"""
POST /api/calibrate
字幕校准服务 - 用LLM将WhisperX识别的歌词时间戳与原始歌词对齐

Parameters:
    original_lyrics: 原始歌词（准确文字）
    whisperx_json: WhisperX输出的JSON（时间戳准，文字不准）

Returns:
    JSON with calibrated subtitles (accurate text + precise timestamps)
"""
import json
import os
import sys

CALIBRATE_PROMPT = """你是一个专业的歌词字幕校准专家。

任务：将WhisperX语音识别输出的时间戳与原始歌词文字进行精确对齐。

规则：
1. 保留WhisperX输出的所有时间戳（start/end），这些时间是准确的
2. 将WhisperX识别的文字替换为原始歌词中对应的文字
3. 按段落顺序匹配：WhisperX的第N段对应原歌词的第N段
4. 处理副歌重复：如果WhisperX识别出重复段落，映射到同一歌词段落
5. 忽略纯音乐段落（无歌词的时间段）

输出格式（严格JSON）：
{
    "segments": [
        {
            "text": "校准后的歌词文字",
            "start": 1.234,
            "end": 4.567,
            "chars": [
                {"char": "爱", "start": 1.234, "end": 1.500},
                {"char": "上", "start": 1.500, "end": 1.800}
            ]
        }
    ]
}

原始歌词：
{original_lyrics}

WhisperX输出JSON：
{whisperx_json}

请输出校准后的JSON（不要markdown代码块，直接输出JSON）：
"""

try:
    original_lyrics = params_kw.get('original_lyrics', '')
    whisperx_json = params_kw.get('whisperx_json', '')

    if not original_lyrics or not whisperx_json:
        result = json.dumps({"status": "error", "error": "missing original_lyrics or whisperx_json"}, ensure_ascii=False)
    else:
        # Build the prompt
        prompt = CALIBRATE_PROMPT.format(
            original_lyrics=original_lyrics,
            whisperx_json=whisperx_json
        )

        # Call LLM via Sage llmage API
        import aiohttp
        LLM_API_BASE = os.environ.get('LLM_API_BASE', 'https://token.opencomputing.cn/llmage/v1')
        LLM_API_KEY = os.environ.get('LLM_API_KEY', '')

        if not LLM_API_KEY:
            # Try to get from config
            from ahserver.serverenv import ServerEnv
            env = ServerEnv()
            LLM_API_KEY = getattr(env, 'llm_api_key', '') or ''

        async with aiohttp.ClientSession() as session:
            payload = {
                "model": "qwen3-235b-a22b",
                "catelogid": "t2t",
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.1,
                "max_tokens": 4096
            }
            headers = {
                "Authorization": f"Bearer {LLM_API_KEY}",
                "Content-Type": "application/json"
            }

            async with session.post(f"{LLM_API_BASE}/chat/completions", json=payload, headers=headers, timeout=120) as resp:
                if resp.status == 200:
                    data = await resp.json()
                    content = data.get('choices', [{}])[0].get('message', {}).get('content', '')
                    # Parse the LLM response
                    content = content.strip()
                    if content.startswith('```'):
                        content = content.split('```')[1]
                        if content.startswith('json'):
                            content = content[4:]
                    content = content.strip()
                    try:
                        calibrated = json.loads(content)
                        result = json.dumps({"status": "success", "data": calibrated}, ensure_ascii=False)
                    except json.JSONDecodeError:
                        result = json.dumps({"status": "error", "error": "LLM response not valid JSON", "raw": content[:500]}, ensure_ascii=False)
                else:
                    text = await resp.text()
                    result = json.dumps({"status": "error", "error": f"LLM API returned {resp.status}", "detail": text[:300]}, ensure_ascii=False)

except Exception as e:
    import traceback
    result = json.dumps({"status": "error", "error": str(e), "traceback": traceback.format_exc()}, ensure_ascii=False)