"""LLM bridge for pipeline handlers.

Provides a simple async interface for handlers to call LLM APIs.
Uses harnessed_agent's llm_chat under the hood when available,
falls back to direct HTTP calls.
"""

import json
import logging
import os

logger = logging.getLogger("pipeline.llm_bridge")


async def llm_call(prompt: str, model: str = None, temperature: float = 0.7) -> str:
    """Call LLM and return text response.
    
    Tries multiple backends:
    1. harnessed_agent.llm_chat (if loaded in ServerEnv)
    2. Direct OpenAI-compatible API call
    """
    # Try harnessed_agent first
    try:
        from ahserver.serverenv import ServerEnv
        env = ServerEnv()
        if hasattr(env, 'llm_chat'):
            result = await env.llm_chat(prompt, model=model, temperature=temperature)
            if isinstance(result, dict):
                return result.get("content", result.get("text", str(result)))
            return str(result)
    except Exception:
        pass

    # Fallback: direct HTTP call to OpenAI-compatible endpoint
    import aiohttp
    
    api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
    api_key = os.environ.get("LLM_API_KEY", "")
    model = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
    
    if not api_key:
        raise ValueError("No LLM API configured (set LLM_API_KEY env var)")

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }
    payload = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": temperature,
    }

    async with aiohttp.ClientSession() as session:
        async with session.post(
            f"{api_base}/chat/completions", headers=headers, json=payload, timeout=aiohttp.ClientTimeout(total=120)
        ) as resp:
            if resp.status != 200:
                text = await resp.text()
                raise ValueError(f"LLM API error {resp.status}: {text[:200]}")
            data = await resp.json()
            return data["choices"][0]["message"]["content"]