from __future__ import annotations

from threading import RLock
from typing import Any, Iterator
from llama_cpp import Llama


class OrchestratorAdapter:
    def __init__(self, llm: Llama, system_prompt: str | None = None, lock: RLock | None = None) -> None:
        self._llm = llm
        self._lock = lock or RLock()
        self._system_prompt = system_prompt or (
            "You are an expert orchestrator for a local AI agent system. "
            "Your role is to analyze the user's task, decide whether planning is needed."
        )
        self._temperature = 0.2

    def generate(self, prompt: str, max_tokens: int | None = None) -> str:
        messages = [
            {"role": "system", "content": self._system_prompt},
            {"role": "user", "content": prompt},
        ]
        with self._lock:
            output = self._llm.create_chat_completion(
                messages=messages,
                max_tokens=max_tokens or 512,
                temperature=self._temperature,
            )
        return output["choices"][0]["message"]["content"]

    def stream(self, prompt: str, max_tokens: int | None = None) -> Iterator[str]:
        messages = [
            {"role": "system", "content": self._system_prompt},
            {"role": "user", "content": prompt},
        ]
        with self._lock:
            for chunk in self._llm.create_chat_completion(
                messages=messages,
                max_tokens=max_tokens or 512,
                temperature=self._temperature,
                stream=True,
            ):
                content = chunk["choices"][0].get("delta", {}).get("content")
                if content:
                    yield content