"""Narrative Dashboard — 3×3 grid telling the AI bubble story

FLAGSHIP chart: single figure combining all evidence streams
into a cohesive visual narrative.
"""
import sys
import os
# Ensure the project root is on sys.path so `src.*` imports work
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

import matplotlib
matplotlib.use("Agg")

# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
# Known bug: https://github.com/matplotlib/matplotlib/issues/29280
try:
    from matplotlib.path import Path
    _original_path_deepcopy = Path.__deepcopy__

    def _safe_path_deepcopy(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        memo[id(self)] = self
        return self

    Path.__deepcopy__ = _safe_path_deepcopy
except Exception:
    pass

import matplotlib.pyplot as plt
import numpy as np
from src.data.market_bubbles import shiller_cape, buffett_indicator, sp500_pe
from src.data.ai_infrastructure import hyperscaler_capex_annual, nvidia_revenue
from src.data.agent_adoption import agent_survey_data, developer_ai_adoption
from src.utils.styling import (
    get_theme, EXPORT_DPI, BUBBLE_ZONE, WARNING_ZONE, NORMAL_ZONE,
    GRAY_DARK, GRAY_MEDIUM, BLACK, WHITE,
    AGENT_GROWTH, REVENUE, DEBT, AI_SPEND, PRODUCTIVITY,
    get_company_colors,
)


def plot_narrative_dashboard() -> str:
    """Generate the flagship 3×3 narrative dashboard.

    Returns the output file path.
    """
    plt.rcParams.update(get_theme())

    fig, axes = plt.subplots(3, 3, figsize=(20, 16))
    fig.set_facecolor(WHITE)

    # ------------------------------------------------------------------
    # ROW 1: Market Bubble Evidence
    # ------------------------------------------------------------------

    # Panel (0,0): Shiller CAPE
    ax = axes[0, 0]
    years = [d["year"] for d in shiller_cape]
    values = [d["value"] for d in shiller_cape]
    ax.axhspan(0, 20, alpha=0.15, color=NORMAL_ZONE)
    ax.axhspan(20, 30, alpha=0.15, color=WARNING_ZONE)
    ax.axhspan(30, 50, alpha=0.15, color=BUBBLE_ZONE)
    ax.plot(years, values, color=GRAY_DARK, linewidth=0.8)
    ax.axhline(y=17.39, color="#333", linestyle="--", linewidth=0.6)
    ax.text(2024, 18.5, "mean: 17.4", fontsize=7, color=GRAY_MEDIUM)
    ax.annotate(
        f"{values[-1]:.1f}",
        xy=(2026, values[-1]), fontsize=7, fontweight="bold",
        color=BUBBLE_ZONE, xytext=(2023, values[-1] - 5),
        arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
    )
    ax.set_title("Shiller CAPE (1880–2026)", fontsize=11, fontweight="bold")
    ax.set_ylabel("CAPE")
    ax.set_ylim(0, 50)
    ax.tick_params(labelsize=7)
    ax.grid(True, alpha=0.2)

    # Panel (0,1): Buffett Indicator
    ax = axes[0, 1]
    b_years = [d["year"] for d in buffett_indicator]
    b_vals = [d["value"] for d in buffett_indicator]
    ax.axhspan(0, 100, alpha=0.15, color=NORMAL_ZONE)
    ax.axhspan(100, 200, alpha=0.15, color=WARNING_ZONE)
    ax.axhspan(200, 300, alpha=0.15, color=BUBBLE_ZONE)
    ax.plot(b_years, b_vals, color=GRAY_DARK, linewidth=0.8)
    ax.axhline(y=200, color=BUBBLE_ZONE, linestyle="--", linewidth=1)
    ax.text(2000, 205, "Danger: 200%", fontsize=7, color=BUBBLE_ZONE)
    ax.annotate(
        f"{b_vals[-1]:.0f}%",
        xy=(2026, b_vals[-1]), fontsize=7, fontweight="bold",
        color=BUBBLE_ZONE, xytext=(2020, b_vals[-1] + 10),
        arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
    )
    ax.set_title("Buffett Indicator (1975–2026)", fontsize=11, fontweight="bold")
    ax.set_ylabel("Mkt Cap / GDP %")
    ax.tick_params(labelsize=7)
    ax.grid(True, alpha=0.2)

    # Panel (0,2): S&P 500 P/E
    ax = axes[0, 2]
    pe_years = [d["year"] for d in sp500_pe]
    pe_vals = [d["value"] for d in sp500_pe]
    ax.plot(pe_years, pe_vals, color=GRAY_DARK, linewidth=0.8)
    ax.axhline(y=17.9, color="#333", linestyle="--", linewidth=0.6)
    ax.text(2020, 19, "mean: 17.9", fontsize=7, color=GRAY_MEDIUM)
    ax.annotate(
        f"{pe_vals[-1]:.1f}",
        xy=(2026, pe_vals[-1]), fontsize=7, fontweight="bold",
        color=WARNING_ZONE, xytext=(2023, pe_vals[-1] - 3),
        arrowprops=dict(arrowstyle="->", color=WARNING_ZONE, lw=0.6),
    )
    ax.set_title("S&P 500 P/E (1950–2026)", fontsize=11, fontweight="bold")
    ax.set_ylabel("P/E")
    ax.set_ylim(0, 75)
    ax.tick_params(labelsize=7)
    ax.grid(True, alpha=0.2)

    # ------------------------------------------------------------------
    # ROW 2: AI Infrastructure Buildout
    # ------------------------------------------------------------------

    # Panel (1,0): Hyperscaler Capex (stacked area, 2020–2026)
    ax = axes[1, 0]
    company_colors = get_company_colors()
    companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
    years_annual = list(range(2020, 2027))

    data = {c: [0.0] * 7 for c in companies}
    for entry in hyperscaler_capex_annual:
        idx = entry["year"] - 2020
        if 0 <= idx < 7:
            data[entry["company"]][idx] = entry["capex_billions"]

    y_off = np.zeros(7)
    for c in companies:
        vals = np.array(data[c], dtype=float)
        ax.fill_between(
            years_annual, y_off, y_off + vals,
            alpha=0.7, color=company_colors[c], label=c,
        )
        y_off += vals

    ax.set_title("Hyperscaler Capex (2020–2026)", fontsize=11, fontweight="bold")
    ax.set_ylabel("Capex $B")
    ax.tick_params(labelsize=7)
    ax.legend(loc="upper left", fontsize=6, framealpha=0.8)
    ax.grid(True, alpha=0.2, axis="y")

    # Panel (1,1): Tech Debt Spike
    ax = axes[1, 1]
    debt_years = [2020, 2021, 2022, 2023, 2024, 2025, 2026]
    debt_vals = [25, 30, 28, 25, 30, 121, 125]
    colors_debt = [GRAY_DARK] * 5 + [BUBBLE_ZONE, WARNING_ZONE]
    bars = ax.bar(debt_years, debt_vals, color=colors_debt, width=0.5)
    avg5 = np.mean(debt_vals[:5])
    ax.axhline(y=avg5, color="#333", linestyle="--", linewidth=1)
    ax.text(2022, avg5 + 3, f"pre-2025 avg: ${avg5:.0f}B",
            fontsize=7, color=GRAY_MEDIUM)
    ax.text(2025.5, 125 + 5, "4× spike!", fontsize=8,
            fontweight="bold", color=BUBBLE_ZONE, ha="right")
    ax.set_title("Tech Debt: 2025 4× Spike", fontsize=11, fontweight="bold")
    ax.set_ylabel("Debt $B")
    ax.set_ylim(0, 150)
    ax.tick_params(labelsize=7)

    # Panel (1,2): NVIDIA Data Center Revenue
    ax = axes[1, 2]
    dc_rev = [d.get("data_center_billions",
              d.get("compute_billions", 0) + d.get("networking_billions", 0))
              for d in nvidia_revenue]
    quarters = list(range(len(dc_rev)))
    ax.fill_between(quarters, dc_rev, alpha=0.25, color=REVENUE)
    ax.plot(quarters, dc_rev, color=REVENUE, linewidth=1)

    # Mark the inflection and latest
    nvidia_quarters_labels = [d["fiscal_quarter"] for d in nvidia_revenue]
    # Highlight 2026-Q4 (index 27)
    latest_idx = len(dc_rev) - 2  # before FY2027-Q1
    ax.plot(latest_idx, dc_rev[latest_idx], "o", color=REVENUE,
            markersize=5)
    ax.annotate(
        f"${dc_rev[latest_idx]:.1f}B",
        xy=(latest_idx, dc_rev[latest_idx]),
        xytext=(latest_idx - 3, dc_rev[latest_idx] - 8),
        fontsize=7, fontweight="bold", color=REVENUE,
        arrowprops=dict(arrowstyle="->", color=REVENUE, lw=0.5),
    )
    ax.set_title("NVIDIA DC Revenue (Quarterly)", fontsize=11, fontweight="bold")
    ax.set_ylabel("Revenue $B")
    ax.tick_params(labelsize=7)
    ax.set_xticks(range(0, len(quarters), 4))
    ax.set_xticklabels([nvidia_quarters_labels[i].replace("FY", "")
                        for i in range(0, len(quarters), 4)],
                       rotation=45, ha="right")
    ax.grid(True, alpha=0.2)

    # ------------------------------------------------------------------
    # ROW 3: Agent Revolution and Reality
    # ------------------------------------------------------------------

    # Panel (2,0): GPU Utilization Paradox
    ax = axes[2, 0]
    cats = ["AI Spend", "GPU Util.", "Target", "Human"]
    vals = [100, 5, 65, 85]
    colors_util = [AI_SPEND, BUBBLE_ZONE, NORMAL_ZONE, WARNING_ZONE]
    bars = ax.barh(cats, vals, color=colors_util, height=0.5)
    for bar, v in zip(bars, vals):
        ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
                f"{v}%", va="center", fontsize=8, fontweight="bold",
                color=BLACK)
    ax.set_title("GPU Utilization Paradox", fontsize=11, fontweight="bold")
    ax.set_xlim(0, 115)
    ax.tick_params(labelsize=8)
    ax.grid(True, alpha=0.2, axis="x")

    # Panel (2,1): Developer AI Reality
    ax = axes[2, 1]
    dev_cats = ["Use AI tools", "Daily AI use", "AI code merged", "AI code has vulns"]
    dev_vals = [84, 51, 22, 48]
    dev_colors = [AGENT_GROWTH, AGENT_GROWTH, NORMAL_ZONE, BUBBLE_ZONE]
    bars = ax.barh(dev_cats, dev_vals, color=dev_colors, height=0.5)
    for bar, v in zip(bars, dev_vals):
        ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
                f"{v}%", va="center", fontsize=8, fontweight="bold",
                color=BLACK)
    ax.set_title("Developer AI Reality", fontsize=11, fontweight="bold")
    ax.set_xlim(0, 100)
    ax.tick_params(labelsize=8)
    ax.grid(True, alpha=0.2, axis="x")

    # Panel (2,2): Enterprise Agent Adoption
    ax = axes[2, 2]
    surveys = ["LangChain", "McKinsey", "PwC"]
    labels = ["In production", "Scaling agents", "Measurable value"]
    adoption = [
        agent_survey_data["langchain_2025"]["production"],
        agent_survey_data["mckinsey_2025"]["agentic_ai_scaling"],
        agent_survey_data["pwc_2025"]["measurable_productivity_value"],
    ]
    bars = ax.barh(surveys, adoption, color=AGENT_GROWTH, height=0.5)
    for bar, v, label in zip(bars, adoption, labels):
        ax.text(v + 1, bar.get_y() + bar.get_height() / 2,
                f"{v:.0f}%  ({label})", va="center", fontsize=7,
                fontweight="bold", color=BLACK)
    ax.set_title("Enterprise Agent Adoption", fontsize=11, fontweight="bold")
    ax.set_xlim(0, 100)
    ax.tick_params(labelsize=8)
    ax.grid(True, alpha=0.2, axis="x")

    # ------------------------------------------------------------------
    # Row labels (vertical text on the left)
    # ------------------------------------------------------------------
    fig.text(0.02, 0.78, "MARKET BUBBLE EVIDENCE", fontsize=10,
             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
             va="center")
    fig.text(0.02, 0.50, "AI INFRASTRUCTURE BUILDOUT", fontsize=10,
             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
             va="center")
    fig.text(0.02, 0.16, "AGENT REVOLUTION & REALITY", fontsize=10,
             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
             va="center")

    # ------------------------------------------------------------------
    # Overall title
    # ------------------------------------------------------------------
    fig.suptitle(
        "The AI Bubble and the Fundamental Value of LLMs — June 2026",
        fontsize=20, fontweight="bold", y=0.98,
    )

    fig.subplots_adjust(
        hspace=0.35, wspace=0.25,
        left=0.06, right=0.98,
        top=0.95, bottom=0.04,
    )

    out_path = "output/combined/narrative_dashboard.png"
    plt.rcParams['savefig.bbox'] = None  # Disable tight cropping for full 20x16 output
    fig.savefig(
        out_path, dpi=EXPORT_DPI,
        facecolor=fig.get_facecolor(), edgecolor="none",
    )
    plt.close(fig)
    return out_path


def main():
    path = plot_narrative_dashboard()
    print(f"Dashboard saved: {path}")


if __name__ == "__main__":
    main()