#!/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 transcribe_with_elevenlabs(audio_path, lang=None, model="scribe_v1"):
    """ElevenLabs Scribe transkripcija (najboljša multilingual accuracy 2026).
    
    Lang accepted in ISO 639-1 ('de', 'sl', 'hr') — auto-converted to ISO 639-3.
    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

    # 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",
    }
    # Reverse mapping for parsing response
    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")
    add_text("tag_audio_events", "false")
    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 whitespace tokens
        real_words = [w for w in words if w.get("text", "").strip()]
        
        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"):
    """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
    """
    if provider in ("elevenlabs", "auto") and os.environ.get("ELEVENLABS_API_KEY"):
        result = transcribe_with_elevenlabs(audio_path, lang=lang)
        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 = 0.4 if starts_in_vocal else 0.2
    # Krajši fade out (0.5s) ker zdaj clip konča po koncu vokala
    fade_out = 0.5 if ends_in_vocal else 0.3

    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):
    """Pripravi enotni prompt za Claude/Gemini analizo."""
    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}"
Iz imena datoteke morda lahko prepoznaš naslov pesmi ali izvajalca. Če je tako:
- Uporabi svoje znanje o **dejanskem besedilu** te pesmi
- Če Whisper transkript ne ustreza znanemu besedilu pesmi, POPRAVI besedilo na **dejansko besedilo pesmi**
- Ohrani timestamp-e iz Whisper-ja (časovne meje so pravilne, samo besede so morda napačne)

🔍 ČE NE POZNAŠ PESMI (npr. slovenske narodno-zabavne, manj znane pesmi) → **UPORABI web_search tool** da poiščeš pravo besedilo!
   Primeri search queryjev:
   - "[ime izvajalca] [naslov pesmi] besedilo" (slovenske: Modrijani, Veseli Dolenjci, Avseniki, Čuki, Atomik Harmonik)
   - "[artist] [title] lyrics" (angleške/nemške)
   - Pogosto so besedila na: besedila.com, lyricstranslate.com, genius.com, tekstovi.net (HR/SR), songtexte.com (DE)
   Ko najdeš pravo besedilo, uporabi to za popravljanje "corrected_segments" — **transkript bo veliko bolj točen** kot če le ugibaš.
"""

    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 — narečne besede, slovanski jeziki, ko glasba prevladuje:
- Pri narečjih in slovanskih 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
4. Izberi najboljši odsek za reel:
   - Vključi cel refren (brez prekinitve)
   - Lahko dodaj pre-chorus build-up
   - 20-45 sekund
   - Začni in končaj na smiselni meji
5. Če pesem nima jasnega refrena, izberi najbolj dramatičen ali zaključen del
6. Če Whisper transkript je v večini halucinacija (manj kot 30% smiselnih besed), v "reason" napiši "WHISPER_HALLUCINATION_DETECTED" in vrni najmanj segmentov (samo tisti ki so smiselni)

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):
    """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)

    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):
    """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)

    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):
    """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)
    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)
    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)
        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)
    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
        transcript = transcribe_full(
            audio, lang=lang, model_size=args.model,
            provider=args.whisper_provider,
        )
        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,
            )

        # 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)"
        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()