"""
app/services/llm_service.py — LLM Client (Ollama)

Version:
    0.1.2 (WP-05 Fix: Increased Timeout for CPU Inference)
"""

import httpx
import yaml
import logging
import os
from pathlib import Path
from app.config import get_settings

logger = logging.getLogger(__name__)

class LLMService:
    def __init__(self):
        self.settings = get_settings()
        self.prompts = self._load_prompts()
        # FIX: Timeout auf 120 Sekunden erhöht für CPU-Only Server
        self.client = httpx.AsyncClient(base_url=self.settings.OLLAMA_URL, timeout=120.0)

    def _load_prompts(self) -> dict:
        """Lädt Prompts aus der konfigurierten YAML-Datei."""
        path = Path(self.settings.PROMPTS_PATH)
        if not path.exists():
            logger.warning(f"Prompt config not found at {path}, using defaults.")
            return {
                "system_prompt": "You are a helpful AI assistant.",
                "rag_template": "Context: {context_str}\nQuestion: {query}"
            }
        
        try:
            with open(path, "r", encoding="utf-8") as f:
                return yaml.safe_load(f)
        except Exception as e:
            logger.error(f"Failed to load prompts: {e}")
            return {}

    async def generate_rag_response(self, query: str, context_str: str) -> str:
        """
        Generiert eine Antwort basierend auf Query und Kontext.
        """
        system_prompt = self.prompts.get("system_prompt", "")
        template = self.prompts.get("rag_template", "{context_str}\n\n{query}")
        
        # Template füllen
        final_prompt = template.format(context_str=context_str, query=query)
        
        payload = {
            "model": self.settings.LLM_MODEL,
            "system": system_prompt,
            "prompt": final_prompt,
            "stream": False,
            "options": {
                "temperature": 0.7,
                # Kleinerer Context spart Rechenzeit, falls 4096 zu viel ist
                "num_ctx": 2048 
            }
        }

        try:
            response = await self.client.post("/api/generate", json=payload)
            
            if response.status_code != 200:
                error_msg = response.text
                logger.error(f"Ollama API Error ({response.status_code}): {error_msg}")
                return f"Fehler vom LLM (Modell '{self.settings.LLM_MODEL}' vorhanden?): {error_msg}"

            data = response.json()
            return data.get("response", "")
            
        except httpx.ReadTimeout:
            return "Timeout: Das Modell braucht zu lange zum Antworten (>120s). Hardware-Limit erreicht?"
        except httpx.ConnectError:
            return "Verbindungsfehler: Ist Ollama gestartet (Port 11434)?"
        except Exception as e:
            logger.error(f"LLM Service Exception: {e}")
            return f"Interner Fehler: {str(e)}"

    async def close(self):
        await self.client.aclose()