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)
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ramseshk
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"""Page 3 — Auction War Room.
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Budget waterfall, value scatter, grid-auction heatmap, budget slider simulator.
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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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import plotly.graph_objects as go
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from dashboard.warehouse import load_players, load_predictions
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from dashboard.viz.components import inject_css, section, insight, role_chip, kpi_card
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from dashboard.viz.charts import budget_waterfall, value_scatter
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from dashboard.viz.template import (
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PITCH_GREEN, GOLD, RED, SKY, BG, CARD_BG, BORDER, TEXT_SECONDARY,
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WHITE, FANTABETO_TEMPLATE, HEATMAP_COLORS, ROLE_COLORS,
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)
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@st.cache_data(ttl=3600)
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def _get_data():
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players = load_players()
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preds = load_predictions()
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return players, preds
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def _compute_auction(players, budget, gk, df, mf, fw):
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"""Greedy knapsack auction solver."""
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quotas = {"P": gk, "D": df, "C": mf, "A": fw}
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filled = {"P": 0, "D": 0, "C": 0, "A": 0}
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remaining = budget
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df = players.copy()
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df["value_ratio"] = df["fv_avg"] / df["qi"].clip(lower=1)
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df["estimated_price"] = df["qi"] * np.clip(np.random.RandomState(42).normal(2.5, 0.8, len(df)), 0.8, 6)
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scored = []
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for _, p in df.iterrows():
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role = p["role"]
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if role not in quotas:
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continue
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scored.append((p["value_ratio"] * p["fv_avg"], p))
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scored.sort(key=lambda x: -x[0])
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selected = []
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for _, p in scored:
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role = p["role"]
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if filled[role] >= quotas[role]:
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continue
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price = p["estimated_price"]
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if price > remaining:
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continue
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selected.append({
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"player": p["player"], "role": role, "team": p["team"],
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"fv_avg": p["fv_avg"], "qi": p["qi"],
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"estimated_price": price,
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"games_season": p.get("games_season", 30),
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})
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remaining -= price
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filled[role] += 1
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total = budget - remaining
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total_fv = sum(s["fv_avg"] for s in selected)
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return selected, total, total_fv, remaining
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def _grid_heatmap(players):
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"""Simplified grid auction heatmap: top players × bid levels."""
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top = players.nlargest(10, "fv_avg")[
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["player", "role", "fv_avg", "qi"]
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].copy()
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bid_multipliers = [1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0]
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matrix = []
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labels = []
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for _, p in top.iterrows():
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row = []
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for mult in bid_multipliers:
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bid = p["qi"] * mult
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surplus = p["fv_avg"] * 3 - bid # rough value
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row.append(max(0, surplus))
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matrix.append(row)
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labels.append(p["player"])
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fig = go.Figure(data=go.Heatmap(
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z=matrix,
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x=[f"{m}x QI" for m in bid_multipliers],
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y=labels,
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colorscale=HEATMAP_COLORS,
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hovertemplate="%{y}<br>Bid: %{x}<br>Surplus: %{z:.0f}<extra></extra>",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE, height=350,
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xaxis=dict(side="top"),
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yaxis=dict(autorange="reversed"),
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)
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return fig
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def run():
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st.set_page_config(page_title="Auction — Fantabeto", page_icon="💰", layout="wide")
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inject_css()
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players, preds = _get_data()
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st.markdown("## 💰 Auction War Room")
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st.caption("Project Al-Cihred — Draft Strategy for 2026/27 Classic Auction")
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# ── Budget controls ──
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c_budget, c_gk, c_def, c_mid, c_fwd = st.columns(5)
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with c_budget:
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budget = st.slider("Budget (cr)", 300, 700, 500, 10)
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with c_gk:
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n_gk = st.number_input("GK", 1, 5, 3)
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with c_def:
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n_def = st.number_input("DEF", 3, 12, 8)
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with c_mid:
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n_mid = st.number_input("MID", 3, 12, 8)
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with c_fwd:
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n_fwd = st.number_input("FWD", 1, 8, 6)
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selected, total_cost, total_fv, remaining = _compute_auction(
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players, budget, n_gk, n_def, n_mid, n_fwd
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)
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# ── KPI Row ──
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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("PLAYERS DRAFTED", str(len(selected)),
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f"{n_gk+n_def+n_mid+n_fwd} target", SKY),
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unsafe_allow_html=True)
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with k2:
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st.markdown(kpi_card("TOTAL SPENT", f"{total_cost:.0f} cr",
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f"{remaining:.0f} cr remaining", PITCH_GREEN),
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unsafe_allow_html=True)
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with k3:
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st.markdown(kpi_card("PROJECTED FV", f"{total_fv:.1f}",
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f"{total_fv / max(total_cost, 1):.2f} cr/FV", GOLD),
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unsafe_allow_html=True)
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with k4:
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st.markdown(kpi_card("AVG PRICE", f"{total_cost / max(len(selected), 1):.0f} cr",
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"per player", SKY),
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unsafe_allow_html=True)
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st.divider()
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# ── Budget Waterfall + Value Scatter ──
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c1, c2 = st.columns([2, 3])
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with c1:
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section("💧 Budget Allocation")
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allocations = {}
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for r in ["P", "D", "C", "A"]:
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allocations[r] = sum(s["estimated_price"] for s in selected if s["role"] == r)
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fig = budget_waterfall(allocations)
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st.plotly_chart(fig, use_container_width=True)
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insight("How your budget maps across roles. Aim for ~15% GK, ~35% DEF, ~30% MID, ~20% FWD.")
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with c2:
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section("📈 Value Scatter")
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fig = value_scatter(players)
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st.plotly_chart(fig, use_container_width=True)
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insight("Top-right: high FV, high price. Bottom-right: value steals. "
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"Bubble size = games played. Dashed lines = cost-per-FV-point isolines.")
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st.divider()
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# ── Target Squad ──
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section("🎯 Recommended Squad")
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if selected:
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squad_df = pd.DataFrame(selected)
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for role in ["P", "D", "C", "A"]:
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rdf = squad_df[squad_df["role"] == role]
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if rdf.empty:
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continue
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role_name = {"P": "Goalkeepers", "D": "Defenders", "C": "Midfielders", "A": "Forwards"}[role]
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st.markdown(f"**{role_name}** {role_chip(role)}")
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for _, p in rdf.iterrows():
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st.markdown(
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f"- **{p['player']}** ({p['team']}) — "
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f"FV: {p['fv_avg']:.2f} | "
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f"Max bid: {p['estimated_price']:.0f} cr | "
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f"Games: {p['games_season']:.0f}",
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)
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st.divider()
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# ── Grid Auction Heatmap ──
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section("🔢 Grid Auction Simulator")
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fig = _grid_heatmap(players)
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st.plotly_chart(fig, use_container_width=True)
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insight("Green = good value at that bid multiplier. Red = overpaying. "
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"Bid at the 'green' multiplier for each player.")
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if __name__ == "__main__":
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run()
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