#!/usr/bin/env python3
"""
llm.py — LLM interaction layer for The Chaos engine.

Provides the low-level call_llm function and environment variable setup
for provider-specific auth.
"""

from __future__ import annotations

import os

from .state import append_llm_log


def set_llm_env(model: str, api_key: str | None, api_base: str | None) -> None:
    """Set provider-specific env vars for litellm."""
    prefix = model.split("/")[0].upper()
    key = api_key or "sk-placeholder"
    os.environ[f"{prefix}_API_KEY"] = key
    if api_base:
        os.environ[f"{prefix}_API_BASE"] = api_base


def call_llm(
    messages: list[dict],
    *,
    model: str | None = None,
    temperature: float | None = None,
    timeout: int | None = None,
    max_tokens: int | None = None,
    label: str = "",
) -> str | None:
    """Make a single LLM call. Loads config automatically. Returns content text or None on error."""
    from .config import load_config
    cfg = load_config().get("llm", {})
    model = model or cfg.get("model", "ollama/llama3.1")
    temperature = temperature if temperature is not None else cfg.get("temperature", 0.8)
    timeout = timeout if timeout is not None else cfg.get("timeout", 120)
    max_tokens = max_tokens if max_tokens is not None else cfg.get("max_tokens", 4096)
    api_key = cfg.get("api_key")
    api_base = cfg.get("api_base")
    set_llm_env(model, api_key, api_base)
    try:
        import litellm
    except ImportError:
        return None
    try:
        response = litellm.completion(
            model=model,
            messages=messages,
            temperature=temperature,
            stream=False,
            timeout=timeout,
            max_tokens=max_tokens,
        )
        content = getattr(response.choices[0].message, 'content', None) or ""
        reasoning = getattr(response.choices[0].message, 'reasoning_content', None) or ""
        if reasoning and reasoning not in content:
            append_llm_log(f"\n--- {label} [reasoning] ---\n{reasoning}")
        text = content or reasoning
        append_llm_log(f"\n--- {label} ---\n{text}")
        return text
    except Exception as e:
        err_msg = f"{type(e).__name__}: {e}"
        append_llm_log(f"\n--- LLM ERROR ({label}) ---\n{err_msg}")
        return None