"""
LLM orchestration service with OpenRouter integration, model routing, and fallback.
"""

from __future__ import annotations

import asyncio
import logging
from typing import Any, Dict, Optional

import httpx

from app.core.config import settings
from app.core.cache import cache_service


logger = logging.getLogger(__name__)


class ModelRouter:
    """Selects models based on task type and tenant overrides."""

    DEFAULT_TASK_TO_MODEL: Dict[str, str] = {
        "extraction": "gpt-4o-mini",
        "analysis": "gpt-4o-mini",
        "synthesis": "gpt-4o-mini",
        "vision": "gpt-4-vision-preview",
        "classification": "gpt-4o-mini",
        "general": settings.OPENROUTER_MODEL,
    }

    @staticmethod
    async def choose_model(task: str, tenant_id: str) -> str:
        task_norm = (task or "general").lower()
        # Tenant override lookup
        override_key = f"llm:model:override:{tenant_id}:{task_norm}"
        override = await cache_service.get(override_key, tenant_id)
        if isinstance(override, str) and override:
            return override
        # Default mapping
        return ModelRouter.DEFAULT_TASK_TO_MODEL.get(task_norm, settings.OPENROUTER_MODEL)


class LLMService:
    """OpenRouter-backed LLM service with tenant-aware routing and fallback."""

    def __init__(self):
        self.base_url = settings.OPENROUTER_BASE_URL.rstrip("/")
        self.default_model = settings.OPENROUTER_MODEL
        self.fallback_model = settings.OPENROUTER_FALLBACK_MODEL

    def _headers(self) -> Dict[str, str]:
        return {
            "Authorization": f"Bearer {self.api_key}" if self.api_key else "",
            "Content-Type": "application/json",
            "HTTP-Referer": "https://virtual-board-member.local",
            "X-Title": "Virtual Board Member AI",
        }

    async def _post_chat(self, payload: Dict[str, Any]) -> Dict[str, Any]:
        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.post(
                f"{self.base_url}/chat/completions", json=payload, headers=self._headers()
            )
            response.raise_for_status()
            return response.json()

    def _api_key(self) -> Optional[str]:
        return getattr(settings, "OPENROUTER_API_KEY", None)

    async def generate_text(
        self,
        prompt: str,
        *,
        tenant_id: str,
        task: str = "general",
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        system_prompt: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Generate text with routing and fallback.

        Returns a dict with keys: text, model, usage, raw
        """
        api_key = self._api_key()
        if (
            settings.MOCK_LLM_RESPONSES
            or not api_key
            or (isinstance(api_key, str) and api_key.strip() in ["", "your-openrouter-api-key"])
        ):
            # Operate in offline mode for environments without OpenRouter keys
            fake_text = (
                "[LLM unavailable] This environment lacks OPENROUTER_API_KEY. "
                "Returning deterministic offline response."
            )
            return {"text": fake_text, "model": "offline", "usage": {}, "raw": {}}

        chosen_model = await ModelRouter.choose_model(task, tenant_id)
        payload = {
            "model": chosen_model,
            "messages": self._build_messages(system_prompt, prompt),
            "max_tokens": max_tokens or settings.OPENROUTER_MAX_TOKENS,
            "temperature": temperature if temperature is not None else settings.OPENROUTER_TEMPERATURE,
        }

        try:
            data = await self._post_chat(payload)
            text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
            return {
                "text": text,
                "model": chosen_model,
                "usage": data.get("usage", {}),
                "raw": data,
            }
        except Exception as primary_error:
            logger.warning("Primary model failed, attempting fallback: %s", primary_error)
            # Fallback
            fallback_model = self.fallback_model
            try:
                payload["model"] = fallback_model
                data = await self._post_chat(payload)
                text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
                return {
                    "text": text,
                    "model": fallback_model,
                    "usage": data.get("usage", {}),
                    "raw": data,
                }
            except Exception as fallback_error:
                logger.error("Fallback model also failed: %s", fallback_error)
                raise

    @staticmethod
    def _build_messages(system_prompt: Optional[str], user_prompt: str) -> Any:
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": user_prompt})
        return messages


# Global instance
llm_service = LLMService()