"""Bounded local media analysis. No HTTP, shell interpolation, or model-generated file paths.
Scene boundaries: PySceneDetect. Beats: librosa. ASR: FunASR sentence timestamps,
as used by FunClip. Silence rough-cut uses an independently implemented RMS gate.
"""
import contextlib
import importlib.util
import json
import os
import sys
from pathlib import Path

for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMBA_NUM_THREADS'):
    os.environ[key] = '2'

def capabilities():
    import shutil
    from separate import available
    return {'separation': available(), 'scenes': importlib.util.find_spec('scenedetect') is not None,
            'beats': importlib.util.find_spec('librosa') is not None,
            'asr': importlib.util.find_spec('funasr') is not None and all(all((Path(os.environ.get(k, '/nonexistent'))/f).is_file() for f in ('model.pt','config.yaml')) for k in ('SMART_EDIT_ASR_MODEL','SMART_EDIT_VAD_MODEL','SMART_EDIT_PUNC_MODEL')),
            'ffmpeg': shutil.which('ffmpeg') is not None,
            'ffprobe': shutil.which('ffprobe') is not None}

def active_regions(wave, sr, threshold_db=-35):
    import numpy as np
    hop=max(1,round(sr*.02))
    energy=np.array([np.sqrt(np.mean(wave[i:i+hop]**2)) for i in range(0,len(wave),hop)])
    active=energy > 10 ** (threshold_db/20)
    intervals=[]; start=None
    for i,keep in enumerate(active):
        if keep and start is None:start=i*hop/sr
        if start is not None and (not keep or i==len(active)-1):
            end=(i+1 if keep else i)*hop/sr
            intervals.append([max(0,start-.2),min(len(wave)/sr,end+.2)]);start=None
    merged=[]
    for start,end in intervals:
        if merged and start-merged[-1][1]<.3:merged[-1][1]=end
        else:merged.append([start,end])
    return [[round(a*1e6),round(b*1e6)] for a,b in merged if b-a>=.2]

def analyze(data):
    result={'scenes':[], 'active':[], 'subtitles':[], 'beats':[], 'warnings':[]}
    duration=data.get('duration',0)
    if data.get('video'):
        import cv2
        cv2.setNumThreads(2)
        from scenedetect import detect,AdaptiveDetector
        result['scenes']=[[round(a.get_seconds()*1e6),round(b.get_seconds()*1e6)] for a,b in detect(data['video'],AdaptiveDetector(min_scene_len=15),start_in_scene=True)] or [[0,duration]]
    if data.get('audio'):
        import soundfile as sf
        import numpy as np
        wave,sr=sf.read(data['audio'],dtype='float32')
        if wave.ndim>1:wave=wave.mean(axis=1)
        result['active']=active_regions(wave,sr,data.get('thresholdDb',-35))
        if data.get('beats'):
            import librosa
            tempo,beats=librosa.beat.beat_track(y=wave,sr=sr,units='time')
            result['beats']=[round(float(b)*1e6) for b in beats]
            if len(result['beats'])<3:
                result['beats']=[];result['warnings'].append('未检测到稳定音乐节拍，将保留普通切镜。')
        if data.get('transcribe'):
            if not capabilities()['asr']:raise RuntimeError('本地语音识别尚未配置；请安装 ASR 依赖及三个模型，或关闭自动转写并导入 SRT 字幕。')
            import torch
            from funasr import AutoModel
            torch.set_num_threads(2)
            model=AutoModel(model=os.environ['SMART_EDIT_ASR_MODEL'],vad_model=os.environ['SMART_EDIT_VAD_MODEL'],punc_model=os.environ['SMART_EDIT_PUNC_MODEL'],device='cpu',disable_update=True,trust_remote_code=False)
            outputs=model.generate(input=data['audio'],sentence_timestamp=True,pred_timestamp=True,batch_size_s=30)
            for output in outputs:
                for sentence in output.get('sentence_info',[]):
                    start=sentence.get('start');end=sentence.get('end')
                    if start is None or end is None:
                        times=sentence.get('timestamp') or []
                        if times:start,end=times[0][0],times[-1][1]
                    text=str(sentence.get('text','')).strip()
                    if text and start is not None and end is not None and end>start:
                        bounded_start=max(0,round(start*1000))
                        bounded_end=min(round(len(wave)/sr*1e6),round(end*1000))
                        if bounded_end>bounded_start:
                            result['subtitles'].append({'start':bounded_start,'end':bounded_end,'text':text})
            if not result['subtitles']:result['warnings'].append('语音识别没有返回有效语句，将按画面镜头处理；不会生成虚构字幕。')
    elif data.get('transcribe'):result['warnings'].append('素材没有音轨，跳过语音转写。')
    return result

if __name__=='__main__':
    if sys.argv[1:] == ['--check']:
        print(json.dumps(capabilities()));sys.exit(0)
    request_path,output_path=sys.argv[1:3]
    try:
        data=json.loads(Path(request_path).read_text())
        with contextlib.redirect_stdout(sys.stderr):result=analyze(data)
        Path(output_path).write_text(json.dumps(result,ensure_ascii=False))
    except Exception as error:
        Path(output_path).write_text(json.dumps({'error':str(error)},ensure_ascii=False));sys.exit(1)