"""Task F: alert summarization. Produces the concise, non-exaggerated text
shown in the alert/email/SMS (Phase 8) - short by design, honest about
confidence, no hype language.
"""

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 write concise, factual alert summaries for a competitive intelligence tool. Never "
    "exaggerate. State what changed, cite the evidence type, and note the confidence level "
    "plainly. The title must be under 100 characters and must not use hype words like "
    "'huge', 'massive', or 'game-changing'."
)


class AlertSummary(BaseModel):
    title: str = Field(max_length=100)
    summary: str
    why_it_matters: str


async def summarize_alert(
    llm: LLMProvider,
    *,
    company_name: str,
    change_type: str,
    change_summary: str,
    severity: str,
    confidence: float,
    evidence_snippets: list[str],
) -> AlertSummary:
    evidence = {
        "company_name": company_name,
        "change_type": change_type,
        "change_summary": change_summary,
        "severity": severity,
        "confidence": confidence,
        "evidence_snippets": evidence_snippets[:10],
    }
    user_prompt = build_user_prompt("Write a concise alert summary for this change.", evidence)
    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, AlertSummary)