from dataclasses import dataclass
from hashlib import sha256

from app.llm.schemas import Classification


@dataclass(frozen=True)
class ProactiveDecision:
    respond: bool
    reason: str
    fingerprint: str


def decide(
    mode: str,
    classification: Classification,
    *,
    is_mention: bool,
    duplicate: bool,
    cooldown: bool,
    threshold: float,
) -> ProactiveDecision:
    fingerprint = sha256(repr(classification).encode()).hexdigest()
    if mode == "off":
        return ProactiveDecision(False, "mode_off", fingerprint)
    if mode == "mentions" and not is_mention:
        return ProactiveDecision(False, "not_mentioned", fingerprint)
    if duplicate or cooldown:
        return ProactiveDecision(False, "anti_spam", fingerprint)
    if not classification.is_question or classification.rhetorical:
        return ProactiveDecision(False, "not_actionable_question", fingerprint)
    if not classification.project_related:
        return ProactiveDecision(False, "not_project_related", fingerprint)
    if classification.asks_new_decision:
        return ProactiveDecision(False, "requires_human_decision", fingerprint)
    if classification.confidence < threshold:
        return ProactiveDecision(False, "low_confidence", fingerprint)
    return ProactiveDecision(True, "confirmed_fact_candidate", fingerprint)