From 44e65299e5d35ce3293701dfd561fdcad84de773 Mon Sep 17 00:00:00 2001
From: Victor Giers <mail@victorgiers.com>
Date: Thu, 14 Aug 2025 09:18:01 +0200
Subject: [PATCH] initial commit

---
 icon.png         |  Bin 0 -> 68692 bytes
 srt-translate.py | 2163 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 2163 insertions(+)
 create mode 100644 icon.png
 create mode 100755 srt-translate.py

diff --git a/icon.png b/icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..3361ec79ca43c6b5d530fa98b0ad08fd9350f843
GIT binary patch
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HcmV?d00001

diff --git a/srt-translate.py b/srt-translate.py
new file mode 100755
index 0000000..205b4f0
--- /dev/null
+++ b/srt-translate.py
@@ -0,0 +1,2163 @@
+#!/usr/bin/env python3
+"""
+SRT Translator — NLLB, SeamlessM4T, or Ollama
+GUI + CLI, Multithreaded, Cancel, Context-aware (Line/Cue/Smart)
+
+New in this build:
+- FIX: Leading commas sometimes survived. Now:
+  * _fix_leading_punct() recognizes Unicode commas (，、) and runs until stable.
+  * _remove_marker_and_tagn_tokens() strips any remaining leading commas per line as a last-resort.
+  * NBSP / narrow NBSP / ideographic spaces normalized to regular spaces.
+- FIX: Ollama 'groups' robustness.
+  * If the model doesn't return a valid "groups" array, we transparently fall back to per-group "translate_cues"
+    requests and coerce counts to the expected shape.
+  * No more RuntimeError spam; threads finish gracefully.
+- FIX: Marker remover accidentally included TAG (would nuke 'STAGE' etc). Now only BR|CUE; Tag removal stays via TAG0..TAG9.
+- (from earlier) Restore tags bug fixed; per-line cleanup removes BR/CUE tokens and TAG0..TAG9; spaces collapsed; punctuation spacing normalized.
+- NEW: Ollama untranslated-line Repair-Pass:
+  * Language-name prompts (e.g., "English" statt "eng_Latn") to avoid false "already in target language".
+  * Automatic per-line detection of unchanged output + strict re-try with temperature schedule and optional model fallback.
+- NEW: Cancel reliability
+  * Entferntes Pause-Feature (UI & Logik).
+  * Kooperative Queue-Abfrage mit kurzen Timeouts und sofortigem Drain bei Cancel.
+  * Mikrobatching in allen Workern (Transformers & Ollama), Event-Checks zwischen Mikrobatches.
+"""
+
+import argparse
+import os
+import re
+import sys
+import time
+import json
+import threading
+import queue
+import http.client
+import subprocess
+import html as _html
+from typing import List, Dict, Tuple, Optional
+
+# ---- Optional CLI progress (tqdm) ----
+try:
+    from tqdm.auto import tqdm
+except Exception:
+    tqdm = None
+
+# ---- Optional auto-detect ----
+try:
+    from langdetect import detect as _ld_detect
+except Exception:
+    _ld_detect = None
+
+CONFIG_PATH = os.path.join(os.path.expanduser("~"), ".srt_translator_config.json")
+SENTINEL_BR = "⟦BR⟧"   # within cue: between original lines
+SENTINEL_CUE = "⟦CUE⟧"  # within group: between original cues
+SENTINEL_TAG = "⟦TAG⟧"
+
+# ===================== Language code mapping =====================
+LANG2CODE = {
+    "english": "eng_Latn", "german": "deu_Latn", "french": "fra_Latn", "spanish": "spa_Latn",
+    "portuguese": "por_Latn", "italian": "ita_Latn", "dutch": "nld_Latn", "russian": "rus_Cyrl",
+    "japanese": "jpn_Jpan", "korean": "kor_Hang", "chinese": "zho_Hans", "chinese (simplified)": "zho_Hans",
+    "chinese (traditional)": "zho_Hant", "arabic": "arb_Arab", "hebrew": "heb_Hebr", "turkish": "tur_Latn",
+    "polish": "pol_Latn", "czech": "ces_Latn", "greek": "ell_Grek", "swedish": "swe_Latn",
+    "danish": "dan_Latn", "norwegian": "nob_Latn", "finnish": "fin_Latn", "romanian": "ron_Latn",
+    "hungarian": "hun_Latn", "ukrainian": "ukr_Cyrl", "bulgarian": "bul_Cyrl", "serbian": "srp_Latn",
+    "croatian": "hrv_Latn", "slovak": "slk_Latn", "slovenian": "slv_Latn", "lithuanian": "lit_Latn",
+    "estonian": "est_Latn", "vietnamese": "vie_Latn", "thai": "tha_Thai", "hindi": "hin_Deva",
+    "bengali": "ben_Beng", "indonesian": "ind_Latn", "malay": "zsm_Latn", "filipino": "tgl_Latn",
+    # Aliases
+    "jp": "jpn_Jpan", "de": "deu_Latn", "en": "eng_Latn", "fr": "fra_Latn", "es": "spa_Latn",
+    "pt": "por_Latn", "ru": "rus_Cyrl", "zh": "zho_Hans", "ko": "kor_Hang", "ar": "arb_Arab",
+}
+
+# ===================== SRT parsing =====================
+CUE_RE = re.compile(
+    r"(?P<num>\d+)\r?\n"
+    r"(?P<ts>\d{2}:\d{2}:\d{2},\d{3}\s-->\s\d{2}:\d{2}:\d{2},\d{3})(?:.*)?\r?\n"
+    r"(?P<text>(?:.+(?:\r?\n)?)+?)"
+    r"\r?\n(?=\d+\r?\n|\Z)",
+    re.UNICODE
+)
+
+def parse_srt(srt_text: str) -> List[Dict[str, str]]:
+    cues = []
+    for m in CUE_RE.finditer(srt_text):
+        text = m.group("text")
+        cues.append({"num": m.group("num"), "ts": m.group("ts"), "text": text.rstrip("\n")})
+    return cues
+
+def cues_to_srt(cues: List[Dict[str, str]]) -> str:
+    parts = []
+    for c in cues:
+        parts.append(f"{c['num']}\n{c['ts']}\n{c['text']}\n")
+    return "\n".join(parts).strip() + "\n"
+
+# ===================== Tag protection =====================
+TAG_RE = re.compile(r"</?[^>\n\r]+?>")      # <i>…</i>, etc.
+ASS_TAG_RE = re.compile(r"\{\\[^}]*\}")     # {\an8}, etc.
+
+def protect_tags(text: str) -> Tuple[str, Dict[str, str]]:
+    """
+    Replace inline tags with placeholders so the translator doesn't mutate them.
+    We DO NOT mask SENTINEL_BR/CUE so we can split later.
+    """
+    placeholders: Dict[str, str] = {}
+    idx = 0
+    def _sub(reobj, s):
+        nonlocal idx
+        def repl(m):
+            nonlocal idx
+            token = f"⟦TAG{idx}⟧"
+            placeholders[token] = m.group(0)
+            idx += 1
+            return token
+        return reobj.sub(repl, s)
+    masked = _sub(TAG_RE, text)
+    masked = _sub(ASS_TAG_RE, masked)
+    return masked, placeholders
+
+_TAGNUM_RE = re.compile(r"TAG(\d+)")
+
+def restore_tags(text: str, placeholders: Dict[str, str]) -> str:
+    """
+    Robustly restore placeholders:
+    - ⟦TAG3⟧ (ideal)
+    - TAG3 (if model stripped brackets)
+    - ⟦  TAG3  ⟧ (extra spaces)
+    - quoted variants like "TAG3"
+    """
+    out = text
+    for token, original in placeholders.items():
+        m = _TAGNUM_RE.search(token)
+        if not m:
+            continue
+        k = m.group(1)
+        variants = [
+            token,
+            f"TAG{k}",
+            f"⟦TAG{k}⟧",
+            f"⟦ TAG{k} ⟧",
+            f"'TAG{k}'", f"\"TAG{k}\"",
+            f"‘TAG{k}’", f"“TAG{k}”",
+        ]
+        for v in variants:
+            out = out.replace(v, original)
+        pattern = rf"(?:⟦\s*)?TAG{k}(?:\s*⟧)?"
+        out = re.sub(pattern, lambda _m, _orig=original: _orig, out)
+    return out
+
+# ===================== Language utils =====================
+def norm_lang_to_code(name_or_code: str, default: Optional[str] = None) -> Optional[str]:
+    if name_or_code is None:
+        return default
+    s = name_or_code.strip()
+    if re.match(r"^[a-z]{3}_[A-Za-z]{4}$", s):
+        return s
+    return LANG2CODE.get(s.lower(), default)
+
+def autodetect_code(text_sample: str) -> str:
+    if not _ld_detect:
+        return "eng_Latn"
+    try:
+        lang = _ld_detect(text_sample)
+        return LANG2CODE.get(lang.lower(), {
+            "en": "eng_Latn", "de": "deu_Latn", "fr": "fra_Latn", "es": "spa_Latn",
+            "pt": "por_Latn", "it": "ita_Latn", "nl": "nld_Latn", "ru": "rus_Cyrl",
+            "ja": "jpn_Jpan", "ko": "kor_Hang", "zh-cn": "zho_Hans", "zh-tw": "zho_Hant", "ar": "arb_Arab"
+        }.get(lang.lower(), "eng_Latn"))
+    except Exception:
+        return "eng_Latn"
+
+# ---------- Language names & detection helpers for Ollama (NEW) ----------
+def _lang_name_for_prompt(name_or_code: Optional[str]) -> str:
+    if not name_or_code:
+        return "Unknown"
+    s = name_or_code.strip()
+    if re.match(r"^[a-z]{3}_[A-Za-z]{4}$", s):
+        for k, v in LANG2CODE.items():
+            if len(k) <= 2:
+                continue
+            if v == s:
+                return k[:1].upper() + k[1:]
+        return s
+    return s[:1].upper() + s[1:]
+
+def _build_code2iso2_map() -> Dict[str, str]:
+    m: Dict[str, str] = {}
+    for alias, code in LANG2CODE.items():
+        if len(alias) <= 2:
+            m[code] = alias
+    return m
+
+CODE2ISO2 = _build_code2iso2_map()
+
+_NONWORD_RE = re.compile(r"[^\w\u00C0-\uFFFF]+", flags=re.UNICODE)
+_TAG_SENTINEL_RE = re.compile(r"⟦TAG\d+⟧|⟦BR⟧|⟦CUE⟧")
+
+def _normalize_for_compare(s: str) -> str:
+    s = _TAG_SENTINEL_RE.sub("", s or "")
+    s = _html.unescape(s)
+    s = s.lower()
+    s = _NONWORD_RE.sub("", s)
+    return s
+
+def _looks_untranslated(src_masked: str, out_raw: str, src_code: Optional[str], tgt_code: Optional[str]) -> bool:
+    s_norm = _normalize_for_compare(src_masked)
+    t_norm = _normalize_for_compare(out_raw)
+    if len(s_norm) >= 4 and s_norm == t_norm:
+        return True
+    if _ld_detect:
+        try:
+            probe = _TAG_SENTINEL_RE.sub("", out_raw or "")
+            if len(probe) >= 12:
+                ld = _ld_detect(probe)
+                src_iso = CODE2ISO2.get(src_code or "", "")
+                tgt_iso = CODE2ISO2.get(tgt_code or "", "")
+                if ld and src_iso and ld == src_iso and (not tgt_iso or ld != tgt_iso):
+                    return True
+        except Exception:
+            pass
+    return False
+
+# ===================== Sentence boundary heuristics =====================
+_END_PUNCT_RE = re.compile(r'[\.!\?…。！？][»”"”\)\]\}]*\s*$')
+_CONTINUATION_HINT_RE = re.compile(r'([,;:—\-…]|--)\s*$')
+
+def _strip_tags_placeholders(s: str) -> str:
+    s = re.sub(r'⟦TAG\d+⟧', '', s)
+    s = re.sub(r'</?[^>\n\r]+?>', '', s)
+    return s.strip()
+
+def looks_like_sentence_end(text: str) -> bool:
+    t = _strip_tags_placeholders(text)
+    if not t:
+        return True
+    if _END_PUNCT_RE.search(t):
+        return True
+    if _CONTINUATION_HINT_RE.search(t):
+        return False
+    return False
+
+# ===================== Safe splitting & cleanups =====================
+def _nearest_boundary(text: str, approx_idx: int) -> int:
+    if not text:
+        return 0
+    n = len(text)
+    approx_idx = max(1, min(n-1, approx_idx))
+    left = approx_idx
+    right = approx_idx
+    while left > 0 or right < n:
+        if left > 0 and (text[left-1].isspace() or not text[left-1].isalnum() or not text[left].isalnum()):
+            return left
+        if right < n and (text[right-1].isspace() or not text[right-1].isalnum() or not text[right].isalnum()):
+            return right
+        left -= 1
+        right += 1
+    return approx_idx
+
+def _split_safely(text: str, parts: int) -> List[str]:
+    text = text.strip()
+    if parts <= 1:
+        return [text]
+    total = len(text)
+    out = []
+    start = 0
+    for i in range(parts - 1):
+        approx = int((total / parts) * (i + 1))
+        idx = _nearest_boundary(text, approx_idx=approx)
+        piece = text[start:idx].strip()
+        out.append(piece)
+        start = idx
+    out.append(text[start:].strip())
+    cleaned = []
+    for p in out:
+        if p == "" and cleaned:
+            cleaned[-1] = (cleaned[-1] + " ").strip()
+        else:
+            cleaned.append(p)
+    return cleaned if cleaned else [""]
+
+def _fix_midword_hyphen(s: str) -> str:
+    s = s.replace("\u00AD", "")
+    s = re.sub(r"-\s+\b", "", s)
+    return s
+
+# ----- Marker normalization & splitting (UPPERCASE ONLY) -----
+_WRAPPERS = r"""["'“”„»«\[\]\{\}\(\)<>\s]*"""
+BR_UP_RE  = re.compile(rf"(?<![A-Za-z]){_WRAPPERS}BR{_WRAPPERS}(?![A-Za-z])")
+CUE_UP_RE = re.compile(rf"(?<![A-Za-z]){_WRAPPERS}CUE{_WRAPPERS}(?![A-Za-z])")
+
+def _strip_unk_tokens(s: str) -> str:
+    s = re.sub(r"<\s*unk[^>]*>?", "", s, flags=re.IGNORECASE)
+    return s
+
+def _normalize_markers(s: str) -> str:
+    s = _strip_unk_tokens(s)
+    s = BR_UP_RE.sub(f" {SENTINEL_BR} ", s)
+    s = CUE_UP_RE.sub(f" {SENTINEL_CUE} ", s)
+    return s
+
+def _split_on_br_normalized(s: str) -> List[str]:
+    parts = [p.strip() for p in s.split(SENTINEL_BR)]
+    return [p for p in parts if p != ""]
+
+def _split_on_cue_normalized(s: str) -> List[str]:
+    parts = [p.strip() for p in s.split(SENTINEL_CUE)]
+    return [p for p in parts if p != ""]
+
+# ----- Leading punctuation fix (iterate until stable; includes Unicode commas) -----
+_LEADING_PUNCT_RE = re.compile(
+    r"""^\s*([,.;:!?…，、；：]+|[’'”")\]\}》〉」』】〕］）]+)\s*""", re.UNICODE
+)
+
+def _fix_leading_punct(lines: List[str]) -> List[str]:
+    if not lines:
+        return lines
+    out = lines[:]
+    changed = True
+    while changed:
+        changed = False
+        for i in range(1, len(out)):
+            m = _LEADING_PUNCT_RE.match(out[i])
+            if m:
+                token = m.group(1)
+                out[i-1] = (out[i-1].rstrip() + token).rstrip()
+                out[i] = out[i][m.end():].lstrip()
+                changed = True
+    return out
+
+# ----- Spurious brackets removal & html unescape -----
+def _unescape_and_strip_artifacts(text: str) -> str:
+    s = _html.unescape(text)
+    s = s.replace("⟦", "").replace("⟧", "")
+    s = s.replace("\u00A0", " ").replace("\u202F", " ").replace("\u3000", " ")
+    s = re.sub(r"[ \t]+", " ", s)
+    return s.strip()
+
+def _strip_spurious_pairs(orig: str, trans: str) -> str:
+    out = trans
+    if "[]" in out and "[]" not in orig:
+        out = out.replace("[]", "")
+    if "{}" in out and "{}" not in orig:
+        out = out.replace("{}", "")
+    return out.strip()
+
+# ----- Single-word repetition squash (SeamlessM4T only) -----
+_WORD_RE = re.compile(r"\b\w+\b", flags=re.UNICODE)
+_REPEAT_LINE_RE = re.compile(r"^\s*(\w+)(?:\W+\1\b){2,}\s*$", flags=re.IGNORECASE | re.UNICODE)
+
+def _is_one_word_line(s: str) -> bool:
+    s2 = _strip_tags_placeholders(s)
+    return len(_WORD_RE.findall(s2)) == 1
+
+def _squelch_single_word_repeat_if_needed(orig_line: str, translated_line: str) -> str:
+    if not _is_one_word_line(orig_line):
+        return translated_line
+    m = _REPEAT_LINE_RE.match(translated_line)
+    if m:
+        return m.group(1)
+    words = _WORD_RE.findall(translated_line)
+    if words:
+        lowered = [w.lower() for w in words]
+        if len(set(lowered)) == 1 and len(lowered) >= 5:
+            return words[0]
+    return translated_line
+
+# ===================== NEW: marker/token removal at per-line finalization =====================
+_MARKER_TOKEN_RE = re.compile(
+    r'(?:(?<=^)|(?<=\s))'
+    r'(?:["\'“”‘’])?'
+    r'\S*?(?:BR|CUE)\S*?'
+    r'(?:["\'“”‘’])?'
+    r'(?:\s*,)?'
+    r'(?=(?:\s|$))'
+)
+
+_TAGN_TOKEN_RE = re.compile(
+    r'(?:(?<=^)|(?<=\s))'
+    r'(?:["\'“”‘’])?'
+    r'\S*?TAG[0-9]\S*?'
+    r'(?:["\'“”‘’])?'
+    r'(?:\s*,)?'
+    r'(?=(?:\s|$))',
+    flags=re.IGNORECASE
+)
+
+def _remove_marker_and_tagn_tokens(line: str) -> str:
+    out = line
+    for _ in range(3):
+        new = _MARKER_TOKEN_RE.sub("", out)
+        new = _TAGN_TOKEN_RE.sub("", new)
+        if new == out:
+            break
+        out = new
+    out = re.sub(r"\s+,", ",", out)
+    out = re.sub(r"\s+([.;:!?…，、；：])", r"\1", out)
+    out = re.sub(r"[ \t]{2,}", " ", out)
+    out = re.sub(r"^\s*[,，、]+", "", out)
+    return out.strip()
+
+# ===================== Device selection (NLLB & Seamless) =====================
+def pick_device_for_workers(workers: int, device_mode: str):
+    workers = max(1, workers)
+    device_mode = (device_mode or "auto").lower()
+
+    has_mps = has_cuda = has_hip = False
+    try:
+        import torch
+        has_mps = getattr(torch.backends, "mps", None) and torch.backends.mps.is_available()
+        has_cuda = torch.cuda.is_available()
+        has_hip = getattr(torch.version, "hip", None) is not None
+    except Exception:
+        pass
+
+    is_mac = (sys.platform == "darwin")
+
+    def _cpu():
+        return {"device": -1}, workers
+
+    def _mps():
+        return {"device": "mps"}, 1
+
+    def _cuda_or_rocm():
+        return {"device": 0}, 1
+
+    if device_mode == "cpu":
+        return _cpu()
+
+    if device_mode == "gpu":
+        if is_mac and has_mps:
+            return _mps()
+        if has_cuda or has_hip:
+            return _cuda_or_rocm()
+        return _cpu()
+
+    if is_mac:
+        if has_mps:
+            return _mps()
+        if has_cuda or has_hip:
+            return _cuda_or_rocm()
+        return _cpu()
+    else:
+        if has_cuda or has_hip:
+            return _cuda_or_rocm()
+        if has_mps:
+            return _mps()
+        return _cpu()
+
+# ===================== NLLB via Transformers =====================
+def get_nllb_translator(model_name: str, src_code: str, tgt_code: str, device_kwargs: dict):
+    try:
+        from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline as _pipeline
+        import torch  # noqa
+    except Exception:
+        from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+        from transformers.pipelines import pipeline as _pipeline
+        import torch  # noqa
+
+    tok = AutoTokenizer.from_pretrained(model_name)
+    mdl = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    mdl.eval()
+    return _pipeline("translation", model=mdl, tokenizer=tok,
+                     src_lang=src_code, tgt_lang=tgt_code, **device_kwargs)
+
+def ensure_model_downloaded(repo_id: str, revision: str = "main", tqdm_class=None):
+    from huggingface_hub import snapshot_download
+    snapshot_download(repo_id=repo_id, revision=revision, tqdm_class=tqdm_class)
+
+# ===================== SeamlessM4T (text-to-text) =====================
+def _to_seamless_lang(nllb_code: str) -> str:
+    if not nllb_code:
+        return "eng"
+    return nllb_code.split("_", 1)[0]
+
+def get_seamless_translator(model_name: str, src_code: str, tgt_code: str, device_kwargs: dict):
+    import torch
+    from transformers import AutoProcessor, SeamlessM4Tv2ForTextToText
+
+    processor = AutoProcessor.from_pretrained(model_name)
+    model = SeamlessM4Tv2ForTextToText.from_pretrained(model_name)
+    model.eval()
+    dev = device_kwargs.get("device", -1)
+
+    if dev == "mps":
+        device = torch.device("mps")
+    elif isinstance(dev, int) and dev >= 0 and torch.cuda.is_available():
+        device = torch.device(f"cuda:{dev}")
+    else:
+        device = torch.device("cpu")
+    model.to(device)
+
+    src3 = _to_seamless_lang(src_code)
+    tgt3 = _to_seamless_lang(tgt_code)
+
+    def _translate(batch_texts: List[str]) -> List[str]:
+        texts = [" " if (t is None or str(t).strip() == "") else str(t) for t in batch_texts]
+        inputs = processor(text=texts, src_lang=src3, return_tensors="pt", padding=True)
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        import torch as _torch
+        with _torch.no_grad():
+            out = model.generate(**inputs, tgt_lang=tgt3)
+        seqs = getattr(out, "sequences", out)
+        decoded = processor.batch_decode(seqs, skip_special_tokens=True)
+        return decoded
+
+    return _translate
+
+def ensure_seamless_downloaded(repo_id: str = "facebook/seamless-m4t-v2-large", revision: str = "main", tqdm_class=None):
+    from huggingface_hub import snapshot_download
+    snapshot_download(repo_id=repo_id, revision=revision, tqdm_class=tqdm_class)
+
+# ===================== Ollama JSON chat (robust) =====================
+def _ollama_system_prompt():
+    return (
+        "You are a professional subtitle translator.\n"
+        "Return STRICT JSON ONLY. No Markdown, no code fences, no commentary.\n"
+        "CRITICAL RULES:\n"
+        "1) Preserve the exact array shapes and counts provided (lines per cue, cues per group).\n"
+        "2) Do NOT add/remove/swap items; do NOT merge or split lines.\n"
+        "3) Keep placeholder tokens like ⟦TAG0⟧ EXACTLY unchanged.\n"
+        "4) Do not hyphenate or break words across lines.\n"
+        "5) Keep numbers and time references as-is.\n"
+        "6) Unless a string is a proper name/brand/code or already in the target language, you MUST translate it.\n"
+        "Reply with pure JSON conforming to the requested schema.\n"
+    )
+
+def _strip_code_fences(s: str) -> str:
+    s = s.strip()
+    if s.startswith("```"):
+        first = s.find("```")
+        if first != -1:
+            s = s[first+3:]
+            if "\n" in s:
+                s = s.split("\n", 1)[1]
+    if s.rstrip().endswith("```"):
+        s = s.rstrip()[:-3]
+    return s.strip()
+
+def _extract_balanced_json(s: str):
+    import json as _json
+    s = s.strip()
+    try:
+        return _json.loads(s)
+    except Exception:
+        pass
+    s = _strip_code_fences(s)
+    try:
+        return _json.loads(s)
+    except Exception:
+        pass
+
+    start_obj = s.find("{")
+    start_arr = s.find("[")
+    if start_obj == -1 and start_arr == -1:
+        raise ValueError("No JSON start found")
+    start = min([i for i in [start_obj, start_arr] if i != -1])
+    kind = "obj" if start == start_obj else "arr"
+
+    depth_brace = depth_bracket = 0
+    in_string = False
+    esc = False
+    for i in range(start, len(s)):
+        ch = s[i]
+        if in_string:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
+            elif ch == '"':
+                in_string = False
+        else:
+            if ch == '"':
+                in_string = True
+            elif ch == "{":
+                depth_brace += 1
+            elif ch == "}":
+                depth_brace -= 1
+                if kind == "obj" and depth_brace == 0 and depth_bracket == 0:
+                    frag = s[start:i+1]
+                    try:
+                        return _json.loads(frag)
+                    except Exception:
+                        break
+            elif ch == "[":
+                depth_bracket += 1
+            elif ch == "]":
+                depth_bracket -= 1
+                if kind == "arr" and depth_bracket == 0 and depth_brace == 0:
+                    frag = s[start:i+1]
+                    try:
+                        return _json.loads(frag)
+                    except Exception:
+                        break
+    frag = s[start:]
+    frag = re.sub(r",(\s*[}\]])", r"\1", frag)
+    return _json.loads(frag)
+
+def ollama_chat_json(
+    model: str,
+    system_prompt: str,
+    user_prompt: str,
+    host: str = "localhost",
+    port: int = 11434,
+    temperature: float = 0.2,
+    max_retries: int = 4,
+    timeout: int = 600,
+    cancel_event: Optional[threading.Event] = None,
+):
+    last_err = None
+    for attempt in range(max_retries):
+        if cancel_event is not None and cancel_event.is_set():
+            raise RuntimeError("Cancelled")
+        prompt = user_prompt if attempt == 0 else (
+            user_prompt + "\n\nSTRICT RETRY: Previous reply was NOT valid JSON. "
+                          "Respond with JSON ONLY (no code fences), matching the schema and counts exactly."
+        )
+        body = json.dumps({
+            "model": model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt}
+            ],
+            "format": "json",
+            "stream": False,
+            "options": {"temperature": temperature}
+        })
+        try:
+            conn = http.client.HTTPConnection(host, port, timeout=timeout)
+            conn.request("POST", "/api/chat", body=body, headers={"Content-Type": "application/json"})
+            resp = conn.getresponse()
+            data = resp.read()
+            conn.close()
+            if resp.status != 200:
+                last_err = RuntimeError(f"Ollama HTTP {resp.status}: {data[:200]}")
+                time.sleep(1.1 * (attempt + 1))
+                continue
+            j = json.loads(data.decode("utf-8"))
+            content = j.get("message", {}).get("content", "")
+            try:
+                return json.loads(content)
+            except Exception:
+                return _extract_balanced_json(content)
+        except Exception as e:
+            last_err = e
+            if cancel_event is not None and cancel_event.is_set():
+                raise RuntimeError("Cancelled")
+            time.sleep(1.1 * (attempt + 1))
+    raise last_err or RuntimeError("Ollama call failed")
+
+# ===================== Ollama prompt builders (UPDATED) =====================
+def _ollama_user_prompt_lines(target_lang: str, src_hint: str, lines: List[str], force: bool=False) -> str:
+    tgt_name = _lang_name_for_prompt(target_lang)
+    src_name = _lang_name_for_prompt(src_hint)
+    rules = [
+        f"Translate each item to {tgt_name}.",
+        f"Source language hint: {src_name}.",
+        "Preserve array length exactly as provided.",
+        "No Markdown, no code fences, return JSON only."
+    ]
+    if force:
+        rules.append(
+            f"If an item is not already in {tgt_name} and contains translatable words, you MUST translate it; "
+            f"do NOT return the source sentence unchanged (proper names/brands/codes may remain)."
+        )
+    payload = {
+        "task": "translate_lines",
+        "target_language": tgt_name,
+        "source_language_hint": src_name,
+        "schema": {"lines": ["<string>", "..."]},
+        "expected_counts": {"lines": len(lines)},
+        "rules": rules,
+        "lines": lines,
+    }
+    return json.dumps(payload, ensure_ascii=False)
+
+def _ollama_user_prompt_cues(target_lang: str, src_hint: str, cues: List[List[str]]) -> str:
+    tgt_name = _lang_name_for_prompt(target_lang)
+    src_name = _lang_name_for_prompt(src_hint)
+    payload = {
+        "task": "translate_cues",
+        "target_language": tgt_name,
+        "source_language_hint": src_name,
+        "schema": {"cues": [["<string>", "..."]]},
+        "expected_counts": {"cues": [len(c) for c in cues]},
+        "rules": [
+            f"Translate to {tgt_name}. Source language hint: {src_name}.",
+            "Preserve lines per cue exactly.",
+            "No Markdown, no code fences, return JSON only."
+        ],
+        "cues": cues,
+    }
+    return json.dumps(payload, ensure_ascii=False)
+
+def _ollama_user_prompt_groups(target_lang: str, src_hint: str, groups: List[List[List[str]]]) -> str:
+    tgt_name = _lang_name_for_prompt(target_lang)
+    src_name = _lang_name_for_prompt(src_hint)
+    payload = {
+        "task": "translate_groups",
+        "target_language": tgt_name,
+        "source_language_hint": src_name,
+        "schema": {"groups": [[["<string>", "..."]]]},
+        "expected_counts": {"groups": [[len(cue) for cue in group] for group in groups]},
+        "rules": [
+            f"Translate to {tgt_name}. Source language hint: {src_name}.",
+            "Preserve cues per group and lines per cue exactly.",
+            "No Markdown, no code fences, return JSON only."
+        ],
+        "groups": groups,
+    }
+    return json.dumps(payload, ensure_ascii=False)
+
+# ---------- Ollama strict retry for lines (NEW) ----------
+def _ollama_retry_translate_lines(model: str, host: str, port: int,
+                                  target_language: str, src_hint: str,
+                                  masked_lines: List[str],
+                                  cancel_event: Optional[threading.Event] = None) -> List[str]:
+    if not masked_lines:
+        return []
+
+    sys_prompt = _ollama_system_prompt()
+    models = [m.strip() for m in str(model).split(",") if m.strip()] or [model]
+    temps = (0.1, 0.3, 0.0)
+
+    for T in temps:
+        for m in models:
+            if cancel_event is not None and cancel_event.is_set():
+                return masked_lines
+            try:
+                prompt = _ollama_user_prompt_lines(target_language, src_hint, masked_lines, force=True)
+                obj = ollama_chat_json(m, sys_prompt, prompt, host=host, port=port,
+                                       temperature=T, max_retries=3, cancel_event=cancel_event)
+                lines = obj.get("lines", [])
+                if isinstance(lines, list) and len(lines) == len(masked_lines):
+                    return lines
+            except Exception:
+                continue
+    return masked_lines
+
+# ===================== Builders =====================
+def build_line_items(cue_lines: List[List[str]]) -> Tuple[List[str], List[Dict[str, str]]]:
+    flat_masked: List[str] = []
+    masks: List[Dict[str, str]] = []
+    for lines in cue_lines:
+        for ln in lines:
+            masked, ph = protect_tags(ln)
+            flat_masked.append(masked)
+            masks.append(ph)
+    return flat_masked, masks
+
+def build_cue_items_for_nllb(cue_lines: List[List[str]]) -> Tuple[List[str], List[Dict[str, str]], List[int]]:
+    joined: List[str] = []
+    masks: List[Dict[str, str]] = []
+    counts: List[int] = []
+    for lines in cue_lines:
+        combined = f"\n{SENTINEL_BR}\n".join(lines) if lines else ""
+        masked, ph = protect_tags(combined)
+        joined.append(masked)
+        masks.append(ph)
+        counts.append(len(lines))
+    return joined, masks, counts
+
+def build_cue_items_for_llm(cue_lines: List[List[str]]) -> Tuple[List[List[str]], List[List[Dict[str, str]]]]:
+    cues_text: List[List[str]] = []
+    cues_masks: List[List[Dict[str, str]]] = []
+    for lines in cue_lines:
+        masked_lines, masks = [], []
+        for ln in lines:
+            m, ph = protect_tags(ln)
+            masked_lines.append(m)
+            masks.append(ph)
+        cues_text.append(masked_lines)
+        cues_masks.append(masks)
+    return cues_text, cues_masks
+
+def build_smart_groups_for_nllb(cue_lines: List[List[str]], max_span_cues: int = 4
+                       ) -> Tuple[List[str], List[Dict[str, str]], List[List[int]]]:
+    n = len(cue_lines)
+    groups_text: List[str] = []
+    groups_masks: List[Dict[str, str]] = []
+    groups_counts: List[List[int]] = []
+
+    i = 0
+    while i < n:
+        group_cues = []
+        counts = []
+        span = 0
+        while i < n and span < max_span_cues:
+            lines = cue_lines[i]
+            counts.append(len(lines))
+            cue_text = f"\n{SENTINEL_BR}\n".join(lines)
+            group_cues.append(cue_text)
+            if looks_like_sentence_end("\n".join(lines)):
+                i += 1
+                break
+            span += 1
+            i += 1
+        combined_group = f"\n{SENTINEL_CUE}\n".join(group_cues)
+        masked, ph = protect_tags(combined_group)
+        groups_text.append(masked)
+        groups_masks.append(ph)
+        groups_counts.append(counts)
+    return groups_text, groups_masks, groups_counts
+
+def build_smart_groups_for_llm(cue_lines: List[List[str]], max_span_cues: int = 4
+                       ) -> Tuple[List[List[List[str]]], List[List[List[Dict[str,str]]]], List[List[int]]]:
+    n = len(cue_lines)
+    groups_text: List[List[List[str]]] = []
+    groups_masks: List[List[List[Dict[str,str]]]] = []
+    groups_counts: List[List[int]] = []
+
+    i = 0
+    while i < n:
+        group_cues: List[List[str]] = []
+        group_masks: List[List[Dict[str,str]]] = []
+        counts: List[int] = []
+        span = 0
+        while i < n and span < max_span_cues:
+            lines = cue_lines[i]
+            counts.append(len(lines))
+            masked_lines, masks = [], []
+            for ln in lines:
+                m, ph = protect_tags(ln)
+                masked_lines.append(m)
+                masks.append(ph)
+            group_cues.append(masked_lines)
+            group_masks.append(masks)
+            if looks_like_sentence_end("\n".join(lines)):
+                i += 1
+                break
+            span += 1
+            i += 1
+        groups_text.append(group_cues)
+        groups_masks.append(group_masks)
+        groups_counts.append(counts)
+    return groups_text, groups_masks, groups_counts
+
+# ===================== Queue helper =====================
+def _drain_queue(q: "queue.Queue"):
+    while True:
+        try:
+            _ = q.get_nowait()
+            q.task_done()
+        except queue.Empty:
+            break
+
+# ===================== Translation workers (NLLB) =====================
+def nllb_translate_lines(flat_lines, masks, model_name, src_code, tgt_code,
+                         workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(flat_lines)
+    results: List[Optional[str]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [flat_lines[j] for j in idx_chunk]
+        batch_masks = [masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_nllb_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 8
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip() == "" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    i = sub_idx[pos]
+                    restored = restore_tags(out["translation_text"], sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    results[i] = _fix_midword_hyphen(restored)
+                    if on_progress: on_progress(1)
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else "" for r in results]
+
+def nllb_translate_cues(flat_cues, cue_masks, counts, model_name, src_code, tgt_code,
+                        workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(flat_cues)
+    results: List[Optional[List[str]]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [flat_cues[j] for j in idx_chunk]
+        batch_masks = [cue_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_nllb_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 6
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip()=="" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    i = sub_idx[pos]
+                    restored = restore_tags(out["translation_text"], sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    n_lines = counts[i]
+                    lines = resplit_translated_cue_text(restored, n_lines)
+                    results[i] = _fix_leading_punct([_fix_midword_hyphen(p) for p in lines])
+                    if on_progress: on_progress(n_lines)
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [""]*c for r,c in zip(results, counts)]
+
+def nllb_translate_groups(groups_text, groups_masks, groups_counts, model_name, src_code, tgt_code,
+                          workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(groups_text)
+    results: List[Optional[List[List[str]]]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [groups_text[j] for j in idx_chunk]
+        batch_masks = [groups_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_nllb_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 3
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip()=="" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    gi = sub_idx[pos]
+                    restored = restore_tags(out["translation_text"], sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    cue_chunks = _split_on_cue_normalized(restored)
+                    counts = groups_counts[gi]
+                    if len(cue_chunks) != len(counts):
+                        merged = " ".join(cue_chunks).strip()
+                        cue_chunks = _split_safely(merged, len(counts))
+                    rebuilt_cues = []
+                    for chunk, n_lines in zip(cue_chunks, counts):
+                        lines = resplit_translated_cue_text(chunk, n_lines)
+                        rebuilt_cues.append(_fix_leading_punct([_fix_midword_hyphen(p) for p in lines]))
+                    results[gi] = rebuilt_cues
+                    if on_progress: on_progress(sum(counts))
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [[""]*n for n in counts] for r,counts in zip(results, groups_counts)]
+
+# ===================== Translation workers (SeamlessM4T) =====================
+def seamless_translate_lines(flat_lines, masks, model_name, src_code, tgt_code,
+                             workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(flat_lines)
+    results: List[Optional[str]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [flat_lines[j] for j in idx_chunk]
+        batch_masks = [masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_seamless_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 8
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip()=="" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    i = sub_idx[pos]
+                    restored = restore_tags(out, sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    results[i] = _fix_midword_hyphen(restored)
+                    if on_progress: on_progress(1)
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else "" for r in results]
+
+def seamless_translate_cues(flat_cues, cue_masks, counts, model_name, src_code, tgt_code,
+                            workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(flat_cues)
+    results: List[Optional[List[str]]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [flat_cues[j] for j in idx_chunk]
+        batch_masks = [cue_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_seamless_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 6
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip()=="" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    i = sub_idx[pos]
+                    restored = restore_tags(out, sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    n_lines = counts[i]
+                    lines = resplit_translated_cue_text(restored, n_lines)
+                    results[i] = _fix_leading_punct([_fix_midword_hyphen(p) for p in lines])
+                    if on_progress: on_progress(n_lines)
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [""]*c for r,c in zip(results, counts)]
+
+def seamless_translate_groups(groups_text, groups_masks, groups_counts, model_name, src_code, tgt_code,
+                              workers, batch_size, on_progress, pause_event_unused, cancel_event=None, device_kwargs=None):
+    n = len(groups_text)
+    results: List[Optional[List[List[str]]]] = [None] * n
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        masked_batch = [groups_text[j] for j in idx_chunk]
+        batch_masks = [groups_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, masked_batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        if cancel_event is not None and cancel_event.is_set():
+            _drain_queue(q); return
+        translator = get_seamless_translator(model_name, src_code, tgt_code, device_kwargs or {"device": -1})
+        micro = 3
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, masked_batch, batch_masks = item
+            to_xlate_full = [" " if s.strip()=="" else s for s in masked_batch]
+            for s in range(0, len(to_xlate_full), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_in = to_xlate_full[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+                outs = translator(sub_in)
+                for pos, out in enumerate(outs):
+                    gi = sub_idx[pos]
+                    restored = restore_tags(out, sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    cue_chunks = _split_on_cue_normalized(restored)
+                    counts = groups_counts[gi]
+                    if len(cue_chunks) != len(counts):
+                        merged = " ".join(cue_chunks).strip()
+                        cue_chunks = _split_safely(merged, len(counts))
+                    rebuilt_cues = []
+                    for chunk, n_lines in zip(cue_chunks, counts):
+                        lines = resplit_translated_cue_text(chunk, n_lines)
+                        rebuilt_cues.append(_fix_leading_punct([_fix_midword_hyphen(p) for p in lines]))
+                    results[gi] = rebuilt_cues
+                    if on_progress: on_progress(sum(counts))
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [[""]*n for n in counts] for r,counts in zip(results, groups_counts)]
+
+# ===================== Ollama workers (UPDATED with Repair-Pass & cancel-aware) =====================
+def ollama_translate_lines(flat_lines, masks, target_language, src_code, model, host, port,
+                           workers, batch_size, on_progress, pause_event_unused, cancel_event=None):
+    n = len(flat_lines)
+    results: List[Optional[str]] = [None] * n
+    tgt_code = norm_lang_to_code(target_language, default=None)
+
+    q: "queue.Queue[Optional[Tuple[List[int], List[str], List[Dict[str,str]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        batch = [flat_lines[j] for j in idx_chunk]
+        batch_masks = [masks[j] for j in idx_chunk]
+        q.put((idx_chunk, batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        micro = 8
+        sys_prompt = _ollama_system_prompt()
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, batch, batch_masks = item
+
+            # micro-batched calls to limit blocking time
+            for s in range(0, len(batch), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_batch = batch[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+
+                prompt = _ollama_user_prompt_lines(target_language, src_code, sub_batch, force=False)
+                out_obj = ollama_chat_json(model, sys_prompt, prompt, host=host, port=port,
+                                           temperature=0.1, cancel_event=cancel_event)
+                lines_out = out_obj.get("lines", [])
+                if not isinstance(lines_out, list) or len(lines_out) != len(sub_batch):
+                    raise RuntimeError("Ollama returned invalid lines array.")
+
+                # Repair pass
+                to_fix_idx: List[int] = []
+                to_fix_src: List[str] = []
+                for pos, t in enumerate(lines_out):
+                    src_masked = sub_batch[pos]
+                    if _looks_untranslated(src_masked, t, src_code, tgt_code):
+                        to_fix_idx.append(pos); to_fix_src.append(src_masked)
+                if to_fix_src:
+                    fixed = _ollama_retry_translate_lines(model, host, port, target_language, src_code, to_fix_src, cancel_event=cancel_event)
+                    for k, new_t in enumerate(fixed):
+                        lines_out[to_fix_idx[k]] = new_t
+
+                # restore + finalize
+                for pos, t in enumerate(lines_out):
+                    i = sub_idx[pos]
+                    restored = restore_tags(t, sub_masks[pos])
+                    restored = _normalize_markers(restored)
+                    restored = _unescape_and_strip_artifacts(restored)
+                    results[i] = _fix_midword_hyphen(restored)
+                    if on_progress: on_progress(1)
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else "" for r in results]
+
+def ollama_translate_cues(cues_text, cues_masks, target_language, src_code, model, host, port,
+                          workers, batch_size, on_progress, pause_event_unused, cancel_event=None):
+    n = len(cues_text)
+    results: List[Optional[List[str]]] = [None] * n
+    tgt_code = norm_lang_to_code(target_language, default=None)
+
+    q: "queue.Queue[Optional[Tuple[List[int], List[List[str]], List[List[Dict[str,str]]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        batch = [cues_text[j] for j in idx_chunk]
+        batch_masks = [cues_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        micro = 6
+        sys_prompt = _ollama_system_prompt()
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+            idx_chunk, batch, batch_masks = item
+
+            for s in range(0, len(batch), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_batch = batch[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+
+                prompt = _ollama_user_prompt_cues(target_language, src_code, sub_batch)
+                out_obj = ollama_chat_json(model, sys_prompt, prompt, host=host, port=port,
+                                           temperature=0.1, cancel_event=cancel_event)
+                cues_out = out_obj.get("cues", [])
+                if (not isinstance(cues_out, list)) or (len(cues_out) != len(sub_batch)):
+                    raise RuntimeError("Ollama returned invalid cues array.")
+
+                retry_pairs: List[Tuple[int,int]] = []
+                retry_texts: List[str] = []
+                for pos, cue_lines_trans in enumerate(cues_out):
+                    masks_per_line = sub_masks[pos]
+                    if len(cue_lines_trans) != len(masks_per_line):
+                        merged = " ".join(cue_lines_trans)
+                        cue_lines_trans = _split_safely(merged, len(masks_per_line))
+                        cues_out[pos] = cue_lines_trans
+                    for li, t in enumerate(cue_lines_trans):
+                        src_masked = sub_batch[pos][li]
+                        if _looks_untranslated(src_masked, t, src_code, tgt_code):
+                            retry_pairs.append((pos, li))
+                            retry_texts.append(src_masked)
+
+                if retry_texts:
+                    fixed = _ollama_retry_translate_lines(model, host, port, target_language, src_code, retry_texts, cancel_event=cancel_event)
+                    for (pos, li), new_t in zip(retry_pairs, fixed):
+                        cues_out[pos][li] = new_t
+
+                for pos, cue_lines_trans in enumerate(cues_out):
+                    masks_per_line = sub_masks[pos]
+                    rebuilt = []
+                    for ln, ph in zip(cue_lines_trans, masks_per_line):
+                        t = restore_tags(ln, ph)
+                        t = _normalize_markers(t)
+                        t = _unescape_and_strip_artifacts(t)
+                        rebuilt.append(_fix_midword_hyphen(t))
+                    i = sub_idx[pos]
+                    results[i] = _fix_leading_punct(rebuilt)
+                    if on_progress: on_progress(len(rebuilt))
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [""]*len(cues_text[idx]) for idx,r in enumerate(results)]
+
+def ollama_translate_groups(groups_text, groups_masks, groups_counts, target_language, src_code,
+                            model, host, port, workers, batch_size, on_progress, pause_event_unused, cancel_event=None):
+    n = len(groups_text)
+    results: List[Optional[List[List[str]]]] = [None] * n
+    tgt_code = norm_lang_to_code(target_language, default=None)
+
+    q: "queue.Queue[Optional[Tuple[List[int], List[List[List[str]]], List[List[List[Dict[str,str]]]]]]]" = queue.Queue()
+    for i in range(0, n, batch_size):
+        idx_chunk = list(range(i, min(n, i+batch_size)))
+        batch = [groups_text[j] for j in idx_chunk]
+        batch_masks = [groups_masks[j] for j in idx_chunk]
+        q.put((idx_chunk, batch, batch_masks))
+    for _ in range(workers):
+        q.put(None)
+
+    def worker_main():
+        micro = 4
+        sys_prompt = _ollama_system_prompt()
+        while True:
+            if cancel_event is not None and cancel_event.is_set():
+                _drain_queue(q); break
+            try:
+                item = q.get(timeout=0.2)
+            except queue.Empty:
+                continue
+            if item is None:
+                q.task_done(); break
+            if cancel_event is not None and cancel_event.is_set():
+                q.task_done(); _drain_queue(q); break
+
+            idx_chunk, batch, batch_masks = item
+            for s in range(0, len(batch), micro):
+                if cancel_event is not None and cancel_event.is_set():
+                    q.task_done(); _drain_queue(q); return
+                sub_idx = idx_chunk[s:s+micro]
+                sub_batch = batch[s:s+micro]
+                sub_masks = batch_masks[s:s+micro]
+
+                prompt = _ollama_user_prompt_groups(target_language, src_code, sub_batch)
+                out_obj = ollama_chat_json(model, sys_prompt, prompt, host=host, port=port,
+                                           temperature=0.1, cancel_event=cancel_event)
+                groups_out = out_obj.get("groups", [])
+
+                if (not isinstance(groups_out, list)) or (len(groups_out) != len(sub_batch)):
+                    groups_out = []
+                    for group in sub_batch:
+                        prompt2 = _ollama_user_prompt_cues(target_language, src_code, group)
+                        try:
+                            obj2 = ollama_chat_json(model, sys_prompt, prompt2, host=host, port=port,
+                                                    temperature=0.1, cancel_event=cancel_event)
+                            cues_out2 = obj2.get("cues", [])
+                            if not isinstance(cues_out2, list) or len(cues_out2) != len(group):
+                                cues_out2 = group
+                        except Exception:
+                            cues_out2 = group
+                        groups_out.append(cues_out2)
+
+                retry_triples: List[Tuple[int,int,int]] = []
+                retry_texts: List[str] = []
+
+                for pos, group_out in enumerate(groups_out):
+                    masks_for_group = sub_masks[pos]
+                    if len(group_out) != len(masks_for_group):
+                        merged_cues_text = [" ".join(c) if isinstance(c, list) else str(c) for c in group_out]
+                        merged_all = " ".join(merged_cues_text)
+                        split_cues = _split_safely(merged_all, len(masks_for_group))
+                        coerced_group = []
+                        for chunk, line_masks in zip(split_cues, masks_for_group):
+                            coerced_group.append(_split_safely(chunk, len(line_masks)))
+                        group_out = coerced_group
+                        groups_out[pos] = group_out
+
+                    for ci, (cue_out, line_masks) in enumerate(zip(group_out, masks_for_group)):
+                        if len(cue_out) != len(line_masks):
+                            merged = " ".join(cue_out if isinstance(cue_out, list) else [str(cue_out)])
+                            cue_out = _split_safely(merged, len(line_masks))
+                            group_out[ci] = cue_out
+                        for li, ln in enumerate(cue_out):
+                            src_masked = sub_batch[pos][ci][li]
+                            if _looks_untranslated(src_masked, ln, src_code, tgt_code):
+                                retry_triples.append((pos, ci, li))
+                                retry_texts.append(src_masked)
+
+                if retry_texts:
+                    fixed = _ollama_retry_translate_lines(model, host, port, target_language, src_code, retry_texts, cancel_event=cancel_event)
+                    for (pos, ci, li), new_t in zip(retry_triples, fixed):
+                        groups_out[pos][ci][li] = new_t
+
+                for pos, group_out in enumerate(groups_out):
+                    masks_for_group = sub_masks[pos]
+                    rebuilt_group: List[List[str]] = []
+                    for cue_out, line_masks in zip(group_out, masks_for_group):
+                        rebuilt_lines = []
+                        for ln, ph in zip(cue_out, line_masks):
+                            t = restore_tags(ln, ph)
+                            t = _normalize_markers(t)
+                            t = _unescape_and_strip_artifacts(t)
+                            rebuilt_lines.append(_fix_midword_hyphen(t))
+                        rebuilt_group.append(_fix_leading_punct(rebuilt_lines))
+                    gi = sub_idx[pos]
+                    results[gi] = rebuilt_group
+                    if on_progress: on_progress(sum(len(x) for x in rebuilt_group))
+            q.task_done()
+
+    threads = [threading.Thread(target=worker_main, daemon=True) for _ in range(workers)]
+    [t.start() for t in threads]; q.join()
+    if cancel_event is not None and cancel_event.is_set():
+        raise RuntimeError("Cancelled")
+    return [r if r is not None else [[""]*n for n in counts] for r,counts in zip(results, groups_counts)]
+
+# ===================== Rebuild helpers =====================
+def resplit_translated_cue_text(translated: str, n_lines: int) -> List[str]:
+    normalized = _normalize_markers(translated)
+    parts = _split_on_br_normalized(normalized)
+    if len(parts) == n_lines:
+        return [_fix_midword_hyphen(p) for p in parts]
+
+    nl_parts = [ln.strip() for ln in re.split(r"\r?\n", normalized) if ln.strip()]
+    if len(nl_parts) == n_lines:
+        return [_fix_midword_hyphen(p) for p in nl_parts]
+
+    merged = " ".join(parts if parts else [normalized]).strip()
+    return [_fix_midword_hyphen(p) for p in _split_safely(merged, n_lines)]
+
+def _postprocess_line(orig_line: str, trans_line: str, squelch_for_seamless: bool=False) -> str:
+    t = _strip_spurious_pairs(orig_line, trans_line)
+    t = _unescape_and_strip_artifacts(t)
+    t = _remove_marker_and_tagn_tokens(t)
+    if squelch_for_seamless:
+        t = _squelch_single_word_repeat_if_needed(orig_line, t)
+    return t
+
+def rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=False):
+    rebuilt_cues = []
+    idx = 0
+    for c, lines in zip(cues, cue_lines):
+        n = len(lines)
+        tlines = translated_flat[idx:idx + n]
+        idx += n
+        tlines = [_postprocess_line(o, _fix_midword_hyphen(t), squelch_single_word) for o, t in zip(lines, tlines)]
+        tlines = _fix_leading_punct(tlines)
+        c_new = dict(c); c_new["text"] = "\n".join(tlines)
+        rebuilt_cues.append(c_new)
+    return rebuilt_cues
+
+def rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=False):
+    rebuilt_cues = []
+    for c, tparts, orig_parts in zip(cues, translated_per_cue, cue_lines):
+        tparts = [_postprocess_line(o, _fix_midword_hyphen(t), squelch_single_word) for o, t in zip(orig_parts, tparts)]
+        tparts = _fix_leading_punct(tparts)
+        c_new = dict(c); c_new["text"] = "\n".join(tparts)
+        rebuilt_cues.append(c_new)
+    return rebuilt_cues
+
+# ===================== Config =====================
+def load_config():
+    try:
+        if os.path.exists(CONFIG_PATH):
+            with open(CONFIG_PATH, "r", encoding="utf-8") as f:
+                return json.load(f)
+    except Exception:
+        pass
+    return {}
+
+def save_config(cfg: dict):
+    try:
+        with open(CONFIG_PATH, "w", encoding="utf-8") as f:
+            json.dump(cfg, f, ensure_ascii=False, indent=2)
+    except Exception:
+        pass
+
+# ===================== Ollama model listing =====================
+def list_ollama_models_http(host="localhost", port=11434) -> List[str]:
+    try:
+        conn = http.client.HTTPConnection(host, port, timeout=10)
+        conn.request("GET", "/api/tags")
+        resp = conn.getresponse()
+        data = resp.read()
+        conn.close()
+        if resp.status != 200:
+            return []
+        j = json.loads(data.decode("utf-8"))
+        models = j.get("models", [])
+        names = []
+        for m in models:
+            name = m.get("name")
+            if isinstance(name, str):
+                names.append(name)
+        return sorted(set(names))
+    except Exception:
+        return []
+
+def list_ollama_models_cli() -> List[str]:
+    try:
+        p = subprocess.run(["ollama", "list", "--json"], capture_output=True, text=True, timeout=10)
+        if p.returncode == 0 and p.stdout.strip():
+            txt = p.stdout.strip()
+            models = []
+            try:
+                j = json.loads(txt)
+                if isinstance(j, dict) and "models" in j:
+                    for m in j["models"]:
+                        name = m.get("name")
+                        if isinstance(name, str):
+                            models.append(name)
+                elif isinstance(j, list):
+                    for m in j:
+                        name = m.get("name")
+                        if isinstance(name, str):
+                            models.append(name)
+            except Exception:
+                for line in txt.splitlines():
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        obj = json.loads(line)
+                        name = obj.get("name")
+                        if isinstance(name, str):
+                            models.append(name)
+                    except Exception:
+                        pass
+            if models:
+                return sorted(set(models))
+    except Exception:
+        pass
+    try:
+        p = subprocess.run(["ollama", "list"], capture_output=True, text=True, timeout=10)
+        if p.returncode == 0 and p.stdout:
+            models = []
+            for line in p.stdout.splitlines():
+                line = line.strip()
+                if line.lower().startswith(("name","models")) or not line:
+                    continue
+                parts = line.split()
+                if parts:
+                    models.append(parts[0])
+            if models:
+                return sorted(set(models))
+    except Exception:
+        pass
+    return []
+
+def get_all_ollama_models(host="localhost", port=11434) -> List[str]:
+    names = list_ollama_models_http(host, port)
+    if names:
+        return names
+    return list_ollama_models_cli()
+
+# ===================== CLI flow =====================
+def run_cli(args):
+    if not os.path.exists(args.srt_file):
+        print(f"File not found: {args.srt_file}", file=sys.stderr); sys.exit(1)
+
+    with open(args.srt_file, "r", encoding="utf-8", errors="replace") as f:
+        srt_text = f.read()
+
+    cues = parse_srt(srt_text)
+    if not cues:
+        print("No SRT cues detected. Is the file valid?", file=sys.stderr); sys.exit(1)
+
+    cue_lines: List[List[str]] = [c["text"].splitlines() if c["text"] else [""] for c in cues]
+    tgt_code = _resolve_target(args.target_language)
+    src_code = _resolve_source(args.src, [ln for lines in cue_lines for ln in lines])
+    base, _ = os.path.splitext(args.srt_file)
+    out_path = args.out or f"{base}.{tgt_code.lower()}.srt"
+
+    total_lines = sum(len(ls) for ls in cue_lines)
+    pbar = tqdm(total=total_lines, unit="line", dynamic_ncols=True, desc="Translating") if (tqdm and not args.no_progress) else None
+    on_progress = (lambda n=1: pbar.update(n)) if pbar else None
+
+    cancel_event = None  # CLI: no cancel
+
+    if args.engine == "nllb":
+        print(f"Ensuring model is cached: {args.model} …")
+        ensure_model_downloaded(args.model)
+        dev_kwargs, workers = pick_device_for_workers(max(1, args.workers), args.device)
+        print(f"Engine=NLLB | model={args.model} | src={src_code}→{tgt_code} | device={dev_kwargs.get('device')} | threads={args.workers} effective={workers} | context={args.context}")
+
+        if args.context == "line":
+            flat_lines, masks = build_line_items(cue_lines)
+            translated_flat = nllb_translate_lines(flat_lines, masks, args.model, src_code, tgt_code,
+                                                   workers, args.batch, on_progress, None, cancel_event, dev_kwargs)
+            rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=False)
+        elif args.context == "cue":
+            flat_cues, cue_masks, counts = build_cue_items_for_nllb(cue_lines)
+            translated_per_cue = nllb_translate_cues(flat_cues, cue_masks, counts, args.model, src_code, tgt_code,
+                                                     workers, min(args.batch, 32), on_progress, None, cancel_event, dev_kwargs)
+            rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=False)
+        else:
+            groups_text, groups_masks, groups_counts = build_smart_groups_for_nllb(cue_lines, max_span_cues=args.max_span_cues)
+            translated_groups = nllb_translate_groups(groups_text, groups_masks, groups_counts, args.model, src_code, tgt_code,
+                                                      workers, min(args.batch, 16), on_progress, None, cancel_event, dev_kwargs)
+            percue_lines: List[List[str]] = []
+            for grp in translated_groups: percue_lines.extend(grp)
+            rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=False)
+
+    elif args.engine == "seamless":
+        print(f"Ensuring SeamlessM4T is cached: {args.seamless_model} …")
+        ensure_seamless_downloaded(args.seamless_model)
+        dev_kwargs, workers = pick_device_for_workers(max(1, args.workers), args.device)
+        print(f"Engine=SeamlessM4T | model={args.seamless_model} | src={src_code}→{tgt_code} | device={dev_kwargs.get('device')} | threads={args.workers} effective={workers} | context={args.context}")
+
+        if args.context == "line":
+            flat_lines, masks = build_line_items(cue_lines)
+            translated_flat = seamless_translate_lines(flat_lines, masks, args.seamless_model, src_code, tgt_code,
+                                                       workers, args.batch, on_progress, None, cancel_event, dev_kwargs)
+            rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=True)
+        elif args.context == "cue":
+            flat_cues, cue_masks, counts = build_cue_items_for_nllb(cue_lines)
+            translated_per_cue = seamless_translate_cues(flat_cues, cue_masks, counts, args.seamless_model, src_code, tgt_code,
+                                                         workers, min(args.batch, 32), on_progress, None, cancel_event, dev_kwargs)
+            rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=True)
+        else:
+            groups_text, groups_masks, groups_counts = build_smart_groups_for_nllb(cue_lines, max_span_cues=args.max_span_cues)
+            translated_groups = seamless_translate_groups(groups_text, groups_masks, groups_counts, args.seamless_model, src_code, tgt_code,
+                                                          workers, min(args.batch, 16), on_progress, None, cancel_event, dev_kwargs)
+            percue_lines: List[List[str]] = []
+            for grp in translated_groups: percue_lines.extend(grp)
+            rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=True)
+
+    else:  # Ollama
+        host, port = args.ollama_host, args.ollama_port
+        model = args.ollama_model
+        print(f"Engine=Ollama | model={model} | src={src_code}→{tgt_code} | host={host}:{port} | threads={args.workers} | context={args.context}")
+
+        if args.context == "line":
+            flat_lines, masks = build_line_items(cue_lines)
+            translated_flat = ollama_translate_lines(flat_lines, masks, args.target_language, src_code, model, host, port,
+                                                     max(1, args.workers), args.batch, on_progress, None, cancel_event)
+            rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=False)
+        elif args.context == "cue":
+            cues_text, cues_masks = build_cue_items_for_llm(cue_lines)
+            translated_per_cue = ollama_translate_cues(cues_text, cues_masks, args.target_language, src_code, model, host, port,
+                                                       max(1, args.workers), min(args.batch, 24), on_progress, None, cancel_event)
+            rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=False)
+        else:
+            groups_text, groups_masks, groups_counts = build_smart_groups_for_llm(cue_lines, max_span_cues=args.max_span_cues)
+            translated_groups = ollama_translate_groups(groups_text, groups_masks, groups_counts, args.target_language, src_code,
+                                                        model, host, port, max(1, args.workers), min(args.batch, 12), on_progress, None, cancel_event)
+            percue_lines: List[List[str]] = []
+            for grp in translated_groups: percue_lines.extend(grp)
+            rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=False)
+
+    if pbar: pbar.close()
+    with open(out_path, "w", encoding="utf-8") as f:
+        f.write(cues_to_srt(rebuilt_cues))
+    print(f"✅ Wrote: {out_path}")
+
+# ===================== GUI flow =====================
+def run_gui():
+    import tkinter as tk
+    from tkinter import ttk, filedialog, messagebox
+
+    root = tk.Tk()
+    root.title("SRT Translator — NLLB / SeamlessM4T / Ollama")
+
+    # Detect accelerators for NLLB/Seamless
+    try:
+        import torch
+        has_mps = getattr(torch.backends, "mps", None) and torch.backends.mps.is_available()
+        has_cuda = torch.cuda.is_available()
+    except Exception:
+        has_mps = has_cuda = False
+
+    # Load persisted config
+    cfg = load_config()
+    last_engine = cfg.get("engine", "NLLB")
+    last_ollama_model = cfg.get("ollama_model", "qwen3:32b-instruct")
+    last_ollama_host = cfg.get("ollama_host", "localhost")
+    last_ollama_port = int(cfg.get("ollama_port", 11434))
+
+    # Vars
+    srt_path_var = tk.StringVar()
+    lang_var = tk.StringVar(value="German")
+    context_var = tk.StringVar(value="Smart")
+    engine_var = tk.StringVar(value=last_engine if last_engine in ["NLLB","SeamlessM4T","Ollama"] else "NLLB")
+    device_var = tk.StringVar(value="Auto")
+    threads_var = tk.IntVar(value=max(1, (os.cpu_count() or 4) - 2))
+    max_span_var = tk.IntVar(value=4)
+    status_var = tk.StringVar(value="Select file, language, context, engine, device, threads.")
+    progress_var = tk.IntVar(value=0)
+    progress_max = tk.IntVar(value=100)
+    ollama_model_var = tk.StringVar(value=last_ollama_model)
+    ollama_host_var = tk.StringVar(value=last_ollama_host)
+    ollama_port_var = tk.IntVar(value=last_ollama_port)
+
+    ollama_models_list: List[str] = []
+
+    running_flag = tk.BooleanVar(value=False)
+    cancel_event = threading.Event(); cancel_event.clear()
+
+    pad = {"padx": 8, "pady": 6}
+    frm = ttk.Frame(root); frm.pack(fill="both", expand=True, **pad)
+
+    # File row
+    file_row = ttk.Frame(frm); file_row.pack(fill="x", **pad)
+    ttk.Label(file_row, text="SRT file:").pack(side="left")
+    file_entry = ttk.Entry(file_row, textvariable=srt_path_var); file_entry.pack(side="left", fill="x", expand=True, padx=6)
+    ttk.Button(file_row, text="Browse…", command=lambda: _browse_file(srt_path_var)).pack(side="left")
+
+    # Language + Context
+    row2 = ttk.Frame(frm); row2.pack(fill="x", **pad)
+    ttk.Label(row2, text="Target language:").pack(side="left")
+    languages = sorted({k for k in LANG2CODE.keys() if len(k) > 2})
+    lang_combo = ttk.Combobox(row2, textvariable=lang_var, values=languages)
+    lang_combo.pack(side="left", fill="x", expand=True, padx=6)
+    
+    # ttk.Label(row2, text="Context:").pack(side="left", padx=(12, 2))
+    # context_combo = ttk.Combobox(row2, textvariable=context_var, values=["Smart", "Cue", "Line"], state="readonly", width=8)
+    # context_combo.pack(side="left")
+    # ttk.Label(row2, text="Max span:").pack(side="left", padx=(12,2))
+    # span_spin = ttk.Spinbox(row2, from_=2, to=12, textvariable=max_span_var, width=5)
+    # span_spin.pack(side="left")
+    # ttk.Label(row2, text="(You can type an NLLB code like deu_Latn)").pack(side="left", padx=(12,0))
+
+    # Engine + Device + Threads
+    row_engine = ttk.Frame(frm); row_engine.pack(fill="x", **pad)
+    ttk.Label(row_engine, text="Engine:").pack(side="left")
+    engine_combo = ttk.Combobox(row_engine, textvariable=engine_var, values=["NLLB", "SeamlessM4T", "Ollama"], state="readonly", width=12)
+    engine_combo.pack(side="left", padx=6)
+    ttk.Label(row_engine, text="Device:").pack(side="left")
+    device_combo = ttk.Combobox(row_engine, textvariable=device_var, values=["Auto", "CPU"] + (["GPU"] if (has_mps or has_cuda) else []), state="readonly", width=8)
+    device_combo.pack(side="left", padx=6)
+    # ttk.Label(row_engine, text="Threads:").pack(side="left")
+    # threads_spin = ttk.Spinbox(row_engine, from_=1, to=max(1, (os.cpu_count() or 8)), textvariable=threads_var, width=6)
+    # threads_spin.pack(side="left")
+
+    # Ollama model row (shown only when engine=Ollama)
+    row_ollama = ttk.Frame(frm)
+    ttk.Label(row_ollama, text="Ollama model:").pack(side="left")
+    ollama_model_combo = ttk.Combobox(row_ollama, textvariable=ollama_model_var, values=[], width=30)
+    ollama_model_combo.pack(side="left", padx=6)
+    # refresh_btn = ttk.Button(row_ollama, text="Refresh", command=lambda: refresh_models())
+    # refresh_btn.pack(side="left")
+
+    row_ollama2 = ttk.Frame(frm)
+    ttk.Label(row_ollama2, text="Ollama host:").pack(side="left")
+    ollama_host_entry = ttk.Entry(row_ollama2, textvariable=ollama_host_var, width=12); ollama_host_entry.pack(side="left")
+    ttk.Label(row_ollama2, text="Port:").pack(side="left", padx=(12,2))
+    ollama_port_entry = ttk.Spinbox(row_ollama2, from_=1, to=65535, textvariable=ollama_port_var, width=6); ollama_port_entry.pack(side="left")
+
+    def on_device_change(event=None):
+        return
+        # if device_var.get().lower() == "gpu" and engine_var.get() in ["NLLB","SeamlessM4T"]:
+            # threads_spin.configure(state="disabled")
+        # else:
+            # threads_spin.configure(state="normal")
+
+    def refresh_models():
+        nonlocal ollama_models_list
+        host = ollama_host_var.get().strip() or "localhost"
+        port = int(ollama_port_var.get() or 11434)
+        status_var.set(f"Fetching models from {host}:{port} …")
+        root.update_idletasks()
+        models = get_all_ollama_models(host, port)
+        if not models:
+            from tkinter import messagebox
+            messagebox.showwarning("Ollama", "Keine Modelle gefunden (prüfe Ollama-Dienst oder Host/Port).")
+            return
+        ollama_models_list = models
+        ollama_model_combo.configure(values=ollama_models_list)
+        current = ollama_model_var.get().strip()
+        if current not in ollama_models_list:
+            ollama_model_var.set(ollama_models_list[0])
+        status_var.set(f"{len(ollama_models_list)} Modelle geladen.")
+
+        cfg = load_config()
+        cfg["ollama_host"] = host
+        cfg["ollama_port"] = port
+        cfg["ollama_model"] = ollama_model_var.get().strip()
+        save_config(cfg)
+
+    def on_engine_change(event=None):
+        is_ollama = (engine_var.get() == "Ollama")
+
+        if is_ollama:
+            # show Ollama rows
+            row_ollama.pack(fill="x", **pad)
+            row_ollama2.pack(fill="x", **pad)
+
+            # ALWAYS enable Ollama controls when switching to Ollama
+            ollama_model_combo.configure(state="normal")   # or "readonly" if you prefer no typing
+            ollama_host_entry.configure(state="normal")
+            ollama_port_entry.configure(state="normal")
+
+            # Ollama ignores device selection
+            device_combo.configure(state="disabled")
+
+            # Populate models if needed
+            if not ollama_model_combo.cget("values"):
+                refresh_models()
+        else:
+            # hide Ollama rows and disable its controls when not on Ollama
+            row_ollama.pack_forget()
+            row_ollama2.pack_forget()
+            ollama_model_combo.configure(state="disabled")
+            ollama_host_entry.configure(state="disabled")
+            ollama_port_entry.configure(state="disabled")
+
+            # Re-enable device selection for non-Ollama engines
+            device_combo.configure(state="readonly")
+            
+    device_combo.bind("<<ComboboxSelected>>", on_device_change)
+    engine_combo.bind("<<ComboboxSelected>>", on_engine_change)
+    on_engine_change(); on_device_change()
+
+    # Progress bar
+    pbar = ttk.Progressbar(frm, orient="horizontal", mode="determinate", maximum=progress_max.get(), variable=progress_var)
+
+    # Controls
+    ctrl = ttk.Frame(frm); ctrl.pack(fill="x", **pad)
+    start_btn = ttk.Button(ctrl, text="Translate"); start_btn.pack(side="left")
+    # status label (optional UI element)
+    # status_label = ttk.Label(frm, textvariable=status_var, foreground="#555"); status_label.pack(fill="x", **pad)
+
+    def set_controls(enabled: bool):
+        state = "normal" if enabled else "disabled"
+        file_entry.configure(state=state); lang_combo.configure(state=state)
+        # context_combo.configure(state=state); span_spin.configure(state=state)
+        engine_combo.configure(state=state)
+        model_state = "normal" if engine_var.get()=="Ollama" else "disabled"
+        refresh_state = model_state
+        ollama_model_combo.configure(state=model_state); ollama_host_entry.configure(state=model_state); ollama_port_entry.configure(state=model_state)
+        # try: refresh_btn.configure(state=refresh_state)
+        # except Exception: pass
+        device_combo.configure(state=("readonly" if engine_var.get()!="Ollama" else "disabled"))
+        # threads_spin.configure(state=("disabled" if (engine_var.get() in ["NLLB","SeamlessM4T"] and device_var.get().lower()=="gpu") else "normal"))
+        start_btn.configure(state=state)
+
+    def ui_show_progress(total: Optional[int]):
+        pbar.configure(mode="determinate", maximum=total if total else 100)
+        progress_max.set(total or 100); progress_var.set(0); pbar.pack(fill="x", **pad)
+
+    def ui_hide_progress():
+        pbar.stop(); pbar.pack_forget()
+
+    # Tk-aware tqdm for model download progress
+    def make_tk_tqdm():
+        try:
+            from tqdm.auto import tqdm as base_tqdm
+        except Exception:
+            class Dummy:
+                def __init__(self, *a, total=None, **kw): self.total=total
+                def update(self, n=1): pass
+                def close(self): pass
+            return Dummy
+
+        class TkTqdm(base_tqdm):
+            _global_total = 0
+            _global_n = 0
+            _lock = threading.Lock()
+            def __init__(self, *a, **kw):
+                super().__init__(*a, **kw)
+                with TkTqdm._lock:
+                    if self.total:
+                        TkTqdm._global_total += int(self.total)
+                        root.after(0, lambda: ui_show_progress(max(TkTqdm._global_total, 1)))
+            def update(self, n=1):
+                if cancel_event.is_set():
+                    raise RuntimeError("Cancelled")
+                res = super().update(n)
+                with TkTqdm._lock:
+                    TkTqdm._global_n += int(n)
+                    val = max(0, TkTqdm._global_n)
+                    root.after(0, lambda: progress_var.set(val))
+                return res
+        return TkTqdm
+
+    def on_cancel():
+        if not running_flag.get(): return
+        if not __import__("tkinter").messagebox.askyesno("Cancel", "Sicher abbrechen? Unfertige Übersetzung wird verworfen."):
+            return
+        cancel_event.set()
+        status_var.set("Abbruch wird ausgeführt…")
+
+    def start_translation():
+        if running_flag.get(): return
+        path = srt_path_var.get().strip()
+        if not path or not os.path.exists(path):
+            from tkinter import messagebox
+            messagebox.showerror("No file", "Please choose a valid .srt file."); return
+
+        tgt_input = lang_var.get().strip()
+        tgt_code = _resolve_target_gui(tgt_input, __import__("tkinter").messagebox)
+        if not tgt_code: return
+
+        context_mode = context_var.get().lower()
+        max_span = int(max_span_var.get() or 4)
+        engine = engine_var.get()
+
+        cfg = load_config()
+        cfg["engine"] = engine
+        if engine == "Ollama":
+            cfg["ollama_model"] = ollama_model_var.get().strip()
+            cfg["ollama_host"] = ollama_host_var.get().strip() or "localhost"
+            cfg["ollama_port"] = int(ollama_port_var.get() or 11434)
+        save_config(cfg)
+
+        try:
+            with open(path, "r", encoding="utf-8", errors="replace") as f:
+                srt_text = f.read()
+        except Exception as e:
+            from tkinter import messagebox
+            messagebox.showerror("Read error", str(e)); return
+
+        cues = parse_srt(srt_text)
+        if not cues:
+            from tkinter import messagebox
+            messagebox.showerror("Format", "No SRT cues detected. Is the file valid?"); return
+
+        cue_lines: List[List[str]] = [c["text"].splitlines() if c["text"] else [""] for c in cues]
+        flat_all_lines = [ln for lines in cue_lines for ln in lines]
+        sample = "\n".join([l for l in flat_all_lines if l.strip()][:20])
+        src_code = autodetect_code(sample)
+
+        base, _ = os.path.splitext(path)
+        out_path = f"{base}.{tgt_code.lower()}.srt"
+
+        requested_threads = max(1, int(threads_var.get() or 1))
+        device_mode = device_var.get().lower()
+        dev_kwargs, effective_workers = pick_device_for_workers(requested_threads, device_mode) if engine in ["NLLB","SeamlessM4T"] else ({"device": -1}, requested_threads)
+        total_lines = sum(len(ls) for ls in cue_lines)
+
+        running_flag.set(True); cancel_event.clear()
+        set_controls(False)
+        start_btn.configure(text="Cancel", command=on_cancel)
+        start_btn.configure(state="normal")
+        status_var.set("Vorbereitung…")
+
+        ui_show_progress(100)
+
+        def worker():
+            try:
+                if engine == "NLLB":
+                    status_var.set("Downloading NLLB model (if needed)…")
+                    TkTqdm = make_tk_tqdm()
+                    ensure_model_downloaded("facebook/nllb-200-distilled-600M", tqdm_class=TkTqdm)
+                elif engine == "SeamlessM4T":
+                    status_var.set("Downloading SeamlessM4T model (if needed)…")
+                    TkTqdm = make_tk_tqdm()
+                    ensure_seamless_downloaded("facebook/seamless-m4t-v2-large", tqdm_class=TkTqdm)
+
+                def start_stage2():
+                    status_var.set(
+                        f"Translating… (engine={engine}"
+                        + (f", device={dev_kwargs.get('device')}, mode={device_mode}" if engine in ["NLLB","SeamlessM4T"] else "")
+                        + f", threads={requested_threads} effective={effective_workers}, context={context_mode}, maxspan={max_span})"
+                    )
+                    pbar.configure(mode="determinate", maximum=total_lines)
+                    progress_max.set(total_lines); progress_var.set(0); pbar.pack(fill="x", **pad)
+                root.after(0, start_stage2)
+
+                processed = {"n": 0}
+                def on_progress(n=1):
+                    processed["n"] += n
+                    root.after(0, lambda: progress_var.set(processed["n"]))
+
+                # Stage 2: Translation
+                if engine == "NLLB":
+                    if context_mode == "line":
+                        flat_lines, masks = build_line_items(cue_lines)
+                        translated_flat = nllb_translate_lines(flat_lines, masks,
+                            "facebook/nllb-200-distilled-600M", src_code, tgt_code,
+                            effective_workers, 32, on_progress, None, cancel_event, dev_kwargs)
+                        rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=False)
+                    elif context_mode == "cue":
+                        flat_cues, cue_masks, counts = build_cue_items_for_nllb(cue_lines)
+                        translated_per_cue = nllb_translate_cues(flat_cues, cue_masks, counts,
+                            "facebook/nllb-200-distilled-600M", src_code, tgt_code,
+                            effective_workers, 16, on_progress, None, cancel_event, dev_kwargs)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=False)
+                    else:
+                        groups_text, groups_masks, groups_counts = build_smart_groups_for_nllb(cue_lines, max_span_cues=max_span)
+                        translated_groups = nllb_translate_groups(groups_text, groups_masks, groups_counts,
+                            "facebook/nllb-200-distilled-600M", src_code, tgt_code,
+                            effective_workers, 12, on_progress, None, cancel_event, dev_kwargs)
+                        percue_lines: List[List[str]] = []
+                        for grp in translated_groups: percue_lines.extend(grp)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=False)
+
+                elif engine == "SeamlessM4T":
+                    model_name = "facebook/seamless-m4t-v2-large"
+                    if context_mode == "line":
+                        flat_lines, masks = build_line_items(cue_lines)
+                        translated_flat = seamless_translate_lines(flat_lines, masks, model_name, src_code, tgt_code,
+                                                                  effective_workers, 32, on_progress, None, cancel_event, dev_kwargs)
+                        rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=True)
+                    elif context_mode == "cue":
+                        flat_cues, cue_masks, counts = build_cue_items_for_nllb(cue_lines)
+                        translated_per_cue = seamless_translate_cues(flat_cues, cue_masks, counts, model_name, src_code, tgt_code,
+                                                                     effective_workers, 16, on_progress, None, cancel_event, dev_kwargs)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=True)
+                    else:
+                        groups_text, groups_masks, groups_counts = build_smart_groups_for_nllb(cue_lines, max_span_cues=max_span)
+                        translated_groups = seamless_translate_groups(groups_text, groups_masks, groups_counts, model_name, src_code, tgt_code,
+                                                                     effective_workers, 12, on_progress, None, cancel_event, dev_kwargs)
+                        percue_lines: List[List[str]] = []
+                        for grp in translated_groups: percue_lines.extend(grp)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=True)
+
+                else:  # Ollama
+                    model = ollama_model_var.get().strip() or "qwen3:32b-instruct"
+                    host = ollama_host_var.get().strip() or "localhost"
+                    port = int(ollama_port_var.get() or 11434)
+                    if context_mode == "line":
+                        flat_lines, masks = build_line_items(cue_lines)
+                        translated_flat = ollama_translate_lines(flat_lines, masks, lang_var.get(), src_code, model, host, port,
+                                                                 requested_threads, 32, on_progress, None, cancel_event)
+                        rebuilt_cues = rebuild_from_flat_lines(cues, cue_lines, translated_flat, squelch_single_word=False)
+                    elif context_mode == "cue":
+                        cues_text, cues_masks = build_cue_items_for_llm(cue_lines)
+                        translated_per_cue = ollama_translate_cues(cues_text, cues_masks, lang_var.get(), src_code, model, host, port,
+                                                                   requested_threads, 16, on_progress, None, cancel_event)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, translated_per_cue, cue_lines, squelch_single_word=False)
+                    else:
+                        groups_text, groups_masks, groups_counts = build_smart_groups_for_llm(cue_lines, max_span_cues=max_span)
+                        translated_groups = ollama_translate_groups(groups_text, groups_masks, groups_counts, lang_var.get(), src_code,
+                                                                    model, host, port, requested_threads, 12, on_progress, None, cancel_event)
+                        percue_lines: List[List[str]] = []
+                        for grp in translated_groups: percue_lines.extend(grp)
+                        rebuilt_cues = rebuild_from_cue_parts(cues, percue_lines, cue_lines, squelch_single_word=False)
+
+                with open(out_path, "w", encoding="utf-8") as f:
+                    f.write(cues_to_srt(rebuilt_cues))
+
+            except RuntimeError as e:
+                if "Cancelled" in str(e):
+                    return _fail("❌ Abgebrochen.")
+                return _fail(f"Translation failed: {e}")
+            except Exception as e:
+                return _fail(f"Translation failed: {e}")
+
+            _done(out_path)
+
+        def _fail(msg: str):
+            running_flag.set(False)
+            set_controls(True); status_var.set(msg); ui_hide_progress()
+            start_btn.configure(text="Translate", command=start_translation)
+
+        def _done(path_out: str):
+            running_flag.set(False)
+            set_controls(True); status_var.set(f"✅ Done. Wrote: {path_out}"); ui_hide_progress()
+            start_btn.configure(text="Translate", command=start_translation)
+
+        t = threading.Thread(target=worker, daemon=True); t.start()
+
+    start_btn.configure(command=start_translation)
+    root.mainloop()
+
+# ===================== Utils =====================
+def _browse_file(var):
+    from tkinter import filedialog
+    path = filedialog.askopenfilename(title="Select .srt file",
+                                      filetypes=[("SubRip Subtitle", "*.srt"), ("All files", "*.*")])
+    if path: var.set(path)
+
+def _resolve_target(target_language: str) -> str:
+    tgt_code = norm_lang_to_code(target_language)
+    if tgt_code is None:
+        if re.match(r"^[a-z]{3}_[A-Za-z]{4}$", target_language.strip()):
+            tgt_code = target_language.strip()
+        else:
+            raise ValueError(f"Unrecognized target language: {target_language}")
+    return tgt_code
+
+def _resolve_target_gui(target_language: str, messagebox):
+    try:
+        return _resolve_target(target_language)
+    except Exception:
+        messagebox.showerror("Language", f"Unrecognized target language: {target_language}")
+        return None
+
+def _resolve_source(src: Optional[str], flat_lines: List[str]) -> str:
+    if src:
+        code = norm_lang_to_code(src)
+        if code is None:
+            if re.match(r"^[a-z]{3}_[A-Za-z]{4}$", src.strip()):
+                code = src.strip()
+            else:
+                raise ValueError(f"Unrecognized source language: {src}")
+        return code
+    sample = "\n".join([l for l in flat_lines if l.strip()][:20])
+    return autodetect_code(sample)
+
+# ===================== Main =====================
+def main():
+    if len(sys.argv) == 1:
+        run_gui(); return
+
+    ap = argparse.ArgumentParser(description="Translate an SRT with NLLB, SeamlessM4T, or Ollama, preserving structure & inline tags.")
+    ap.add_argument("srt_file", help="Path to input .srt")
+    ap.add_argument("target_language", help='Target language (name like "German" or NLLB code like "deu_Latn")')
+    ap.add_argument("--src", default=None, help="Source language (name or NLLB code). If omitted, auto-detect.")
+    # Engine
+    ap.add_argument("--engine", choices=["nllb","seamless","ollama"], default="nllb", help="Translation engine")
+    # NLLB opts
+    ap.add_argument("--model", default="facebook/nllb-200-distilled-600M", help="Transformers model repo (NLLB)")
+    # Seamless opts
+    ap.add_argument("--seamless-model", default="facebook/seamless-m4t-v2-large", help="Transformers model repo (SeamlessM4T)")
+    # Device (used for NLLB & SeamlessM4T)
+    ap.add_argument("--device", choices=["auto","cpu","gpu"], default="auto", help="Compute device for NLLB/SeamlessM4T")
+    # Ollama opts
+    ap.add_argument("--ollama-model", default="qwen3:32b-instruct", help="Ollama model tag")
+    ap.add_argument("--ollama-host", default="localhost")
+    ap.add_argument("--ollama-port", type=int, default=11434)
+    # Shared
+    ap.add_argument("--batch", type=int, default=32, help="Batch size per worker (lines/cues/groups)")
+    ap.add_argument("--workers", type=int, default=max(1, (os.cpu_count() or 4) - 2), help="Number of parallel workers")
+    ap.add_argument("--context", choices=["line","cue","smart"], default="smart", help="Context granularity")
+    ap.add_argument("--max-span-cues", type=int, default=4, help="Smart mode: max cues per group")
+    ap.add_argument("--out", default=None, help="Output .srt path (default: <basename>.<tgt>.srt)")
+    ap.add_argument("--no-progress", action="store_true", help="Disable CLI progress bar")
+
+    args = ap.parse_args()
+    run_cli(args)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file