"""Claude prompt step — call LLM via LiteLLM proxy."""

import logging

logger = logging.getLogger(__name__)


async def execute_claude_prompt(
    config: dict,
    context: dict | None = None,
    llm=None,
    default_model: str = "claude-haiku",
    escalation_model: str = "claude-sonnet",
    **_kwargs,
) -> str:
    """Send a prompt to Claude and return the response.

    Supports vision: if config contains 'image_b64' or trigger context has
    'image_b64', the image is included as a vision content block.
    """
    if not llm:
        raise RuntimeError("LLM client not configured")

    prompt = config.get("prompt", "")
    if not prompt:
        raise ValueError("claude_prompt step requires 'prompt' field")

    model_name = config.get("model", "default")
    model = escalation_model if model_name == "escalation" else default_model

    # Check for image data (from config or trigger context)
    image_b64 = config.get("image_b64", "")
    image_mime = config.get("image_mime", "image/png")
    if not image_b64 and context:
        trigger = context.get("trigger", {})
        image_b64 = trigger.get("image_b64", "")
        image_mime = trigger.get("mime_type", "image/png")

    # Build message content
    if image_b64:
        content = [
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:{image_mime};base64,{image_b64}",
                },
            },
            {
                "type": "text",
                "text": prompt,
            },
        ]
    else:
        content = prompt

    response = await llm.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": content}],
        max_tokens=4096,
    )

    return response.choices[0].message.content or ""