"""LLM-powered article summarization with personalization."""

from __future__ import annotations

import logging

from openai import AsyncOpenAI

logger = logging.getLogger("article-summary.summarizer")

WORDS_PER_MINUTE = 150  # Clear narration pace


async def summarize_article(
    content: str,
    language: str,
    duration_minutes: int,
    topics: list[str],
    llm_client: AsyncOpenAI,
    model: str,
) -> str:
    """Generate a narrative summary of article content.

    Args:
        content: Article text (max ~15K chars).
        language: Target language ("en" or "de").
        duration_minutes: Target audio duration (5, 10, or 15).
        topics: Focus topics selected by user.
        llm_client: AsyncOpenAI instance (LiteLLM).
        model: Model name to use.

    Returns:
        Summary text ready for TTS.
    """
    word_target = duration_minutes * WORDS_PER_MINUTE
    lang_name = "German" if language == "de" else "English"
    topics_str = ", ".join(topics) if topics else "all topics"

    system_prompt = f"""You are a professional audio narrator creating a Blinkist-style summary.

RULES:
- Write in {lang_name}.
- Target approximately {word_target} words (for a {duration_minutes}-minute audio).
- Focus on: {topics_str}.
- Use a conversational, engaging narrator tone — as if explaining to a curious friend.
- Structure: brief intro → key insights → practical takeaways → brief conclusion.
- Use flowing prose, NOT bullet points or lists.
- Do NOT include any formatting markers, headers, or markdown.
- Do NOT say "In this article..." — jump straight into the content.
- Make it sound natural when read aloud."""

    # Truncate very long content
    if len(content) > 12_000:
        content = content[:12_000] + "\n\n[Article continues...]"

    resp = await llm_client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": f"Summarize this article:\n\n{content}"},
        ],
        max_tokens=word_target * 2,  # tokens ≈ 1.3x words, with headroom
        temperature=0.7,
    )

    summary = resp.choices[0].message.content.strip()
    logger.info("Generated summary: %d words (target: %d)", len(summary.split()), word_target)
    return summary