"""Conversation context for a capture.

When a document is written out of a chat, that chat's subject is useful twice:
to find existing documents the user might extend, and as background for section
refinement. Both use a short LLM topic summary of the conversation. All LLM
traffic goes through `llm/client.py`; nothing here logs content — a failure
degrades quietly rather than breaking the capture.
"""

import logging

from pydantic import BaseModel

from app.authoring.prompts import render_topic_summary_prompt
from app.llm.client import chat_json
from app.llm.errors import LLMError
from app.models import Conversation, MessageRole

logger = logging.getLogger("pablan.authoring")

# How many recent turns feed the summary — enough for the subject, bounded so
# a long thread cannot blow up the utility prompt.
MAX_CONTEXT_MESSAGES = 12

# Skip the reasoning model's hidden thinking: this is a short, latency-
# sensitive utility call.
_NO_THINKING = {"chat_template_kwargs": {"enable_thinking": False}}


class _TopicSummary(BaseModel):
    topic: str


def conversation_transcript(conversation: Conversation) -> str:
    """The recent user/assistant turns as a plain transcript."""
    turns = [
        message
        for message in conversation.messages
        if message.role in (MessageRole.user, MessageRole.assistant)
    ][-MAX_CONTEXT_MESSAGES:]
    return "\n".join(
        f"{'User' if message.role == MessageRole.user else 'Assistant'}: "
        f"{message.content}"
        for message in turns
    )


async def summarize_transcript(transcript: str) -> str:
    """A short topic summary of a conversation transcript, or '' if none can
    be made. Failures degrade quietly."""
    if not transcript.strip():
        return ""
    try:
        result = await chat_json(
            render_topic_summary_prompt(transcript),
            _TopicSummary,
            extra_body=_NO_THINKING,
        )
    except LLMError:
        logger.info("topic summary failed", extra={"event": "topic_summary_failed"})
        return ""
    return result.topic.strip()


async def summarize_conversation(conversation: Conversation) -> str:
    """A short topic summary of the conversation, or '' if none can be made."""
    return await summarize_transcript(conversation_transcript(conversation))