"""Task E: change significance narrative.

Severity itself stays deterministic (app/change_detection/scoring.py) - see
ARCHITECTURE.md: "the LLM narrates why a change matters; it does not decide
how much it matters." This task supplies the narrative explanation and a
second, independent opinion on whether the change looks real/meaningful and
notification-worthy, which the notification layer (Phase 8) can use as an
extra signal alongside the deterministic severity - it never overrides it.
"""

from __future__ import annotations

from pydantic import BaseModel, Field

from app.analysis.llm.base import LLMProvider
from app.prompts.base import build_user_prompt

SYSTEM_PROMPT = (
    "You are a competitive intelligence analyst reviewing one detected change between two "
    "snapshots of a company's public information. Explain in plain language why this change "
    "would or wouldn't matter to someone monitoring this company, given their stated focus. "
    "Be honest about uncertainty - do not overstate a routine wording tweak as significant, "
    "and do not undersell a genuine signal."
)


class ChangeSignificanceAssessment(BaseModel):
    is_real_change: bool = Field(description="Does this look like a genuine change, not noise?")
    is_meaningful: bool
    why_it_matters: str
    confidence: float = Field(ge=0.0, le=1.0)
    should_notify: bool


async def assess_change_significance(
    llm: LLMProvider,
    *,
    company_name: str,
    monitoring_focus: str | None,
    change_type: str,
    change_summary: str,
    added_text: list[str],
    removed_text: list[str],
    deterministic_severity: str,
    deterministic_confidence: float,
) -> ChangeSignificanceAssessment:
    evidence = {
        "company_name": company_name,
        "monitoring_focus": monitoring_focus,
        "change_type": change_type,
        "change_summary": change_summary,
        "text_added": added_text[:20],
        "text_removed": removed_text[:20],
        "deterministic_severity": deterministic_severity,
        "deterministic_confidence": deterministic_confidence,
    }
    user_prompt = build_user_prompt(
        "Assess this detected change and explain why it does or doesn't matter.", evidence
    )
    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, ChangeSignificanceAssessment)