"""AI clip analysis using Ollama (local LLM)."""

import json
import logging
import re

import httpx

from app.config import settings

logger = logging.getLogger(__name__)

SYSTEM_PROMPT = """You are a viral video clip analyst. Given a video transcript with timestamps, identify the best short clips that would perform well on social media (TikTok, YouTube Shorts, Instagram Reels).

For each clip, provide:
- A catchy title (max 60 chars)
- Start and end timestamps (in seconds)
- Virality score (0-100)
- Category (one of: hook, story, insight, humor, emotional, controversial, educational)
- Brief reasoning for why this clip would go viral

Rules:
- Clips should be {min_dur}-{max_dur} seconds long
- Identify {target} clips, ranked by virality potential
- Clips should start and end at natural sentence boundaries
- Prefer clips with strong hooks in the first 3 seconds
- Look for emotional peaks, surprising statements, quotable moments
- Avoid clips that start mid-sentence or end abruptly

Respond ONLY with valid JSON in this exact format:
{{
  "clips": [
    {{
      "title": "Clip title here",
      "start_time": 12.5,
      "end_time": 45.2,
      "virality_score": 85,
      "category": "hook",
      "reasoning": "Why this clip would perform well"
    }}
  ]
}}"""


async def analyze_transcript(
    transcript: dict,
    video_title: str = "",
    video_duration: float = 0,
) -> list[dict]:
    """Use Ollama to identify the best clips from a transcript.

    Args:
        transcript: dict with 'text', 'words', 'segments' from transcription service
        video_title: original video title for context
        video_duration: total video duration in seconds

    Returns:
        List of clip dicts with title, start_time, end_time, virality_score, category, reasoning
    """
    # Build timestamped transcript for the LLM
    text = transcript.get("text", "")
    segments = transcript.get("segments", [])

    if segments:
        timestamped = "\n".join(
            f"[{_fmt_time(s.get('start', 0))} - {_fmt_time(s.get('end', 0))}] "
            f"{s.get('text', '').strip()}"
            for s in segments
        )
    else:
        # Fall back to plain text with rough time estimates
        timestamped = text

    system = SYSTEM_PROMPT.format(
        min_dur=settings.clip_min_duration,
        max_dur=settings.clip_max_duration,
        target=settings.target_clips,
    )

    user_prompt = f"""Video Title: {video_title}
Video Duration: {_fmt_time(video_duration)}

Transcript:
{timestamped}

Identify the {settings.target_clips} best viral clips from this transcript."""

    logger.info(f"Sending transcript to Ollama ({settings.ollama_model})...")

    async with httpx.AsyncClient(timeout=600.0) as client:
        response = await client.post(
            f"{settings.ollama_url}/api/chat",
            json={
                "model": settings.ollama_model,
                "messages": [
                    {"role": "system", "content": system},
                    {"role": "user", "content": user_prompt},
                ],
                "stream": False,
                "options": {
                    "temperature": 0.3,
                    "num_predict": 4096,
                },
            },
        )
        response.raise_for_status()
        result = response.json()

    content = result.get("message", {}).get("content", "")
    clips = _parse_clips(content, video_duration)

    logger.info(f"AI identified {len(clips)} clips")
    return clips


def _parse_clips(content: str, video_duration: float) -> list[dict]:
    """Parse LLM response into clip list, handling imperfect JSON."""
    # Try to extract JSON from response
    json_match = re.search(r"\{[\s\S]*\}", content)
    if not json_match:
        logger.error(f"No JSON found in LLM response: {content[:200]}")
        return []

    try:
        data = json.loads(json_match.group())
    except json.JSONDecodeError:
        # Try to fix common JSON issues
        fixed = json_match.group()
        fixed = re.sub(r",\s*}", "}", fixed)
        fixed = re.sub(r",\s*]", "]", fixed)
        try:
            data = json.loads(fixed)
        except json.JSONDecodeError:
            logger.error(f"Failed to parse LLM JSON: {content[:200]}")
            return []

    raw_clips = data.get("clips", [])
    clips = []

    for c in raw_clips:
        start = float(c.get("start_time", 0))
        end = float(c.get("end_time", 0))

        # Validate
        if end <= start:
            continue
        if start < 0:
            start = 0
        if end > video_duration and video_duration > 0:
            end = video_duration

        clips.append({
            "title": str(c.get("title", "Untitled"))[:100],
            "start_time": round(start, 2),
            "end_time": round(end, 2),
            "virality_score": max(0, min(100, float(c.get("virality_score", 50)))),
            "category": str(c.get("category", "general")),
            "reasoning": str(c.get("reasoning", "")),
        })

    # Sort by virality score descending
    clips.sort(key=lambda x: x["virality_score"], reverse=True)
    return clips


def _fmt_time(seconds: float) -> str:
    """Format seconds as MM:SS."""
    m, s = divmod(int(seconds), 60)
    return f"{m:02d}:{s:02d}"