import sys; from pathlib import Path; _p = Path(__file__).resolve().parent.parent.parent; str(_p) not in sys.path and sys.path.insert(0, str(_p))
"""Page 3 — Auction War Room.

Loads the ML-optimized Project Al-Cihred auction plan with alternatives.
"""

import numpy as np
import pandas as pd
import streamlit as st
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from dashboard.warehouse import load_players, load_predictions
from dashboard.viz.components import inject_css, section, insight, role_chip, kpi_card
from dashboard.viz.charts import budget_waterfall, value_scatter
from dashboard.viz.template import (
    PITCH_GREEN, GOLD, RED, SKY, VIOLET, BG, CARD_BG, BORDER, TEXT_SECONDARY,
    WHITE, FANTABETO_TEMPLATE, HEATMAP_COLORS, ROLE_COLORS, ROLE_ICONS,
)

AUCTION_PLAN = Path(__file__).resolve().parent.parent.parent / "data" / "auction_plan_al_cihred.xlsx"
PROJECTIONS = Path(__file__).resolve().parent.parent.parent / "data" / "player_projections_26_27.xlsx"


@st.cache_data(ttl=3600)
def _get_data():
    players = load_players()
    preds = load_predictions()
    # Load the precomputed auction plan
    plan = None
    if AUCTION_PLAN.exists():
        plan = pd.read_excel(AUCTION_PLAN)
    proj = None
    if PROJECTIONS.exists():
        proj = pd.read_excel(PROJECTIONS)
    return players, preds, plan, proj


def _price_dist_chart(proj):
    if proj is None:
        return go.Figure()
    df = proj.copy()
    df["price"] = df["price"].fillna(0)
    fig = go.Figure()
    for role, color in ROLE_COLORS.items():
        rdf = df[df["role"] == role]["price"]
        if rdf.empty:
            continue
        fig.add_trace(go.Histogram(
            x=rdf, name=role, marker_color=color, opacity=0.7, nbinsx=50,
            hovertemplate=f"{role} %{{x:.0f}} cr<extra></extra>",
        ))
    fig.update_layout(
        template=FANTABETO_TEMPLATE, height=300, barmode="overlay",
        xaxis_title="Estimated Auction Price (cr)", yaxis_title="Players", bargap=0.05,
    )
    return fig


def _role_radar(squad):
    """Radar showing squad balance: avg FV, games, stability per role."""
    roles = ["P", "D", "C", "A"]
    avg_fv, avg_g, avg_s = [], [], []
    for r in roles:
        rs = [s for s in squad if s.get("role") == r]
        avg_fv.append(np.mean([s.get("fv_proj", 0) for s in rs]) if rs else 0)
        avg_g.append(np.mean([s.get("games", 0) for s in rs]) if rs else 0)
        avg_s.append(np.mean([s.get("stability", 0) for s in rs]) if rs else 0)

    fig = go.Figure()
    fig.add_trace(go.Scatterpolar(
        r=avg_fv + [avg_fv[0]], theta=roles + [roles[0]],
        fill="toself", fillcolor=f"rgba(56,189,248,0.2)",
        line=dict(color=SKY, width=2), name="Avg FV",
    ))
    fig.add_trace(go.Scatterpolar(
        r=avg_g + [avg_g[0]], theta=roles + [roles[0]],
        fill="toself", fillcolor=f"rgba(0,208,132,0.15)",
        line=dict(color=PITCH_GREEN, width=2), name="Avg Games",
    ))
    fig.update_layout(
        template=FANTABETO_TEMPLATE, height=280,
        polar=dict(
            radialaxis=dict(showticklabels=False, gridcolor=GRIDLINE),
            angularaxis=dict(gridcolor=GRIDLINE),
            bgcolor=BG,
        ),
        showlegend=True, legend=dict(orientation="h", yanchor="bottom", y=-0.2),
        margin=dict(l=30, r=30, t=30, b=60),
    )
    return fig


def run():
    inject_css()
    players, preds, plan, proj = _get_data()

    st.markdown("## 💰 Auction War Room")
    st.caption("Project Al-Cihred — ML-Optimized Auction Strategy for 2026/27")

    # Load plan data into squad list
    squad = []
    total_cost = 0
    total_fv = 0
    if plan is not None and len(plan) > 0:
        for _, p in plan.iterrows():
            s = {
                "player": p.get("player", ""), "role": p.get("role", ""),
                "team": p.get("team", ""), "price": p.get("bid_cap", 0),
                "qi": p.get("qi", 0), "fvm": p.get("fvm", 0),
                "fv_proj": p.get("fv_proj", 0), "mv_proj": p.get("mv_proj", 0),
                "goals": p.get("goals", 0), "assists": p.get("assists", 0),
                "games": p.get("games", 0), "starter_pct": p.get("starter%", 0),
                "stability": p.get("stability", 0),
            }
            s["alts"] = []
            for j in range(1, 3):
                if f"alt{j}" in plan.columns and pd.notna(p.get(f"alt{j}")):
                    s["alts"].append({
                        "player": p.get(f"alt{j}"), "price": p.get(f"alt{j}_price"),
                        "fv": p.get(f"alt{j}_fv"),
                    })
            squad.append(s)
            total_cost += s["price"]
            total_fv += s["fv_proj"]
    else:
        st.warning("⚠️ No auction plan found. Run `python -c '...'` to generate one.")
        return

    # ── KPI Row ──
    k1, k2, k3, k4, k5 = st.columns(5)
    with k1:
        st.markdown(kpi_card("BUDGET USED", f"{total_cost} cr",
                             "500 cr ceiling", PITCH_GREEN if total_cost >= 499 else GOLD),
                    unsafe_allow_html=True)
    with k2:
        st.markdown(kpi_card("PROJ FV", f"{total_fv:.1f}",
                             f"{total_fv/total_cost:.3f} cr/FV", GOLD), unsafe_allow_html=True)
    with k3:
        stable = sum(1 for s in squad if s["games"] >= 15)
        st.markdown(kpi_card("RELIABLE", f"{stable}/25",
                             "players >15 games", PITCH_GREEN), unsafe_allow_html=True)
    with k4:
        avg_g = np.mean([s["games"] for s in squad])
        st.markdown(kpi_card("AVG GAMES", f"{avg_g:.0f}",
                             "25/26 experience", SKY), unsafe_allow_html=True)
    with k5:
        stars = sum(1 for s in squad if s["fvm"] > 100)
        st.markdown(kpi_card("PREMIUM", str(stars),
                             "FVM > 100", GOLD), unsafe_allow_html=True)

    st.divider()

    # ── Squad Table + Radar ──
    c1, c2 = st.columns([3, 2])

    with c1:
        section("🎯 Project Al-Cihred — Final Squad")
        role_groups = {"P": "🧤 Goalkeepers", "D": "🛡 Defenders",
                       "C": "⚙ Midfielders", "A": "⚡ Forwards"}

        for role, label in role_groups.items():
            rs = [s for s in squad if s["role"] == role]
            if not rs:
                continue
            cost = sum(s["price"] for s in rs)
            st.markdown(f"**{label}** ({len(rs)}) — *{cost} cr*", unsafe_allow_html=False)

            for s in rs:
                chip = role_chip(s["role"])
                pc = GOLD if s["price"] >= 30 else (SKY if s["price"] >= 15 else PITCH_GREEN)
                risk_warn = " ⚠️" if s["starter_pct"] < 0.5 else ""
                st.markdown(
                    f'{chip} **{s["player"]}** ({s["team"]}) — '
                    f'FV: {s["fv_proj"]:.2f} · '
                    f'<span style="color:{pc};font-weight:600;">{s["price"]:.0f} cr</span> · '
                    f'{int(s["goals"])}G {int(s["assists"])}A · {int(s["games"])}gms '
                    f'· FVM {int(s["fvm"])}{risk_warn}',
                    unsafe_allow_html=True,
                )

                if s["alts"]:
                    alt_text = " · ".join(
                        f'<span style="color:{TEXT_SECONDARY};font-size:11px;">⟳ [{a["player"]} · {int(a["price"])}cr]</span>'
                        for a in s["alts"]
                    )
                    st.markdown(f'<span style="margin-left:22px;">{alt_text}</span>',
                                unsafe_allow_html=True)

    with c2:
        section("📊 Squad Balance Radar")
        fig = _role_radar(squad)
        st.plotly_chart(fig, width="stretch")
        insight("Balanced squad across roles: defenders with high reliability, forwards with high FV ceiling.")

        # Budget allocations
        allocations = {}
        for r in ["P", "D", "C", "A"]:
            allocations[r] = sum(s["price"] for s in squad if s["role"] == r)
        fig = budget_waterfall(allocations)
        st.plotly_chart(fig, width="stretch")

    st.divider()

    # ── Market Analysis ──
    section("📈 Market Overview")
    c3, c4 = st.columns([1, 1])
    with c3:
        fig = _price_dist_chart(proj)
        st.plotly_chart(fig, width="stretch")
        insight("Price distribution by role. Forwards and elite midfielders at premium.")
    with c4:
        section("🔍 Value Scatter")
        fig = value_scatter(players)
        st.plotly_chart(fig, width="stretch")

    # ── Methodology ──
    st.divider()
    with st.expander("⚙️ Methodology — How this squad was computed"):
        st.markdown("""
        **Model**: LightGBM trained on 11,300 per-matchday votes from 2025/26.
        **Features**: role, vote_avg, fv_avg, goals/game, assists/game, yellow/game, red/game, games_played.
        **Solver**: MILP (Mixed Integer Linear Programming) via PuLP with constraints:
        - Budget: exactly 499–500 crediti
        - At least 2 goalkeepers with >15 games played
        - At least 17/25 players with >15 games (reliability)
        - GK budget: 35–80 cr (prevent 1cr scrubs)
        - Price model: QI × (FVM/65) with forward premium and small-sample penalty
        **Alternatives**: Top 2 nearest players by role + price (±20% range) ranked by FV.
        """, unsafe_allow_html=False)


if __name__ == "__main__":
    run()