"""P0 shadow rollout: run the LLM intent classifier beside rule routing, log only.

Observation-only — the LLM result never influences routing. Disagreements are
logged as `chat_intent_shadow` events to build the P1 golden dataset.
"""

from __future__ import annotations

import dataclasses
import logging
from typing import Any

from .chat_intent_classifier import (
    ChatIntentClassifier,
    LLMChatIntentClassifier,
    RuleBasedChatIntentClassifier,
)
from .chat_intents import CHAT_INTENT_REGISTRY_VERSION
from .llm_services import OpenAICompatibleStructuredClient, log_ai_event
from .settings import Settings


class ChatIntentShadowLogger:
    """Compares LLM vs rule classification per message. Never used for routing."""

    def __init__(self, primary: ChatIntentClassifier, rules: ChatIntentClassifier) -> None:
        self.primary = primary
        self.rules = rules

    def observe(self, message: str, *, state_summary: dict[str, Any]) -> None:
        rule_result = self.rules.classify(message, state_summary=state_summary)
        try:
            llm_result = self.primary.classify(message, state_summary=state_summary)
        except Exception as exc:
            log_ai_event(
                "chat_intent_shadow_error",
                level=logging.WARNING,
                exception=type(exc).__name__,
                trace_id=getattr(exc, "trace_id", None),
            )
            return
        log_ai_event(
            "chat_intent_shadow",
            registry_version=CHAT_INTENT_REGISTRY_VERSION,
            rule_intent=rule_result.intent.value,
            llm_intent=llm_result.intent.value,
            llm_confidence=llm_result.confidence,
            disagreement=llm_result.intent != rule_result.intent,
        )


def build_chat_intent_shadow(settings: Settings, client: Any | None = None) -> ChatIntentShadowLogger | None:
    """Shadow only when explicitly enabled *and* an LLM provider is configured."""
    if settings.intent_router_mode != "shadow" or not settings.use_openai:
        return None
    llm_settings = settings
    if settings.intent_model:
        llm_settings = dataclasses.replace(settings, openai_model=settings.intent_model)
    completion = OpenAICompatibleStructuredClient(llm_settings, client)
    return ChatIntentShadowLogger(LLMChatIntentClassifier(completion), RuleBasedChatIntentClassifier())