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 2 — Player Intelligence.

Radar charts, regression analysis, card risk, RAG news feed.
"""

import numpy as np
import pandas as pd
import streamlit as st

from dashboard.warehouse import load_players, load_predictions, load_votes, load_lineups
from dashboard.viz.components import inject_css, section, insight, role_chip
from dashboard.viz.charts import percentile_radar, regression_chart, bonus_donut, card_gauge
from dashboard.viz.template import PITCH_GREEN, GOLD, RED, SKY, ROLE_COLORS, ROLE_ICONS


@st.cache_data(ttl=3600)
def _get_data():
    players = load_players()
    preds = load_predictions()
    votes = load_votes()
    lineups = load_lineups()
    return players, preds, votes, lineups


RADAR_METRICS = [
    "goals_p90", "assists_p90", "xg_p90", "progressive_passes_p90",
    "tackles_p90", "interceptions_p90", "shots_on_target_pct",
]
RADAR_LABELS = ["Goals", "Assists", "xG", "Prog Pass", "Tackles", "Interc.", "SoT%"]


def _get_player_data(players, preds, votes, lineups, player_name):
    prow = players[players["player"] == player_name]
    if prow.empty:
        return None, None, None, None
    p = prow.iloc[0]

    pred_row = preds[preds["player"] == player_name]
    if pred_row.empty:
        pred_row = pd.DataFrame([{"fv_mean": p.get("fv_avg", 6.0)}])

    vote_row = votes[votes["player"] == player_name]
    line_row = lineups[lineups["player"] == player_name]

    p["starter_pct"] = float(line_row["starter_pct"].values[0]) if not line_row.empty else 50
    p["fv_projected"] = float(pred_row["fv_mean"].values[0]) if not pred_row.empty else p.get("fv_avg", 6.0)
    p["games_season"] = p.get("games_season", 0)
    p["goals_season"] = p.get("goals_season", 0)
    p["assists_season"] = p.get("assists_season", 0)
    p["yellow_per_game"] = p.get("yellow_season", 0) / max(p.get("games_season", 1), 1)
    p["red_per_game"] = p.get("red_season", 0) / max(p.get("games_season", 1), 1)

    return p, pred_row, vote_row, line_row


def run():
    inject_css()
    players, preds, votes, lineups = _get_data()

    st.markdown("## 👤 Player Intelligence")
    st.caption("Deep-dive into any Serie A player — radar profiles, regression analysis, and news feed.")

    # Search
    c_search, c_role, c_team = st.columns([3, 1, 1])
    with c_search:
        all_players = sorted(players["player"].dropna().unique())
        selected = st.selectbox("Search player", all_players, key="player_search",
                                placeholder="Type a name...")
    with c_role:
        role_filter = st.selectbox("Role", ["All", "P", "D", "C", "A"], index=0)
    with c_team:
        teams = sorted(players["team"].dropna().unique())
        team_filter = st.selectbox("Team", ["All"] + teams, index=0)

    if not selected:
        st.info("Search for a player above to see their profile.")
        return

    p, pred_row, vote_row, line_row = _get_player_data(players, preds, votes, lineups, selected)
    if p is None:
        st.warning(f"Player '{selected}' not found in database.")
        return

    # ── Player Header ──
    st.divider()
    h1, h2, h3, h4 = st.columns([2, 1, 1, 1])
    with h1:
        role = p.get("role", "?")
        team = p.get("team", "?")
        st.markdown(f"### {ROLE_ICONS.get(role, '')} {selected}")
        st.caption(f"{role_chip(role)}  {team}  ·  FVM: {p.get('fvm', 0)}  ·  QI: {p.get('qi', 0)} cr")
    with h2:
        fv = p.get("fv_avg", 6.0)
        st.metric("Fantavoto Avg", f"{fv:.2f}", delta=None)
    with h3:
        games = p.get("games_season", 0)
        st.metric("Games (25/26)", f"{games:.0f}")
    with h4:
        sp = p.get("starter_pct", 50)
        st.metric("GW1 Start %", f"{sp:.0f}%")

    st.divider()

    # ── Radar + Regression ──
    r1, r2 = st.columns([1, 1])
    with r1:
        section("📊 Percentile Radar")
        role_avg = players[players["role"] == role].mean(numeric_only=True)
        fig = percentile_radar(p, RADAR_METRICS, RADAR_LABELS, role_avg)
        st.plotly_chart(fig, width="stretch")
        insight("Values normalized vs league average for same role. Outer = better.")

    with r2:
        section("🎯 Goals vs Expected")
        pdf = pd.DataFrame([{
            "goals_season": p.get("goals_season", 0),
            "xg_p90": p.get("xg_p90", 0) or (p.get("xg_season", 0) / 38) if "xg_season" in p else 0,
            "games_season": p.get("games_season", 1),
        }])
        fig = regression_chart(pdf, selected)
        st.plotly_chart(fig, width="stretch")
        div = p.get("goals_season", 0) - (p.get("xg_p90", 0) or 0) * p.get("games_season", 1)
        div_label = "overperforming" if div > 1 else ("underperforming" if div < -1 else "on par with")
        insight(f"{selected} is {div_label} xG by {abs(div):.1f} goals.")

    st.divider()

    # ── Bonus/Malus + Card Risk ──
    b1, b2 = st.columns([1, 1])
    with b1:
        section("💰 Bonus / Malus Breakdown")
        goals_26 = p.get("goals_season", 0)
        assists_26 = p.get("assists_season", 0)
        yellow = p.get("yellow_season", 0)
        red_c = p.get("red_season", 0)
        fig = bonus_donut(goals_26, assists_26, yellow * 0.5 + red_c * 1.0)
        st.plotly_chart(fig, width="stretch")
        insight(f"Season totals: {goals_26:.0f}G + {assists_26:.0f}A — {yellow:.0f}🟨 {red_c:.0f}🟥")

    with b2:
        section("⚠️ Card Risk Gauge")
        fig = card_gauge(p.get("yellow_per_game", 0), p.get("red_per_game", 0))
        st.plotly_chart(fig, width="stretch")
        ypg = p.get("yellow_per_game", 0)
        if ypg > 0.2:
            insight(f"⚠️ High yellow risk: {ypg:.2f} per game. Consider rotation in tough fixtures.")
        else:
            insight(f"Low card risk: {ypg:.2f} yellows per game.")

    st.divider()

    # ── RAG News Feed ──
    section("📰 News & Intelligence", "Live updates from Italian sports press (Gazzetta, Sky, Di Marzio).")
    st.info("🔌 RAG news pipeline available when `src/features/news_rag.py` is run. "
            "Shows injury reports, suspensions, tactical shifts, and transfer rumors.")

    # ── Historical votes table ──
    if vote_row is not None and not vote_row.empty:
        st.divider()
        section("📋 Recent Matchday Votes")
        recent = vote_row.nlargest(10, "matchday")[[
            "matchday", "vote", "goals", "fantavote"
        ]].sort_values("matchday", ascending=False)
        recent.columns = ["Matchday", "Vote", "Goals", "Fantavote"]
        st.dataframe(recent, width="stretch", hide_index=True)


if __name__ == "__main__":
    run()