from __future__ import annotations

import pandas as pd

from subenvs.autodatalab.analytics import clean_orders, monthly_revenue

from ..models import ExpertReport
from .finance_tools import break_even, compute_variance, project_next_quarter

_FINANCE_QUERY = (
    "finance forecasting next quarter projection confidence band "
    "variance plan_value favorable unfavorable break-even unit_margin fixed_cost"
)


class FinanceExpert:
    expert_id = "finance"

    def run(
        self,
        task_name: str,
        question: str,
        raw_df: pd.DataFrame,
        analyst_metrics: dict,
        task_meta: dict,
        focused: bool = False,
        use_rag: bool = False,
    ) -> ExpertReport:
        cleaned = clean_orders(raw_df).df
        monthly = monthly_revenue(cleaned)
        projection = project_next_quarter(monthly)
        variance = compute_variance(float(analyst_metrics.get("total_revenue", 0.0)), float(task_meta.get("plan_value", 1.0)))
        be = break_even(float(task_meta.get("fixed_cost", 10000.0)), float(task_meta.get("unit_margin", 500.0)))
        metrics = {
            **projection,
            **variance,
            **be,
        }
        bullets = [
            f"Next-quarter revenue projection is {metrics['projection_next_quarter']:.2f} with +/- {metrics['confidence_band']:.2f} band.",
            f"Variance versus plan is {metrics['variance_abs']:.2f} ({metrics['variance_pct']:.2f}%).",
            f"Break-even sits at {metrics['break_even_units']:.2f} units.",
        ]
        memory_citations: list[str] = []
        memory_snippets: list[str] = []
        summary = (
            f"Finance projects next quarter at {metrics['projection_next_quarter']:.2f} and "
            f"marks performance as {metrics['variance_flag']} plan."
        )
        if use_rag:
            from memory import get_retriever

            hits = get_retriever().query(_FINANCE_QUERY, k=2)
            memory_citations = [h.as_citation() for h in hits]
            memory_snippets = [h.snippet for h in hits]
            if hits:
                summary = summary + f" Following SOP {hits[0].source.split('#')[0]}."
                bullets.append(
                    f"SOP alignment: {hits[0].source.split('#')[0]} (score {hits[0].score:.2f})."
                )

        return ExpertReport(
            expert_id="finance",
            title="Finance Forecast",
            summary=summary,
            metrics=metrics,
            bullet_points=bullets,
            citations=list(monthly['Month'].astype(str).tail(3)),
            memory_citations=memory_citations,
            memory_snippets=memory_snippets,
        )