reels-app/scripts/analyze.py

#!/usr/bin/env python3
"""
analyze.py — Predhodna analiza CELEGA videa pred trim-anjem.

Naredi:
1. Whisper transcript celega videa (auto-detect jezika ali user-specified)
2. Energy profile (RMS dB na 1s windows)
3. Structural detection (vocal/instrumental sections, energy peaks)
4. Pametno izbere clip range (lahko >30s, vključi pre-chorus)
5. Detekcija instrumentalnih pesmi (no_subs auto)

Output: JSON s podatki za clip.py
"""

import argparse
import json
import os
import re
import subprocess
import sys
import tempfile
from pathlib import Path


def get_video_duration(path):
    r = subprocess.run(
        ["ffprobe", "-v", "error", "-show_entries", "format=duration",
         "-of", "default=nw=1:nokey=1", str(path)],
        capture_output=True, text=True
    )
    try:
        return float(r.stdout.strip())
    except ValueError:
        return 0.0


def extract_audio(video_path):
    """Extract avdio v 16kHz mono WAV za Whisper + energy."""
    audio = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
    audio.close()
    subprocess.run(
        ["ffmpeg", "-y", "-i", str(video_path), "-vn",
         "-ac", "1", "-ar", "16000", "-c:a", "pcm_s16le", audio.name],
        check=True, capture_output=True
    )
    return audio.name


def detect_language_from_filename(filename_hint):
    """Detektiraj jezik iz imena datoteke na podlagi znanih izvajalcev/besed.
    
    Vrne ISO 639-1 ('sl', 'de', 'en', 'hr'...) ali None.
    """
    if not filename_hint:
        return None
    
    name = filename_hint.lower()
    
    # Slovenski izvajalci (narodno-zabavna, pop, rock)
    SLO_ARTISTS = [
        "avseniki", "avsenik", "modrijani", "veseli dolenjci",
        "čuki", "atomik harmonik", "alfi nipič", "helena blagne",
        "siddharta", "magnifico", "vlado kreslin", "zaklonišče prepeva",
        "perpetuum jazzile", "tabu", "natalija verboten", "klavdija",
        "iztok mlakar", "rok'n'band", "okrog cele zemlje", "ansambel",
        "miran rudan", "andrej šifrer", "mi2", "elvis jackson",
        "tanja žagar", "manca špik", "saša lendero", "rebeka dremelj",
        "nuša derenda", "alenka godec", "prifarski muzikanti",
        "nova generacija", "polka", "narodno-zabavna",
    ]
    SLO_KEYWORDS = ["pazi", "morju", "zveza", "domovina", "ljubim", "srce", "majhna", 
                    "prav", "nazaj", "noč", "dom", "pomoč", "bolha", "preko"]
    
    # Nemški izvajalci (Schlager, Volksmusik)
    DE_ARTISTS = [
        "ben zucker", "andrea berg", "helene fischer", "andreas gabalier",
        "amigos", "kastelruther spatzen", "florian silbereisen", "voxxclub",
        "wolfgang petry", "mickie krause", "die toten hosen", "rammstein",
        "udo lindenberg", "die ärzte", "westernhagen", "peter maffay",
        "matthias reim", "die zillertaler", "die jungen zillertaler",
        "stefan mross", "marianne", "michael wendler", "vincent gross",
        "schlager", "volksmusik",
    ]
    DE_KEYWORDS = ["liebe", "herz", "ohne", "dich", "leben", "nacht", "tag",
                   "schön", "mädchen", "sonne", "himmel", "wenn", "nur",
                   "bist", "hast", "dass", "weiß", "kann", "auch"]
    
    # Hrvaški/srbski izvajalci
    HR_ARTISTS = [
        "thompson", "miroslav škoro", "oliver dragojević", "gibonni",
        "severina", "tony cetinski", "psihomodo pop", "prljavo kazalište",
        "parni valjak", "lepa brena", "ceca", "aca lukas", "mile kitić",
        "halid bešlić", "dino merlin", "zdravko čolić", "magazin",
    ]
    HR_KEYWORDS = ["volim", "ljubav", "srce", "danas", "noćas", "more",
                   "majka", "domovina", "zauvijek", "samo", "ćemo"]
    
    # Angleški izvajalci (preveč jih je za listo, raje preverim ne-SL/DE/HR znake)
    EN_KEYWORDS = ["love", "song", "feat", "remix", "official", "music", "video",
                   "remastered", "lyrics", "by", "with", "tonight", "forever",
                   "heart", "dance", "party", "summer"]
    
    score = {"sl": 0, "de": 0, "hr": 0, "en": 0, "it": 0, "es": 0, "fr": 0}
    
    # Artist matches (težji)
    for a in SLO_ARTISTS:
        if a in name:
            score["sl"] += 5
    for a in DE_ARTISTS:
        if a in name:
            score["de"] += 5
    for a in HR_ARTISTS:
        if a in name:
            score["hr"] += 5
    
    # Keyword matches
    for kw in SLO_KEYWORDS:
        if kw in name.split() or f" {kw} " in f" {name} ":
            score["sl"] += 1
    for kw in DE_KEYWORDS:
        if kw in name.split() or f" {kw} " in f" {name} ":
            score["de"] += 1
    for kw in HR_KEYWORDS:
        if kw in name.split() or f" {kw} " in f" {name} ":
            score["hr"] += 1
    for kw in EN_KEYWORDS:
        if kw in name.split() or f" {kw} " in f" {name} ":
            score["en"] += 1
    
    # Slovenska abeceda (č, ž, š) brez đ (ki je hrvaška)
    if any(c in name for c in "čžš") and "đ" not in name:
        score["sl"] += 2
    # Nemška abeceda (ä ö ü ß)
    if any(c in name for c in "äöüß"):
        score["de"] += 2
    # Hrvaška abeceda (đ)
    if "đ" in name:
        score["hr"] += 2
    
    if not any(score.values()):
        return None
    
    best = max(score.items(), key=lambda x: x[1])
    if best[1] >= 2:  # threshold
        return best[0]
    return None


def transcribe_with_elevenlabs(audio_path, lang=None, model="scribe_v1", filename_hint=None):
    """ElevenLabs Scribe transkripcija (najboljša multilingual accuracy 2026).
    
    lang: ISO 639-1 ('de', 'sl', 'hr') — če None, probamo iz filename_hint
    Pricing: ~$0.40/h (~$0.022 per 200s pesem).
    """
    import urllib.request
    import urllib.error
    import uuid

    api_key = os.environ.get("ELEVENLABS_API_KEY")
    if not api_key:
        print("   ⚠️ ELEVENLABS_API_KEY ni nastavljen", file=sys.stderr)
        return None

    # Auto-detect lang from filename če uporabnik ni eksplicitno izbral
    if not lang and filename_hint:
        guessed = detect_language_from_filename(filename_hint)
        if guessed:
            lang = guessed
            print(f"   🔍 Lang iz filename '{filename_hint}': {lang}", file=sys.stderr)

    # ISO 639-1 → 639-3 mapping (Scribe uses 639-3)
    LANG_1_TO_3 = {
        "en": "eng", "de": "deu", "sl": "slv", "hr": "hrv", "bs": "bos",
        "sr": "srp", "it": "ita", "es": "spa", "fr": "fra", "pt": "por",
        "ru": "rus", "pl": "pol", "cs": "ces", "sk": "slk", "hu": "hun",
        "ro": "ron", "nl": "nld", "sv": "swe", "no": "nor", "da": "dan",
        "fi": "fin", "tr": "tur", "ar": "ara", "uk": "ukr", "bg": "bul",
        "el": "ell", "he": "heb", "ja": "jpn", "ko": "kor", "zh": "zho",
    }
    LANG_3_TO_1 = {v: k for k, v in LANG_1_TO_3.items()}

    # Multipart upload
    boundary = uuid.uuid4().hex
    parts = []

    def add_text(name, value):
        parts.append(
            f"--{boundary}\r\nContent-Disposition: form-data; "
            f"name=\"{name}\"\r\n\r\n{value}\r\n".encode()
        )

    def add_file(name, filename, content, ctype):
        parts.append(
            f"--{boundary}\r\nContent-Disposition: form-data; "
            f"name=\"{name}\"; filename=\"{filename}\"\r\n"
            f"Content-Type: {ctype}\r\n\r\n".encode() + content + b"\r\n"
        )

    with open(audio_path, "rb") as f:
        audio_content = f.read()

    # Limit: ElevenLabs Scribe supports up to ~25 MB / 4.5h per request
    if len(audio_content) > 24 * 1024 * 1024:
        print(f"   ⚠️ Audio {len(audio_content)/1024/1024:.1f} MB > 24 MB limit, fallback", file=sys.stderr)
        return None

    add_text("model_id", model)
    add_text("timestamps_granularity", "word")
    # tag_audio_events=true je kritično: brez tega Scribe predčasno preneha s transkripcijo
    # ko zazna instrumentalni del (npr. polka harmonika prevzame). Z true vstavi oznake
    # kot "(glasba)" in nadaljuje transkripcijo do konca audia.
    # Te oznake potem post-processing odstrani iz besedila.
    add_text("tag_audio_events", "true")
    if lang:
        scribe_lang = LANG_1_TO_3.get(lang, lang)
        add_text("language_code", scribe_lang)
    add_file("file", "audio.mp3", audio_content, "audio/mpeg")
    parts.append(f"--{boundary}--\r\n".encode())
    body = b"".join(parts)

    print(f"   📡 ElevenLabs Scribe ({model}, {len(audio_content)/1024/1024:.1f} MB, "
          f"lang={lang or 'auto'})...", file=sys.stderr)

    req = urllib.request.Request(
        "https://api.elevenlabs.io/v1/speech-to-text",
        data=body,
        headers={
            "xi-api-key": api_key,
            "Content-Type": f"multipart/form-data; boundary={boundary}",
        },
    )

    try:
        with urllib.request.urlopen(req, timeout=300) as resp:
            data = json.loads(resp.read().decode())
    except urllib.error.HTTPError as e:
        body_err = e.read().decode("utf-8", errors="replace")[:500]
        print(f"   ❌ Scribe HTTP {e.code}: {body_err}", file=sys.stderr)
        return None
    except Exception as e:
        print(f"   ❌ Scribe exception: {e}", file=sys.stderr)
        return None

    # Convert response to our standard format
    detected_lang_3 = data.get("language_code", "unknown")
    detected_lang_1 = LANG_3_TO_1.get(detected_lang_3, detected_lang_3[:2])
    detected_prob = data.get("language_probability", 1.0)

    # Scribe returns flat list of words (not segments)
    # We group words into pseudo-segments using **smart phrase-aware segmentation**:
    # - Close on long pause (>= 0.4s) — natural breath/phrase boundary
    # - OR after sentence-ending punctuation (. ! ?)
    # - OR after 4 seconds (max segment length for readable subtitle)
    # This gives ~3-7 word segments matching natural sung phrases.
    words = data.get("words", [])
    segments = []

    if words:
        # Filter out:
        # 1. whitespace tokens
        # 2. audio event tags type='audio_event' or text in (parenthesis) like "(glasba)", "(music)"
        real_words = []
        for w in words:
            t = w.get("text", "").strip()
            wtype = w.get("type", "word")
            # Skip non-word events
            if wtype != "word":
                continue
            if not t:
                continue
            # Skip parenthesized audio events (legacy fallback)
            if t.startswith("(") and t.endswith(")"):
                continue
            real_words.append(w)
        
        if real_words:
            current_seg_words = []
            seg_start = real_words[0].get("start", 0)
            
            for i, w in enumerate(real_words):
                current_seg_words.append(w)
                w_end = w.get("end", w.get("start", 0))
                w_text = w.get("text", "")
                
                close = False
                # Decide if we should close the segment
                if i + 1 < len(real_words):
                    next_start = real_words[i + 1].get("start", w_end)
                    pause = next_start - w_end
                    seg_duration = w_end - seg_start
                    
                    # Trigger close on:
                    # 1. Long pause (>= 0.4s) = phrase boundary
                    # 2. Sentence-ending punctuation
                    # 3. Segment is long enough (>= 4s)
                    if pause >= 0.4:
                        close = True
                    elif seg_duration >= 4.0 and pause >= 0.15:
                        close = True
                    elif w_text.rstrip().endswith(('.', '!', '?')) and pause >= 0.2:
                        close = True
                    elif seg_duration >= 5.5:  # hard cap
                        close = True
                else:
                    close = True  # last word
                
                if close:
                    seg_text = " ".join(ww.get("text", "") for ww in current_seg_words).strip()
                    if seg_text:
                        segments.append({
                            "start": seg_start,
                            "end": w_end,
                            "text": seg_text,
                            "words": [
                                {
                                    "start": ww.get("start", 0),
                                    "end": ww.get("end", 0),
                                    "text": ww.get("text", ""),
                                }
                                for ww in current_seg_words
                            ],
                        })
                    # Reset
                    current_seg_words = []
                    if i + 1 < len(real_words):
                        seg_start = real_words[i + 1].get("start", 0)

    print(f"   ✅ Scribe: {len(words)} words → {len(segments)} segments, "
          f"lang={detected_lang_1} (p={detected_prob:.2f})", file=sys.stderr)

    return {
        "language": detected_lang_1,
        "language_probability": float(detected_prob),
        "segments": segments,
        "_provider": "elevenlabs",
    }


def transcribe_full(audio_path, lang=None, model_size="small", provider="auto", filename_hint=None):
    """Whisper/Scribe transcript dispatcher.
    
    provider:
      - "elevenlabs" → ElevenLabs Scribe (najboljša kvaliteta, $0.40/h, ~10s na 200s pesem)
      - "local"      → faster-whisper na CPU (brezplačno, počasi, halucinacije)
      - "auto"       → Scribe če ELEVENLABS_API_KEY obstaja, sicer local
    
    filename_hint: ime datoteke (uporablja za auto-detect jezika če lang=None)
    """
    if provider in ("elevenlabs", "auto") and os.environ.get("ELEVENLABS_API_KEY"):
        result = transcribe_with_elevenlabs(audio_path, lang=lang, filename_hint=filename_hint)
        if result and result.get("segments"):
            return result
        if provider == "elevenlabs":
            print(f"   ⚠️ Scribe failed, no fallback (provider=elevenlabs)", file=sys.stderr)
            return {"language": "unknown", "language_probability": 0.0, "segments": []}
        print(f"   🔄 Scribe failed, fallback na local Whisper...", file=sys.stderr)

    # Local faster-whisper
    return _transcribe_full_local(audio_path, lang=lang, model_size=model_size)


def _transcribe_full_local(audio_path, lang=None, model_size="small"):
    """Whisper transcript celega avdia. lang=None → robust auto-detect.
    
    Vrne empty transcript če Whisper ne najde govora (popolnoma instrumental)."""
    from faster_whisper import WhisperModel

    print(f"🧠 Whisper LOCAL {model_size}, lang={lang or 'auto'}", file=sys.stderr)
    m = WhisperModel(model_size, device="cpu", compute_type="int8")

    # Auto-detect z 3-sample voting da se zaklenemo na en jezik
    if not lang:
        print("   🔍 Robust lang detection (3 samples)...", file=sys.stderr)
        try:
            duration_proc = subprocess.run(
                ["ffprobe", "-v", "error", "-show_entries", "format=duration",
                 "-of", "default=nw=1:nokey=1", audio_path],
                capture_output=True, text=True
            )
            audio_duration = float(duration_proc.stdout.strip())
        except Exception:
            audio_duration = 180.0

        lang_votes = {}
        for ss in [max(15, audio_duration * 0.15), audio_duration * 0.45, audio_duration * 0.75]:
            if ss + 5 > audio_duration:
                continue
            sample = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
            sample.close()
            try:
                subprocess.run(
                    ["ffmpeg", "-y", "-ss", str(ss), "-i", audio_path,
                     "-t", "30", "-vn", "-ac", "1", "-ar", "16000",
                     "-c:a", "pcm_s16le", sample.name],
                    check=True, capture_output=True
                )
                _, sample_info = m.transcribe(sample.name, language=None, vad_filter=False)
                sl, sp = sample_info.language, float(sample_info.language_probability)
                lang_votes[sl] = lang_votes.get(sl, 0) + sp
                print(f"      sample @ {ss:.0f}s: {sl} (p={sp:.2f})", file=sys.stderr)
            except Exception as e:
                print(f"      sample @ {ss:.0f}s: failed", file=sys.stderr)
            finally:
                try:
                    os.unlink(sample.name)
                except Exception:
                    pass

        if lang_votes:
            lang = max(lang_votes.items(), key=lambda x: x[1])[0]
            print(f"   ✅ Lang lock: {lang}", file=sys.stderr)

    try:
        segs, info = m.transcribe(
            audio_path,
            language=lang,
            word_timestamps=True,
            # VAD filter kdaj izpusti vokal med glasbo — pri pesmi bolje brez
            vad_filter=False,
            # Anti-halucinacije
            condition_on_previous_text=False,
            temperature=0.0,
            compression_ratio_threshold=2.4,
            log_prob_threshold=-1.0,
            no_speech_threshold=0.6,
            # Beam search namesto greedy = bolj zanesljiv decode (manj halucinacij)
            beam_size=5,
            # Halucinacija detection: če je tišina dolga, ne pretvarjaj v tekst
            hallucination_silence_threshold=2.0,
        )
        detected_lang = info.language
        detected_prob = float(info.language_probability)
    except (ValueError, RuntimeError) as e:
        # Whisper failure (např. pri popolnoma instrumentalnih datotekah z VAD)
        print(f"   ⚠️ Whisper transcribe failed: {e}", file=sys.stderr)
        return {
            "language": "unknown",
            "language_probability": 0.0,
            "segments": [],
        }

    print(f"   Detekcija: {detected_lang} (p={detected_prob:.2f})", file=sys.stderr)

    segments = []
    for s in segs:
        words = []
        if s.words:
            for w in s.words:
                words.append({
                    "start": w.start,
                    "end": w.end,
                    "text": w.word,
                })
        segments.append({
            "start": s.start,
            "end": s.end,
            "text": s.text.strip(),
            "words": words,
        })

    return {
        "language": detected_lang,
        "language_probability": detected_prob,
        "segments": segments,
    }


def compute_energy_profile(audio_path, window_sec=1.0):
    """RMS dB na window_sec sekund. Vrne list (timestamp, rms_db)."""
    cmd = [
        "ffmpeg", "-i", audio_path,
        "-af", f"asetnsamples=n={int(16000 * window_sec)}:p=0,"
               f"astats=metadata=1:reset={window_sec},"
               f"ametadata=print:key=lavfi.astats.Overall.RMS_level:file=-",
        "-f", "null", "-",
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)
    output = result.stdout + "\n" + result.stderr

    energies = []
    current_pts = 0.0
    for line in output.split("\n"):
        line = line.strip()
        m = re.search(r"pts_time:(\S+)", line)
        if m:
            try:
                current_pts = float(m.group(1))
            except ValueError:
                pass
            continue
        if "RMS_level=" in line:
            val = line.split("RMS_level=")[-1].strip()
            try:
                rms = float(val)
                # -inf zamenjamo z -90
                if rms < -90 or rms != rms:  # NaN check
                    rms = -90.0
                energies.append((current_pts, rms))
                current_pts += window_sec
            except ValueError:
                pass

    return energies


def detect_vocal_sections(segments, max_gap=3.0):
    """Združi consecutive segmente v "vokalne sekcije"."""
    if not segments:
        return []
    sections = []
    current = {
        "start": segments[0]["start"],
        "end": segments[0]["end"],
        "segments": [segments[0]],
        "text": segments[0]["text"],
    }
    for seg in segments[1:]:
        if seg["start"] - current["end"] > max_gap:
            sections.append(current)
            current = {
                "start": seg["start"],
                "end": seg["end"],
                "segments": [seg],
                "text": seg["text"],
            }
        else:
            current["end"] = seg["end"]
            current["segments"].append(seg)
            current["text"] += " " + seg["text"]
    sections.append(current)
    return sections


def avg_energy_in_range(energies, start, end):
    """Povprečna RMS v rangeu."""
    vals = [r for (t, r) in energies if start <= t <= end]
    if not vals:
        return -90.0
    return sum(vals) / len(vals)


def score_section_as_chorus(section, all_sections, energies, avg_rms):
    """Score sekcijo kot kandidat za refren.

    Faktorji:
    - Ponavljajoče besede (low unique-word-ratio) = refren
    - Visoka energija
    - Sekcija se pojavi večkrat v pesmi (refren se ponovi)
    - Krajše vrstice (3-8 besed)
    """
    text = section["text"].lower()
    words = re.findall(r"\b\w+\b", text)
    if not words:
        return 0

    unique_ratio = len(set(words)) / len(words)
    # Refren = nizko unique ratio (ponovitve)
    chorus_signal = max(0, (1.0 - unique_ratio) * 30)

    # Energija
    sec_energy = avg_energy_in_range(energies, section["start"], section["end"])
    energy_above = max(0, sec_energy - avg_rms)
    energy_score = energy_above * 8

    # Kako pogosto se pojavi podobno besedilo
    repeat_count = 0
    for other in all_sections:
        if other is section:
            continue
        other_text = other["text"].lower()
        other_words = set(re.findall(r"\b\w+\b", other_text))
        common = set(words) & other_words
        # Če imata >50% besed skupnih, je verjetno isti refren
        if len(common) >= len(set(words)) * 0.5 and len(common) >= 3:
            repeat_count += 1
    repeat_score = repeat_count * 25

    # Dolžina vrstice
    duration = section["end"] - section["start"]
    if 3 <= duration <= 25:
        length_score = 10
    elif duration > 25:
        length_score = 5
    else:
        length_score = 2

    return chorus_signal + energy_score + repeat_score + length_score


def find_chorus(transcript, energies, video_duration):
    """Najde najbolj verjeten refren."""
    sections = detect_vocal_sections(transcript["segments"])
    if not sections:
        return None

    avg_rms = sum(r for (_, r) in energies) / len(energies) if energies else -30.0

    candidates = []
    for sec in sections:
        score = score_section_as_chorus(sec, sections, energies, avg_rms)
        candidates.append({
            "start": sec["start"],
            "end": sec["end"],
            "duration": sec["end"] - sec["start"],
            "text_preview": sec["text"][:80],
            "score": round(score, 2),
            "avg_rms": round(avg_energy_in_range(energies, sec["start"], sec["end"]), 2),
        })

    # Sort by score descending
    candidates.sort(key=lambda c: -c["score"])

    if not candidates:
        return None

    return {
        "best": candidates[0],
        "all_candidates": candidates[:10],
        "avg_rms_total": round(avg_rms, 2),
    }


def smart_clip_range(chorus, transcript, video_duration,
                      target_duration=30, max_duration=45, min_duration=20,
                      include_prebuild=False):
    """Inteligentno določi clip range.

    Logika:
    1. Začni z refrenom kot core
    2. Če je krajši od min_duration → razširi z drugim refrenom (ne kitico!)
    3. Cap na max_duration

    include_prebuild=False (default): NE doda kitice/verza pred refrenom.
    include_prebuild=True: doda kratek pre-chorus (max 8s, gap < 3s).
    """
    if not chorus or not chorus.get("best"):
        # Fallback: vzemi sredino videa
        mid = video_duration / 2
        start = max(0, mid - target_duration / 2)
        return {
            "start": start,
            "end": min(video_duration, start + target_duration),
            "reason": "fallback_middle",
        }

    best = chorus["best"]
    sections = detect_vocal_sections(transcript["segments"])

    actual_start = best["start"]
    actual_end = best["end"]

    # Najdi VSE sekcije ki so podobne refrenu (verjetne ponovitve)
    chorus_words = set(re.findall(r"\b\w+\b", best["text_preview"].lower()))
    chorus_sections = []
    for sec in sections:
        sec_words = set(re.findall(r"\b\w+\b", sec["text"].lower()))
        if chorus_words and len(sec_words & chorus_words) >= len(chorus_words) * 0.4:
            chorus_sections.append(sec)

    # 1. Če je core refren prekratek, razširi z naslednjim REFRENOM (ne kitico!)
    if actual_end - actual_start < min_duration:
        for sec in chorus_sections:
            if sec["start"] > actual_end and sec["start"] - actual_end < 8:
                if sec["end"] - actual_start <= max_duration:
                    actual_end = sec["end"]
                    if actual_end - actual_start >= min_duration:
                        break

    # 2. Pre-chorus build-up (samo če uporabnik to izrecno hoče)
    if include_prebuild:
        pre_section = None
        for sec in sections:
            # Pre-section mora biti BLIZU (gap < 3s) in NE preveč dolga (< 8s)
            sec_duration = sec["end"] - sec["start"]
            if (sec["end"] <= actual_start
                and actual_start - sec["end"] < 3
                and sec_duration < 8):
                pre_section = sec
        if pre_section:
            candidate_start = pre_section["start"]
            if actual_end - candidate_start <= max_duration:
                actual_start = candidate_start

    # 3. Če je še prekratek, razširi simetrično znotraj refrenov (ne kitic)
    if actual_end - actual_start < min_duration:
        deficit = min_duration - (actual_end - actual_start)
        # Razširi konec če lahko
        for sec in chorus_sections:
            if sec["start"] > actual_end and sec["start"] - actual_end < 5:
                actual_end = min(sec["end"], actual_end + deficit)
                break
        # Če še ni dovolj, manjše simetrično
        if actual_end - actual_start < min_duration:
            extra = (min_duration - (actual_end - actual_start)) / 2
            actual_start = max(0, actual_start - extra)
            actual_end = min(video_duration, actual_end + extra)

    # 4. Trim na max
    if actual_end - actual_start > max_duration:
        actual_end = actual_start + max_duration

    actual_start = max(0, actual_start)
    actual_end = min(video_duration, actual_end)

    return {
        "start": round(actual_start, 2),
        "end": round(actual_end, 2),
        "duration": round(actual_end - actual_start, 2),
        "reason": "smart_chorus_with_prebuild" if include_prebuild else "smart_chorus_only",
        "chorus_start": round(best["start"], 2),
        "chorus_end": round(best["end"], 2),
    }


def detect_audio_fade(clip_range, transcript, video_duration=None):
    """Določi fade-in/fade-out trajanje + ev. razširi clip range, da fade
    ne reže besedila na koncu refrena.

    Logika:
    - Če clip začne sredi vokala → 0.5s fade in
    - Če se konča sredi vokala → razširi clip do konca segmenta (+ buffer),
      potem 1.0s fade out
    - Sicer manj fade
    """
    cs, ce = clip_range["start"], clip_range["end"]

    # Najdi segment, ki konča znotraj clip-a (ali je clip end znotraj segmenta)
    starts_in_vocal = False
    ends_in_vocal = False
    end_segment = None
    for seg in transcript["segments"]:
        if seg["start"] <= cs <= seg["end"]:
            starts_in_vocal = True
        if seg["start"] <= ce <= seg["end"]:
            ends_in_vocal = True
            end_segment = seg

    # Če clip konča znotraj segmenta, razširi do konca segmenta + 0.5s buffer
    extended_end = ce
    if end_segment:
        extended_end = end_segment["end"] + 0.5
        if video_duration is not None:
            extended_end = min(extended_end, video_duration)

    # Fade-in: če clip začne MED vokalom, fade-in mora biti zelo kratek
    # da ne odreže prve besede. Pri vokalnem začetku samo 0.05s "smooth click prevention",
    # ne pravi audible fade. Pri instrumentalnem intro lahko 0.2-0.3s.
    fade_in = 0.05 if starts_in_vocal else 0.2
    # Krajši fade out (0.5s) ker zdaj clip konča po koncu vokala
    fade_out = 0.3 if ends_in_vocal else 0.4

    return {
        "fade_in": fade_in,
        "fade_out": fade_out,
        "extended_end": round(extended_end, 2),
        "ends_in_vocal": ends_in_vocal,
    }


def _build_analysis_prompt(transcript, video_duration, target_duration=30, filename_hint=None, include_prebuild=False):
    """Pripravi enotni prompt za Claude/Gemini analizo.
    
    include_prebuild: če True, lahko vključi pre-chorus pred refrenom.
                      če False (default), MORA biti SAMO refren — strogo.
    """
    lines = []
    for seg in transcript["segments"]:
        start = seg["start"]
        end = seg["end"]
        text = seg["text"].strip()
        lines.append(f"[{start:6.1f}-{end:6.1f}] {text}")
    transcript_text = "\n".join(lines)

    hint_block = ""
    if filename_hint:
        hint_block = f"""

🎵 IME DATOTEKE: "{filename_hint}"

🚨 **PRVI KORAK — VEDNO PRED ANALIZO**:
Iz imena datoteke prepoznaj izvajalca + naslov pesmi. Potem **OBVEZNO uporabi web_search tool** da poiščeš pravo besedilo pesmi — TUDI ČE MISLIŠ DA POZNAŠ PESEM.

Razlog: večinoma ne poznaš celotnih besedil pesmi (predvsem ne-angleških). Brez pravega besedila NE MOREŠ:
- Pravilno prepoznati strukture (verz / pre-chorus / chorus / bridge)
- Vedeti kje refren **začne in konča** (vključno z outro frazami)
- Popraviti STT halucinacij

📋 **Search strategija** (univerzalna za vse jezike):
1. Prvo iskanje: `[izvajalec] [naslov] lyrics` ALI `[izvajalec] [naslov] besedilo/Songtext/letra/versuri`
2. Če ni rezultatov: `[del transkripta - 4-5 zaporednih besed] lyrics`
3. Trusted lyrics sajti po jezikih:
   - 🇸🇮 SLO: besedila.com, lyricstranslate.com
   - 🇩🇪 DE: songtexte.com, lyricstranslate.com
   - 🇭🇷🇷🇸 HR/SR/BS: tekstovi.net, lyricstranslate.com
   - 🇪🇸 ES: letras.com, musica.com
   - 🇷🇴 RO: versuri.ro, lyricstranslate.com
   - 🇮🇹 IT: angolotesti.it
   - 🇫🇷 FR: paroles.net
   - 🇬🇧🇺🇸 EN: genius.com, azlyrics.com
   - **Univerzalno**: lyricstranslate.com (vsi jeziki)

Ko najdeš lyrics:
- Identificiraj kateri del je REFREN (ponavlja se)
- Identificiraj VERZE (zgodba, ne ponavlja se)
- Identificiraj BRIDGE / PRE-CHORUS / OUTRO če obstajajo
- Mapiraj transkript timestamp-e na strukturne dele
- Popravi corrected_segments z dejanskim besedilom
"""

    return f"""Tu je transcript pesmi iz STT modela (timestamp v sekundah, besedilo):

{transcript_text}

Cela pesem traja {video_duration:.1f}s. Cilj: izrezati ~{target_duration}s odsek za TikTok/Instagram Reel.{hint_block}

⚠️ POMEMBNO: STT lahko naredi napake v vseh jezikih, posebej:
- Pri narečjih, slovanskih jezikih, romanskih jezikih
- Generira "tipičen" tekst (npr. tekst druge pesmi istega izvajalca)
- Lahko vstavi besede ki se POdoBNO slišijo, ampak imajo ČISTO drug pomen

KAKO PREPOZNATI HALUCINACIJO:
- Tekst nima smisla v kontekstu pesmi
- Različni segmenti imajo nepovezane teme (kot da bi bilo več pesmi)
- Refren je v vsakem ponovitvi različen (refren se MORA ponavljati identično)
- Tekst je premalo **glede na trajanje** (več tišine = manj besed, ne več)

PROSIM:
1. Preberi celoten tekst in razumi strukturo (intro / verz / pre-chorus / refren / bridge / outro)
2. POPRAVI očitne halucinacije:
   - Če prepoznaš pesem (po izvajalcu, naslovu, znaku besedila) → **uporabi PRAVO besedilo**
   - Če halucinacijo ne moreš popraviti, **odstrani segment** (raje brez podnapisa kot napačen)
   - Refren MORA imeti vse pojavitve ENAKE
   - Popravi pomešane jezike (vse vrstice v enem jeziku)
   - Ohrani timestamp-e nespremenjene
3. Prepoznaj REFREN: del besedila ki se PONAVLJA (ponavadi 2-4 vrstice, ki se v pesmi večkrat ponovijo). To je **univerzalno za vse jezike** — refren je strukturni element pesmi, ne le slovenske/nemške/angleške.

{"" if include_prebuild else '''4. **🎯 KRITIČNO PRAVILO: SAMO REFREN, NIČ DRUGEGA**

   Uporabnik je izbral način "**SAMO REFREN**". To pomeni:
   
   ## ⚠️ ABSOLUTNO PRAVILO: clip se ZAČNE na PRVI BESEDI prvega refrena
   - **NE** vključuj kateri koli verz, pre-chorus, build-up, ali intro
   - **NE** začni "tik pred" refrenom
   - **Začetek = ~0.3s PRED prvo besedo refrena** (npr. če prva beseda refrena "Žena" začne pri 78.0s, izberi start = 77.7s)
   - **Razlog**: 0.3s buffer da ne odrežeš prve besede zaradi audio fade-in efekta
   
   ## Identifikacija refrena (univerzalno čez jezike):
   - Najdi del, ki se v pesmi **ponavlja vsaj 2-krat** (običajno 3-4x)
   - Refren ima ponavadi **najvišjo melodičnost**, "catchy" del
   - Verzi pripovedujejo zgodbo (različno besedilo), refren je vedno enako besedilo
   - Pri pop pesmih: refren običajno začne z naslovom pesmi ali znano frazo
     - Lady Gaga "Abracadabra" → refren = "Abracadabra, amor, ooh-na-na..."
     - "Despacito" → refren = "Despacito, quiero respirar..."
     - "Shape of You" → refren = "I'm in love with the shape of you..."
   - Pri narodno-zabavnih (SL/HR/SR): refren je tisti del, ki se ponovi po vsakem verzu
   - Pri Schlager (DE): refren je melodični "hook" del
   
   ## Konec: vključno s celotnim naravnim izpevom
   - **Vse outro fraze** ki so del refrena (slo: "aj ja ja", "ej ej ej"; en: "yeah", "oh oh"; es: "ay ay ay"; ro: "hei hei"; ja: "la la la")
   - Pevec drži zadnji ton 1-3s — to je **del refrena**, ne reži ga
   - Refren naj se **naravno izteče**
   
   ## Skupna dolžina: 12-25 sekund (običajno)
   - Če refren traja 18s → izberi 18s
   - Če refren traja 25s → izberi 25s
   - **NIKOLI ne dodajaj sekund pred refrenom** za "obogatitev"

   ## 🚫 NAJPOGOSTEJŠE NAPAKE (NE DELAJ TEH):
   - ❌ Vključitev pre-chorusa "ker je vsebinsko povezan" — NE, samo refren!
   - ❌ Začetek 5s pred refrenom za "kontext" — NE, točno na refrenu!
   - ❌ Kombinacija pre-chorus + refren — NE, zgolj refren!
   - ❌ Drugi/tretji nastop refrena — uporabi PRVI
   - ❌ Sekanje sredi besede / izpeta tona
'''}{'''4. **IZBERI ODSEK — REFREN + PRE-CHORUS:**

   Uporabnik je izbral način "**REFREN + PRE-CHORUS**". 

   ## OBVEZNO: cel **PRVI** refren (kot opisano spodaj)
   
   ## OPCIJSKO: pre-chorus PRED refrenom
   - **Pre-chorus = zadnja 1-2 vrstici verza tik pred refrenom** (slišne, povezane z refrenom)
   - **Dodaj samo če**:
     - Je tik pred refrenom (brez pavze ali instrumental vmes)
     - Vsebinsko vodi v refren (gradnja občutka)
     - Je kratek: 4-10 sekund
   - **Ne dodajaj** če bi presegel skupno dolžino 35s
   
   ## REFREN — kot pri "samo refren":
   - Začetek refrena = prva vrstica refrena
   - Konec refrena = vključno z vsemi outro frazami in zadnjim držečim tonom
   - Naravni izpev (ej-ej-ej, oh oh, la la la, etc.)
   
   ## Skupna dolžina: 18-35 sekund
''' if include_prebuild else ""}

5. Če transkript je v večini halucinacija (manj kot 30% smiselnih besed), v "reason" napiši "STT_HALLUCINATION_DETECTED"

Odgovori SAMO v JSON formatu (brez markdown, brez razlage):
{{
  "start": <sekunde>,
  "end": <sekunde>,
  "reason": "<kratka razlaga>",
  "chorus_text": "<besedilo refrena>",
  "structure": "<1 stavek o strukturi pesmi>",
  "language": "<jezik: sl/de/hr/bs/sr/en/it/es/fr>",
  "hallucination_detected": <true/false>,
  "corrected_segments": [
    {{"start": <s>, "end": <s>, "text": "<popravljeno besedilo ALI prazno če halucinacija>"}}
  ]
}}

V "corrected_segments" vključi VSE segmente iz inputa s popravljenim besedilom. Halucinacije nadomesti s pravim besedilom (če veš) ALI pusti prazno besedilo."""


def _parse_llm_response(text, video_duration):
    """Parse JSON odgovor iz LLM-a, vrne None če invalid."""
    text = text.strip()
    # Odstrani markdown ovoj če obstaja
    if text.startswith("```"):
        text = re.sub(r"^```(?:json)?\s*", "", text)
        text = re.sub(r"\s*```$", "", text)
    # Včasih je pred JSON-om še kakšna razlaga, vzemi prvi { ... } blok
    first_brace = text.find("{")
    last_brace = text.rfind("}")
    if first_brace >= 0 and last_brace > first_brace:
        text = text[first_brace:last_brace + 1]

    result = json.loads(text)

    start = float(result["start"])
    end = float(result["end"])
    if start >= end or start < 0 or end > video_duration:
        print(f"   ⚠️ LLM returned invalid range: {start}-{end}", file=sys.stderr)
        return None

    return {
        "start": round(start, 2),
        "end": round(end, 2),
        "duration": round(end - start, 2),
        "reason": result.get("reason", ""),
        "chorus_text": result.get("chorus_text", ""),
        "structure": result.get("structure", ""),
        "language": result.get("language"),
        "corrected_segments": result.get("corrected_segments"),
    }


def analyze_with_claude(transcript, video_duration, target_duration=30, model="claude-sonnet-4-6", filename_hint=None, include_prebuild=False):
    """Pošlje transkript Claude API-ju (Anthropic).
    
    model: claude-sonnet-4-6 (default), claude-haiku-4-5-20251001, claude-opus-4-7
    filename_hint: ime datoteke (Claude lahko prepozna pesem in popravi halucinacije)
    """
    api_key = os.environ.get("ANTHROPIC_API_KEY")
    if not api_key:
        print("   ⚠️ ANTHROPIC_API_KEY ni nastavljen — preskakujem Claude analizo", file=sys.stderr)
        return None

    if not transcript.get("segments"):
        return None

    prompt = _build_analysis_prompt(transcript, video_duration, target_duration, filename_hint=filename_hint, include_prebuild=include_prebuild)

    try:
        import urllib.request
        import urllib.error

        # Initial messages
        messages = [{"role": "user", "content": prompt}]

        # Sonnet 4.6 podpira web_search tool — Claude lahko poišče prave lyrics
        # za pesmi v slovenščini/hrvaščini/itd., če jih ne pozna iz training data.
        tools = [{
            "type": "web_search_20250305",
            "name": "web_search",
            "max_uses": 3,  # Maksimalno 3 search-i = $0.03/job
        }]

        # Agentic loop: Claude lahko kliče web_search, dobi rezultate, vrne final answer
        max_iterations = 5
        for iteration in range(max_iterations):
            body = json.dumps({
                "model": model,
                "max_tokens": 8192,
                "messages": messages,
                "tools": tools,
            }).encode("utf-8")

            req = urllib.request.Request(
                "https://api.anthropic.com/v1/messages",
                data=body,
                headers={
                    "Content-Type": "application/json",
                    "x-api-key": api_key,
                    "anthropic-version": "2023-06-01",
                },
                method="POST",
            )
            with urllib.request.urlopen(req, timeout=180) as resp:
                data = json.loads(resp.read().decode("utf-8"))

            content = data.get("content", [])
            if not content:
                print("   ⚠️ Claude vrnil prazen odgovor", file=sys.stderr)
                return None

            stop_reason = data.get("stop_reason")
            if stop_reason == "max_tokens":
                usage = data.get("usage", {})
                print(
                    f"   ⚠️ Claude odrezan (max_tokens): "
                    f"input={usage.get('input_tokens')} output={usage.get('output_tokens')}",
                    file=sys.stderr,
                )
                return None

            # Če je end_turn → smo končali, parsiraj text
            if stop_reason in ("end_turn", "stop_sequence"):
                # Najdem zadnji text block
                text_blocks = [b for b in content if b.get("type") == "text"]
                if text_blocks:
                    text = text_blocks[-1].get("text", "").strip()
                    break
                print("   ⚠️ Claude end_turn brez text bloka", file=sys.stderr)
                return None

            # Če je tool_use → Claude kliče web_search; appendamo response in nadaljujemo
            if stop_reason == "tool_use":
                # Anthropic web_search tool je server-side — sami obdela searches in vrne web_search_tool_result
                # Ampak v API odgovoru so OBA: tool_use IN web_search_tool_result kot del content
                # Torej končni text že obstaja v naslednji iteraciji
                # Appendamo content do messages in pošljem nazaj (Claude bo nadaljeval)
                messages.append({"role": "assistant", "content": content})
                # Claude server-side že obdela search, samo nadaljujemo s pustim user msg
                # Ampak server-side tools NE potrebujejo follow-up tool_result
                # Pravilen flow: če stop_reason=tool_use ampak web_search_tool_result je že v content,
                # potem Claude sam nadaljuje. Drugače moramo poslati tool_result.
                
                # Preverim ali so že rezultati v content
                has_results = any(b.get("type") == "web_search_tool_result" for b in content)
                if has_results:
                    # Server-side: Anthropic je sam obdelal search, čakamo nadaljevanje
                    # Pošlji nazaj brez sprememb da Claude nadaljuje
                    print(f"   🔍 Claude je iskal lyrics, čakam nadaljevanje (iter {iteration+1})", file=sys.stderr)
                    continue
                else:
                    print(f"   ⚠️ tool_use brez results", file=sys.stderr)
                    return None
            
            # Drugi stop reasons
            print(f"   ⚠️ Nepričakovan stop_reason: {stop_reason}", file=sys.stderr)
            return None
        else:
            print(f"   ⚠️ Presežena max_iterations ({max_iterations})", file=sys.stderr)
            return None

        result = _parse_llm_response(text, video_duration)
        if not result:
            return None

        print(f"   🤖 Claude ({model}) izbral: {result['start']:.1f}-{result['end']:.1f}s", file=sys.stderr)
        print(f"      Razlog: {result.get('reason', '')[:80]}", file=sys.stderr)
        print(f"      Struktura: {result.get('structure', '')[:80]}", file=sys.stderr)
        if result.get("corrected_segments"):
            print(f"      Popravljeni segmenti: {len(result['corrected_segments'])}", file=sys.stderr)

        result["source"] = f"claude:{model}"
        return result
    except urllib.error.HTTPError as e:
        body = e.read().decode("utf-8", errors="replace")[:500]
        print(f"   ❌ Claude API HTTP {e.code}: {body}", file=sys.stderr)
        return None
    except Exception as e:
        print(f"   ❌ Claude analysis failed: {e}", file=sys.stderr)
        return None


def analyze_with_gemini(transcript, video_duration, target_duration=30, model="gemini-3.1-pro-preview", filename_hint=None, include_prebuild=False):
    """Pošlje transkript Gemini API-ju (Google).
    
    Gemini 3.1 Pro ima najboljši multilingual rezultat (MMMLU 92.6%) — odličen za SLO/HR/BS.
    """
    api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY")
    if not api_key:
        print("   ⚠️ GEMINI_API_KEY ni nastavljen — preskakujem Gemini analizo", file=sys.stderr)
        return None

    if not transcript.get("segments"):
        return None

    prompt = _build_analysis_prompt(transcript, video_duration, target_duration, filename_hint=filename_hint, include_prebuild=include_prebuild)

    try:
        import urllib.request
        import urllib.error

        url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={api_key}"
        # Gemini 3.x Pro je THINKING model — porabi tokene tudi za internal reasoning (thoughtsTokenCount).
        # 4096 je prenizko: pri velikih transkriptih thinking lahko porabi 1500-3000 tokenov,
        # output (corrected_segments za 60+ segmentov) pa še dodatnih 3000-7000 → odreže JSON na pol
        # (finishReason: MAX_TOKENS) in vrne nepopolen, neveljaven JSON.
        # 32768 daje dovolj prostora za thinking + cel JSON output tudi pri dolgih pesmih.
        body = json.dumps({
            "contents": [{
                "role": "user",
                "parts": [{"text": prompt}],
            }],
            "generationConfig": {
                "temperature": 0.1,
                "maxOutputTokens": 32768,
                "responseMimeType": "application/json",
            },
        }).encode("utf-8")

        req = urllib.request.Request(
            url,
            data=body,
            headers={"Content-Type": "application/json"},
            method="POST",
        )
        with urllib.request.urlopen(req, timeout=180) as resp:
            data = json.loads(resp.read().decode("utf-8"))

        candidates = data.get("candidates", [])
        if not candidates:
            print("   ⚠️ Gemini vrnil 0 candidates", file=sys.stderr)
            return None

        cand0 = candidates[0]
        finish_reason = cand0.get("finishReason", "?")
        usage = data.get("usageMetadata", {})

        # Diagnostika: če je finishReason == MAX_TOKENS, je output odrezan in JSON je invalid
        if finish_reason == "MAX_TOKENS":
            print(
                f"   ⚠️ Gemini odrezan (MAX_TOKENS): "
                f"prompt={usage.get('promptTokenCount')} "
                f"thoughts={usage.get('thoughtsTokenCount')} "
                f"output={usage.get('candidatesTokenCount')}",
                file=sys.stderr,
            )
            return None

        parts = cand0.get("content", {}).get("parts", [])
        if not parts:
            print(
                f"   ⚠️ Gemini vrnil prazen content (finishReason={finish_reason}, "
                f"thoughts={usage.get('thoughtsTokenCount')})",
                file=sys.stderr,
            )
            return None
        text = parts[0].get("text", "").strip()
        if not text:
            print(
                f"   ⚠️ Gemini vrnil prazen text (finishReason={finish_reason}, "
                f"thoughts={usage.get('thoughtsTokenCount')}, "
                f"output={usage.get('candidatesTokenCount')})",
                file=sys.stderr,
            )
            return None

        result = _parse_llm_response(text, video_duration)
        if not result:
            return None

        print(f"   🤖 Gemini ({model}) izbral: {result['start']:.1f}-{result['end']:.1f}s", file=sys.stderr)
        print(f"      Razlog: {result.get('reason', '')[:80]}", file=sys.stderr)
        print(f"      Struktura: {result.get('structure', '')[:80]}", file=sys.stderr)
        if result.get("corrected_segments"):
            print(f"      Popravljeni segmenti: {len(result['corrected_segments'])}", file=sys.stderr)

        result["source"] = f"gemini:{model}"
        return result
    except urllib.error.HTTPError as e:
        body = e.read().decode("utf-8", errors="replace")[:500]
        print(f"   ❌ Gemini API HTTP {e.code}: {body}", file=sys.stderr)
        return None
    except Exception as e:
        print(f"   ❌ Gemini analysis failed: {e}", file=sys.stderr)
        return None


def analyze_with_llm(transcript, video_duration, target_duration=30, provider="claude", llm_model=None, filename_hint=None, include_prebuild=False):
    """Glavna funkcija — uporabi izbrano LLM (claude/gemini/auto)."""
    if provider == "gemini":
        model = llm_model or "gemini-3.1-pro-preview"
        return analyze_with_gemini(transcript, video_duration, target_duration, model, filename_hint=filename_hint, include_prebuild=include_prebuild)
    elif provider == "claude":
        model = llm_model or "claude-sonnet-4-6"
        return analyze_with_claude(transcript, video_duration, target_duration, model, filename_hint=filename_hint, include_prebuild=include_prebuild)
    elif provider == "auto":
        # Najprej probaj Claude, fallback na Gemini
        result = analyze_with_claude(transcript, video_duration, target_duration,
                                      llm_model or "claude-sonnet-4-6", filename_hint=filename_hint, include_prebuild=include_prebuild)
        if result:
            return result
        print("   🔄 Claude ni uspel, probam Gemini...", file=sys.stderr)
        return analyze_with_gemini(transcript, video_duration, target_duration,
                                    llm_model or "gemini-3.1-pro-preview", filename_hint=filename_hint, include_prebuild=include_prebuild)
    else:
        print(f"   ⚠️ Neznan LLM provider: {provider}", file=sys.stderr)
        return None



def is_instrumental(transcript, video_duration, threshold=0.1):
    """Detekcija ali je pesem instrumentalna.

    Če je vsota trajanja vokalnih segmentov < threshold * video_duration,
    je pesem instrumentalna.
    """
    if not transcript.get("segments"):
        return True
    vocal_duration = sum(
        s["end"] - s["start"] for s in transcript["segments"]
    )
    ratio = vocal_duration / max(video_duration, 1)
    return bool(ratio < threshold)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("video", help="Vhod video file")
    ap.add_argument("--lang", default=None, help="ISO 639-1 ali 'auto' (default: auto)")
    ap.add_argument("--model", default="large-v3", help="Whisper model")
    ap.add_argument("--target-duration", type=float, default=30.0)
    ap.add_argument("--max-duration", type=float, default=45.0)
    ap.add_argument("--min-duration", type=float, default=20.0)
    ap.add_argument("--include-prebuild", action="store_true",
                    help="Vključi pre-chorus build-up (privzeto: ne)")
    ap.add_argument("--no-claude", action="store_true",
                    help="Preskoči LLM analizo (uporabi samo lokalno heuristiko)")
    ap.add_argument("--llm-provider", default="claude",
                    choices=["claude", "gemini", "auto"],
                    help="Kateri LLM uporabiti za analizo (default: claude)")
    ap.add_argument("--llm-model", default=None,
                    help="Specifičen model (npr. claude-sonnet-4-6, gemini-3.1-pro-preview)")
    ap.add_argument("--filename-hint", default=None,
                    help="Originalno ime datoteke (Claude lahko prepozna pesem)")
    ap.add_argument("--whisper-provider", default="auto",
                    choices=["auto", "elevenlabs", "local"],
                    help="STT provider: elevenlabs=ElevenLabs Scribe (najboljša kvaliteta, $0.40/h), "
                         "local=faster-whisper CPU (brezplačno, halucinacije), auto=Scribe če key, sicer local")
    ap.add_argument("--json", action="store_true", help="Output JSON")
    ap.add_argument("--output", help="Path za JSON output")
    args = ap.parse_args()

    video = Path(args.video)
    if not video.exists():
        print(f"❌ Video ne obstaja: {video}", file=sys.stderr)
        sys.exit(1)

    duration = get_video_duration(video)
    print(f"📹 Video: {video.name}, {duration:.1f}s", file=sys.stderr)

    # 1. Extract avdio
    audio = extract_audio(video)

    try:
        # 2. Whisper transcript
        lang = None if args.lang in (None, "auto", "") else args.lang
        # Filename hint pomaga Scribu detektirati jezik (Avseniki → SL, Ben Zucker → DE)
        fname_hint = args.filename_hint or video.stem
        transcript = transcribe_full(
            audio, lang=lang, model_size=args.model,
            provider=args.whisper_provider,
            filename_hint=fname_hint,
        )
        print(f"   Transkripcija: {len(transcript['segments'])} segmentov", file=sys.stderr)

        # 3. Energy profile
        print(f"⚡ Energy profile...", file=sys.stderr)
        energies = compute_energy_profile(audio)
        print(f"   Energy samples: {len(energies)}", file=sys.stderr)

        # 4. Instrumental detection
        instrumental = is_instrumental(transcript, duration)
        print(f"🎵 Instrumentalna: {instrumental}", file=sys.stderr)

        # 5a. PRIMARNO: LLM analiza (razume cel tekst pesmi + popravki)
        claude_result = None
        if not instrumental and not args.no_claude:
            provider = args.llm_provider
            print(f"🤖 Pošiljam transkript {provider}-u za analizo...", file=sys.stderr)
            # Filename hint = original filename brez extension (Claude lahko prepozna pesem)
            fname_hint = args.filename_hint or video.stem
            claude_result = analyze_with_llm(
                transcript, duration, target_duration=args.target_duration,
                provider=provider, llm_model=args.llm_model,
                filename_hint=fname_hint,
                include_prebuild=args.include_prebuild,
            )

        # 5b. Find chorus lokalno (kot fallback ali za score-jev preview)
        if not instrumental:
            chorus = find_chorus(transcript, energies, duration)
        else:
            # Za instrumentalne: najdi sekcijo z najvišjo energijo
            window = args.target_duration
            best_start = 0
            best_avg = -100
            t = 0
            while t + window <= duration:
                avg = avg_energy_in_range(energies, t, t + window)
                if avg > best_avg:
                    best_avg = avg
                    best_start = t
                t += 5  # step 5s
            chorus = {
                "best": {
                    "start": best_start,
                    "end": best_start + window,
                    "duration": window,
                    "text_preview": "(instrumental — energy peak)",
                    "score": 0,
                    "avg_rms": round(best_avg, 2),
                },
                "all_candidates": [],
                "avg_rms_total": round(
                    sum(r for (_, r) in energies) / len(energies) if energies else -30, 2
                ),
            }

        # 6. Clip range — LLM (Claude/Gemini) ima prednost, sicer smart_clip_range fallback.
        # POMEMBNO: spremenljivka se zgodovinsko imenuje claude_result, dejansko pa vsebuje
        # rezultat KATEREGA KOLI LLM-ja (Claude ali Gemini) — glej analyze_with_llm().
        # llm_source npr. "claude:claude-sonnet-4-6" ali "gemini:gemini-3.1-pro-preview".
        if claude_result:
            llm_source = claude_result.get("source", "llm")
            clip_range = {
                "start": claude_result["start"],
                "end": claude_result["end"],
                "duration": claude_result["duration"],
                "reason": f"{llm_source}: " + claude_result.get("reason", ""),
                "chorus_text": claude_result.get("chorus_text", ""),
                "structure": claude_result.get("structure", ""),
                "source": llm_source,
            }
            # Apply max_duration cap če LLM pretirava
            if clip_range["duration"] > args.max_duration:
                clip_range["end"] = clip_range["start"] + args.max_duration
                clip_range["duration"] = args.max_duration
                clip_range["reason"] += " (capped at max_duration)"

            # ── EXTEND clip end do naslednje naravne pavze ──
            # LLM pogosto reže točno na zadnji besedi refrena, ampak zadnja
            # beseda ima še "ej-ej-ej" outro / pevec drži zadnji ton 1-3s.
            # Razširimo clip do naslednje >= 1s pavze ali instrumentalnega bridg-a,
            # ampak ne čez max_duration + 5s.
            corrected_segs = claude_result.get("corrected_segments") or transcript["segments"]
            current_end = clip_range["end"]
            extension_limit = min(
                clip_range["start"] + args.max_duration + 5,  # max 5s nad max_duration
                duration  # ne čez celoten audio
            )
            
            # ── EXTEND clip START nazaj če Claude začne sredi besede/segmenta ──
            # Refren se pogosto začne na isti besedi kot v transkriptu, ampak Scribe
            # lahko zazna mejo med segmenti **PO** prvi besedi refrena (npr.
            # "Žena me tepe" — beseda "Žena" v prejšnjem segmentu pri 78.0s,
            # nov segment začne pri 78.3s s "tepe"). To pomeni Claude reže
            # PRED besedo "Žena" → odrezana.
            # 
            # Strategija: če clip start pade SREDI segmenta (ne tik na začetku),
            # razširi nazaj na začetek tega segmenta + 0.2s buffer.
            # ── EXTEND clip START nazaj če Claude začne sredi besede ali tik za njo ──
            # Pesem se pogosto začne na isti besedi v transkriptu, ampak Scribe lahko
            # zazna mejo med segmenti **PO** prvi besedi (npr. "Žena me tepe" — "Žena"
            # je v prejšnjem segmentu pri 76.88-77.70s, novi segment začne 78.30).
            # Claude reže tipično na začetku novega segmenta = odrezana prva beseda.
            #
            # Strategija: **na ravni besed** — najdi besedo katere konec je 
            # blizu clip start (±0.5s) IN preveri ali se lahko ta beseda 
            # "naslanja" na clip (z malo pavze do naslednje besede).
            current_start = clip_range["start"]
            
            # Zberi VSE besede z njihovimi timestampi
            all_words = []
            for seg in corrected_segs:
                for w in seg.get("words", []):
                    if w.get("start") is not None and w.get("end") is not None:
                        all_words.append({
                            "start": float(w["start"]),
                            "end": float(w["end"]),
                            "text": w.get("text", ""),
                        })
            
            if all_words:
                # Najdi "rob" — beseda kjer končanje zelo blizu clip start
                # ALI clip start je sredi besede (besedo bi odrezali)
                for i, w in enumerate(all_words):
                    # Beseda zaobsega clip start (clip reže sredi besede)
                    if w["start"] < current_start < w["end"]:
                        new_start = max(0, w["start"] - 0.15)
                        print(f"   🎵 Razširim clip začetek {current_start:.2f}s → {new_start:.2f}s "
                              f"(clip rezal sredi besede '{w['text'].strip()}')", file=sys.stderr)
                        current_start = new_start
                        break
                    # Beseda končana TIK pred clip start (do 0.5s pred)
                    # IN je naslednja beseda PO/blizu clip start
                    if 0 < (current_start - w["end"]) <= 0.5:
                        # Preveri naslednjo besedo
                        next_w = all_words[i + 1] if i + 1 < len(all_words) else None
                        if next_w and next_w["start"] >= current_start - 0.1:
                            # Razdalja od te besede do naslednje > 0.3s pomeni mogoče prelom verz/refren
                            gap_to_next = next_w["start"] - w["end"]
                            # Razširi nazaj na začetek te besede - 0.15s buffer
                            new_start = max(0, w["start"] - 0.15)
                            print(f"   🎵 Razširim clip začetek {current_start:.2f}s → {new_start:.2f}s "
                                  f"(beseda '{w['text'].strip()}' končana {current_start - w['end']:.2f}s pred clip start, "
                                  f"morda začne refren; gap do '{next_w['text'].strip()}' = {gap_to_next:.2f}s)", file=sys.stderr)
                            current_start = new_start
                            break
            else:
                # Fallback: če ni word-level (npr. local Whisper), uporabi segmente kot prej
                for seg in corrected_segs:
                    seg_start = float(seg.get("start", 0))
                    seg_end = float(seg.get("end", 0))
                    if seg_start < current_start < seg_end:
                        new_start = max(0, current_start - 0.5)
                        print(f"   🎵 Razširim clip začetek {current_start:.2f}s → {new_start:.2f}s "
                              f"(brez word-level, fallback -0.5s)", file=sys.stderr)
                        current_start = new_start
                        break
            
            if current_start < clip_range["start"]:
                clip_range["start"] = round(current_start, 2)
                clip_range["duration"] = round(clip_range["end"] - current_start, 2)
                clip_range["reason"] += f" (start extended back)"
            
            # Najdi vse segmente ki se začnejo PO trenutnem clip end
            # STROŽJA pravila: ne podaljšuj v naslednji refren / verz / instrumental.
            # Razširjamo SAMO če zadnji segment se prekriva s clip (klesti iz njega) ALI
            # če je naslednji segment KRATEK (< 2s) IN vsebuje samo outro fillerje
            # (la la, oh, yeah, ej, ja, ah, na, hey itd.).
            
            # Definiraj outro filler regex (multi-jezikovno)
            import re as _re
            OUTRO_FILLER_RE = _re.compile(
                r'^[\s\-,.!?]*'
                r'((?:la|na|oh|ah|eh|ej|aj|ja|hey|yeah|yo|ho|wo|hu|mm|nn|uu|oo|aa|ee|ii)'
                r'[\s\-,.!?]*)+'
                r'[\s\-,.!?]*$',
                _re.IGNORECASE
            )
            # Hard cap: ne razširjaj več kot 3s nad původne clip end
            original_clip_end = clip_range["end"]
            soft_extension_limit = min(original_clip_end + 3.0, extension_limit)
            
            for seg in corrected_segs:
                seg_start = float(seg.get("start", 0))
                seg_end = float(seg.get("end", 0))
                seg_text = seg.get("text", "").strip()
                
                # Segment se prekriva s clip end (zadnji segment refrena, ki ni zaključen)
                if seg_start <= current_end:
                    if seg_end > current_end and seg_end <= soft_extension_limit:
                        new_end = min(seg_end + 0.3, soft_extension_limit)
                        if new_end > current_end:
                            print(f"   🎵 Podaljšam clip {current_end:.1f}s → {new_end:.1f}s "
                                  f"(zadnji segment refrena se zaključi)", file=sys.stderr)
                            current_end = new_end
                else:
                    # Segment začne PO clip end — preveri ali je outro filler
                    pause = seg_start - current_end
                    
                    # Predaleč → ustavi se
                    if pause >= 0.7:
                        break
                    # Predolg segment = nov verz/refren, ne dodaj
                    if (seg_end - seg_start) > 2.5:
                        break
                    # Preveri vsebino — če ni samo outro fillerji, NE dodaj
                    if not OUTRO_FILLER_RE.match(seg_text):
                        # Ni filler → verjetno nov refren/verz/post-chorus
                        break
                    
                    # OK, je outro filler — dodaj
                    new_end = min(seg_end + 0.2, soft_extension_limit)
                    if new_end > current_end:
                        print(f"   🎵 Podaljšam clip {current_end:.1f}s → {new_end:.1f}s "
                              f"(outro filler '{seg_text[:40]}')", file=sys.stderr)
                        current_end = new_end
                    else:
                        break
            
            if current_end > clip_range["end"]:
                clip_range["end"] = round(current_end, 2)
                clip_range["duration"] = round(current_end - clip_range["start"], 2)
                clip_range["reason"] += f" (extended to natural pause)"
        else:
            clip_range = smart_clip_range(
                chorus, transcript, duration,
                target_duration=args.target_duration,
                max_duration=args.max_duration,
                min_duration=args.min_duration,
                include_prebuild=args.include_prebuild,
            )
            clip_range["source"] = "local_heuristic"
        print(f"✂  Clip range: {clip_range['start']:.1f}s - {clip_range['end']:.1f}s "
              f"(duration: {clip_range['duration']}s, source: {clip_range.get('source')})",
              file=sys.stderr)

        # Če Claude je vrnil popravljene segmente, jih uporabi (boljši za podnapise)
        if claude_result and claude_result.get("corrected_segments"):
            corrected = claude_result["corrected_segments"]
            # Ohrani word-level timing iz originala, posodobi samo text
            orig_by_start = {round(s["start"], 1): s for s in transcript["segments"]}
            new_segments = []
            for cs in corrected:
                try:
                    cs_start = float(cs["start"])
                    cs_end = float(cs["end"])
                    cs_text = str(cs["text"]).strip()
                except (KeyError, ValueError, TypeError):
                    continue
                # Najdi originalni segment z istim start (ali blizu) za word-level timing
                orig = orig_by_start.get(round(cs_start, 1))
                if not orig:
                    # Najdi najbližji
                    closest_diff = 999
                    for s in transcript["segments"]:
                        diff = abs(s["start"] - cs_start)
                        if diff < closest_diff and diff < 1.0:
                            closest_diff = diff
                            orig = s
                new_segments.append({
                    "start": cs_start,
                    "end": cs_end,
                    "text": cs_text,
                    # Word-level timing ne moremo posodabljati ker Claude ne vrača besede,
                    # ampak ohranimo če imamo
                    "words": orig.get("words", []) if orig else [],
                })
            transcript["segments"] = new_segments
            transcript["claude_corrected"] = True  # ohranimo ime ključa zaradi backward compat
            # Posodobi tudi jezik če LLM je drugačnega mnenja
            if claude_result.get("language") and claude_result["language"] != transcript["language"]:
                print(f"   ✏️ LLM je popravil jezik: {transcript['language']} → {claude_result['language']}", file=sys.stderr)
                transcript["language"] = claude_result["language"]
            llm_label = claude_result.get("source", "LLM")
            print(f"   ✏️ Whisper segmenti zamenjani z {llm_label} popravljenimi ({len(new_segments)})", file=sys.stderr)

        # 7. Fade params (lahko razširi clip end če konča sredi vokala)
        fade = detect_audio_fade(clip_range, transcript, video_duration=duration)
        print(f"🎚  Fade: in={fade['fade_in']}s, out={fade['fade_out']}s", file=sys.stderr)

        # Če fade detection razširi end (ker clip konča sredi vokala), apply
        if fade.get("extended_end") and fade["extended_end"] > clip_range["end"]:
            old_end = clip_range["end"]
            new_end = min(fade["extended_end"], clip_range["start"] + args.max_duration)
            clip_range["end"] = round(new_end, 2)
            clip_range["duration"] = round(new_end - clip_range["start"], 2)
            print(f"   ↳ Razširjen za {new_end - old_end:.1f}s (zaključek besedila)",
                  file=sys.stderr)

        result = {
            "video": str(video),
            "video_duration": duration,
            "language": transcript["language"],
            "language_probability": transcript["language_probability"],
            "instrumental": instrumental,
            "transcript": transcript,
            "chorus": chorus,
            "clip_range": clip_range,
            "fade": fade,
            "claude_used": claude_result is not None,
            "claude_corrected_text": bool(claude_result and claude_result.get("corrected_segments")),
        }

        if args.output:
            with open(args.output, "w", encoding="utf-8") as f:
                json.dump(result, f, ensure_ascii=False, indent=2)
            print(f"💾 Saved: {args.output}", file=sys.stderr)

        if args.json:
            print(json.dumps(result, ensure_ascii=False))

    finally:
        try:
            os.unlink(audio)
        except Exception:
            pass


if __name__ == "__main__":
    main()