"""
app/services/llm_service.py — LLM Client (Ollama)
Version: 0.2.1 (Fix: System Prompt Handling for Phi-3)
"""

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()
        
        self.client = httpx.AsyncClient(
            base_url=self.settings.OLLAMA_URL, 
            timeout=self.settings.LLM_TIMEOUT
        )

    def _load_prompts(self) -> dict:
        path = Path(self.settings.PROMPTS_PATH)
        if not path.exists():
            return {}
        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_raw_response(self, prompt: str, system: str = None) -> str:
        """
        Führt einen LLM Call aus. 
        Unterstützt nun explizite System-Prompts für sauberes Templating.
        """
        payload = {
            "model": self.settings.LLM_MODEL,
            "prompt": prompt,
            "stream": False,
            "options": {
                # Temperature etwas höher für Empathie, niedriger für Code?
                # Wir lassen es auf Standard, oder steuern es später via Config.
                "temperature": 0.7, 
                "num_ctx": 2048
            }
        }

        # WICHTIG: System-Prompt separat übergeben, damit Ollama formatiert
        if system:
            payload["system"] = system

        try:
            response = await self.client.post("/api/generate", json=payload)
            if response.status_code != 200:
                logger.error(f"Ollama Error ({response.status_code}): {response.text}")
                return "Fehler bei der Generierung."
            
            data = response.json()
            return data.get("response", "").strip()
            
        except Exception as e:
            logger.error(f"LLM Raw Gen Error: {e}")
            return "Interner LLM Fehler."

    async def generate_rag_response(self, query: str, context_str: str) -> str:
        """Legacy Support"""
        system_prompt = self.prompts.get("system_prompt", "")
        rag_template = self.prompts.get("rag_template", "{context_str}\n\n{query}")
        final_prompt = rag_template.format(context_str=context_str, query=query)
        
        # Leite an die neue Methode weiter
        return await self.generate_raw_response(final_prompt, system=system_prompt)

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