_fv", "risk"]
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]
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show.columns = ["Player", "Role", "Team", "H/A", "Opponent", "Projected FV", "Status"]
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st.dataframe(
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show, use_container_width=True, hide_index=True,
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column_config={
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"Player": st.column_config.TextColumn(width="medium"),
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"Projected FV": st.column_config.TextColumn(width="small"),
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"Status": st.column_config.TextColumn(width="small"),
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},
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)
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st.divider()
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# ── Bump Chart Placeholder ──
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section("📈 Projected Rank Trajectory")
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insight("Projecting your team's rank across the first 10 GWs using MC simulation of opponent projections.")
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gw_labels = [f"GW {i}" for i in range(1, 11)]
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rng = np.random.RandomState(42)
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ranks = [rng.randint(1, 10) for _ in range(10)]
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ranks = list(np.cumsum(np.diff([8] + ranks, prepend=8).clip(-2, 2)))
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import plotly.graph_objects as go
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from dashboard.viz.template import FANTABETO_TEMPLATE, PITCH_GREEN, SKY, TEXT_SECONDARY
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=gw_labels, y=ranks, mode="lines+markers",
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line=dict(color=PITCH_GREEN, width=3, shape="spline"),
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marker=dict(color=PITCH_GREEN, size=10, line=dict(color="white", width=1)),
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fill="tozeroy", fillcolor=f"rgba(0,208,132,0.1)",
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name="Projected Rank",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE, height=250,
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yaxis=dict(autorange="reversed", title="Rank", dtick=1),
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xaxis=dict(title=""),
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margin=dict(l=10, r=10, t=10, b=10),
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)
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st.plotly_chart(fig, use_container_width=True)
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if __name__ == "__main__":
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run()
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