"""LLM gateway — the ONLY entry point to the LLM layer (guardrail #1).

Callers pass text that has already gone through `app.core.privacy.scrub()` (`ScrubbedText`). The
gateway adds the shared policy on top of the selected task implementation (GigaChat or offline mock):

* `assert_scrubbed` runtime backstop before anything reaches a provider;
* SPEC §7 confidence threshold for scale items (below it the FSM asks a clarifying question);
* risk classification failures degrade to `RiskLevel.NONE` — it is only an auxiliary signal, the
  lexicon in `app.core.safety` is primary and alerts are decided by rules (ADR-007);
* patient-facing replies pass a forbidden-content filter and fall back to `config/texts` templates.

No method raises on provider failure; every one has a deterministic fallback.
"""

import json
import logging
import re
from collections.abc import Mapping
from functools import lru_cache
from typing import Any

from app.config import Settings, get_settings
from app.core.types import RiskLevel, ScrubbedText
from app.llm.base import LLMTasks
from app.llm.guard import UnscrubbedTextError, assert_scrubbed
from app.llm.mock import MockTasks
from app.llm.schemas import CheckinFacts, ItemScore
from app.llm.texts import render_phrase, render_summary

log = logging.getLogger(__name__)

# SPEC §3/§7: a scale item with confidence below this is treated as "not extracted" -> clarify.
ITEM_CONFIDENCE_THRESHOLD = 0.5
MAX_REPLY_CHARS = 600

_RISK_MAP = {"none": RiskLevel.NONE, "possible": RiskLevel.POSSIBLE, "acute": RiskLevel.ACUTE}

# Output filter backing up the SPEC §7 prompt prohibitions: diagnoses, naming disorders, dose/drug
# advice, confidentiality promises. Patterns target affirmative imperatives, so the safe phrasing
# "не прекращайте приём самостоятельно" from our own templates is not caught.
FORBIDDEN_OUTPUT: tuple[re.Pattern[str], ...] = tuple(
    re.compile(p, re.IGNORECASE)
    for p in (
        r"диагноз",
        r"у (вас|тебя) (\w+ )?(депресси|птср|тревожн\w* расстройств|расстройств|биполяр|шизофрен)",
        r"(это|похоже на|признаки) (\w+ )?(депресси|птср|посттравматическ|расстройств)",
        r"(увеличьте|уменьшите|снизьте|повысьте|удвойте|измените|увеличь|уменьши|снизь) (\w+ )?доз",
        r"(?<!не )отмен(ите|и) ",
        r"(?<!не )(прекратите|перестаньте|бросьте) (\w+ )?(приём|прием|принимать|пить)",
        r"(примите|принимайте|попробуйте|выпейте) (\w+ )?"
        r"(таблетк|препарат|снотворн|антидепрессант|успокоительн)",
        r"никто не узнает",
        r"останется между нами",
        r"полностью конфиденциальн",
        r"(?<!\w)как (ваш )?(психотерапевт|терапевт)",
    )
)


def is_forbidden(text: str) -> bool:
    return any(p.search(text) for p in FORBIDDEN_OUTPUT)


def _dump(data: Mapping[str, Any]) -> str:
    return json.dumps(data, ensure_ascii=False, default=str, sort_keys=True)


class LLMGateway:
    def __init__(self, tasks: LLMTasks) -> None:
        self._tasks = tasks

    @property
    def provider_name(self) -> str:
        return self._tasks.name

    async def extract_item(
        self, scale_item_id: str, item_text: str, max_score: int, answer: ScrubbedText
    ) -> ItemScore | None:
        """Free-text answer -> item score, or `None` if unsure/failed (FSM then asks to clarify)."""
        try:
            safe = assert_scrubbed(answer)
            result = await self._tasks.extract_item(scale_item_id, item_text, max_score, safe)
        except UnscrubbedTextError:
            log.error("llm refused: unscrubbed text task=extract_item")
            return None
        except Exception as exc:
            log.warning("llm task failed task=extract_item error=%s", type(exc).__name__)
            return None
        if result is None or result.score > max_score or result.confidence < ITEM_CONFIDENCE_THRESHOLD:
            return None
        return result

    async def extract_checkin(self, text: ScrubbedText) -> CheckinFacts | None:
        try:
            return await self._tasks.extract_checkin(assert_scrubbed(text))
        except UnscrubbedTextError:
            log.error("llm refused: unscrubbed text task=extract_checkin")
        except Exception as exc:
            log.warning("llm task failed task=extract_checkin error=%s", type(exc).__name__)
        return None

    async def risk_classify(self, text: ScrubbedText) -> RiskLevel:
        """Auxiliary risk signal; the caller combines it as `max(lexicon, llm)`. Failure -> NONE."""
        try:
            verdict = await self._tasks.risk_classify(assert_scrubbed(text))
        except UnscrubbedTextError:
            log.error("llm refused: unscrubbed text task=risk_classify")
            return RiskLevel.NONE
        except Exception as exc:
            log.warning("llm task failed task=risk_classify error=%s", type(exc).__name__)
            return RiskLevel.NONE
        return RiskLevel.NONE if verdict is None else _RISK_MAP[verdict.level]

    async def phrase(self, intent: str, context: Mapping[str, Any] | None = None) -> str:
        """Short empathetic reply for an already-decided action. Never raises; falls back to template.

        `context` must contain only structured, non-identifying values (drug name, day number...).
        """
        context = context or {}
        base = render_phrase(intent, context)
        try:
            payload = assert_scrubbed(_dump({"intent": intent, "context": context, "base": base}))
            text = await self._tasks.phrase(intent, payload, base)
        except UnscrubbedTextError:
            log.error("llm refused: unscrubbed text task=phrase")
            return base
        except Exception as exc:
            log.warning("llm task failed task=phrase error=%s", type(exc).__name__)
            return base
        if not text or len(text) > MAX_REPLY_CHARS or is_forbidden(text):
            if text:
                log.warning("llm reply rejected by output filter intent=%s", intent)
            return base
        return text.strip()

    async def summarize_for_doctor(self, structured: Mapping[str, Any]) -> str:
        """Doctor summary from structured data only (never raw patient text). Falls back to template."""
        base = render_summary(structured)
        try:
            text = await self._tasks.summarize_for_doctor(assert_scrubbed(_dump(structured)), base)
        except UnscrubbedTextError:
            log.error("llm refused: unscrubbed text task=summarize_for_doctor")
            return base
        except Exception as exc:
            log.warning("llm task failed task=summarize_for_doctor error=%s", type(exc).__name__)
            return base
        if not text or is_forbidden(text):
            return base
        return text.strip()


def build_gateway(settings: Settings) -> LLMGateway:
    if settings.llm_provider == "gigachat":
        if not settings.gigachat_credentials:
            # Offline-first (ADR-004): a misconfigured key must not break the demo.
            log.warning("LLM_PROVIDER=gigachat but GIGACHAT_CREDENTIALS is empty; using mock")
            return LLMGateway(MockTasks())
        from app.llm.giga import GigaChatProvider
        from app.llm.tasks import PromptedTasks

        return LLMGateway(PromptedTasks(GigaChatProvider(settings), name="gigachat"))
    return LLMGateway(MockTasks())


@lru_cache
def get_gateway() -> LLMGateway:
    return build_gateway(get_settings())


# Module-level shortcuts, so callers can `from app.llm import gateway; await gateway.extract_item(...)`.
async def extract_item(
    scale_item_id: str, item_text: str, max_score: int, answer: ScrubbedText
) -> ItemScore | None:
    return await get_gateway().extract_item(scale_item_id, item_text, max_score, answer)


async def extract_checkin(text: ScrubbedText) -> CheckinFacts | None:
    return await get_gateway().extract_checkin(text)


async def risk_classify(text: ScrubbedText) -> RiskLevel:
    return await get_gateway().risk_classify(text)


async def phrase(intent: str, context: Mapping[str, Any] | None = None) -> str:
    return await get_gateway().phrase(intent, context)


async def summarize_for_doctor(structured: Mapping[str, Any]) -> str:
    return await get_gateway().summarize_for_doctor(structured)