405 lines
13 KiB
Python
405 lines
13 KiB
Python
import os
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import time
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import logging
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import re
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from typing import List, Optional
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import psycopg2
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import psycopg2.extras
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from langdetect import detect, DetectorFactory
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import ctranslate2
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from transformers import AutoTokenizer
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DetectorFactory.seed = 0
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
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LOG = logging.getLogger("translator_ct2")
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TRANSLATOR_ID = os.environ.get("TRANSLATOR_ID", "")
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TRANSLATOR_TOTAL = int(os.environ.get("TRANSLATOR_TOTAL", "1"))
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def clean_text(text: str) -> str:
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if not text:
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return ""
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text = re.sub(r'<[^>]+>', '', text)
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text = text.replace('<unk>', '')
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text = text.replace(' ', ' ')
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text = text.replace('&', '&')
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text = text.replace('<', '<')
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text = text.replace('>', '>')
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text = text.replace('"', '"')
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text = re.sub(r'\s+', ' ', text)
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return text.strip()
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DB_CONFIG = {
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"host": os.environ.get("DB_HOST", "localhost"),
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"port": int(os.environ.get("DB_PORT", 5432)),
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"dbname": os.environ.get("DB_NAME", "rss"),
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"user": os.environ.get("DB_USER", "rss"),
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"password": os.environ.get("DB_PASS", "x"),
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}
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def _env_list(name: str, default="es"):
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raw = os.environ.get(name)
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if raw:
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return [s.strip() for s in raw.split(",") if s.strip()]
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return [default]
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def _env_int(name: str, default: int = 8):
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v = os.environ.get(name)
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try:
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return int(v)
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except Exception:
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return default
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def _env_str(name: str, default=None):
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v = os.environ.get(name)
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return v if v else default
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TARGET_LANGS = _env_list("TARGET_LANGS")
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BATCH_SIZE = _env_int("TRANSLATOR_BATCH", 8)
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MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", 512)
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MAX_NEW_TOKENS = _env_int("MAX_NEW_TOKENS", 512)
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CT2_MODEL_PATH = _env_str("CT2_MODEL_PATH", "/app/models/nllb-ct2")
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CT2_DEVICE = _env_str("CT2_DEVICE", "cpu")
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CT2_COMPUTE_TYPE = _env_str("CT2_COMPUTE_TYPE", "int8")
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UNIVERSAL_MODEL = _env_str("UNIVERSAL_MODEL", "facebook/nllb-200-distilled-600M")
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BODY_CHARS_CHUNK = _env_int("BODY_CHARS_CHUNK", 900)
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LANG_CODE_MAP = {
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"en": "eng_Latn", "es": "spa_Latn", "fr": "fra_Latn", "de": "deu_Latn",
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"it": "ita_Latn", "pt": "por_Latn", "nl": "nld_Latn", "sv": "swe_Latn",
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"da": "dan_Latn", "fi": "fin_Latn", "no": "nob_Latn",
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"pl": "pol_Latn", "cs": "ces_Latn", "sk": "slk_Latn",
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"sl": "slv_Latn", "hu": "hun_Latn", "ro": "ron_Latn",
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"el": "ell_Grek", "ru": "rus_Cyrl", "uk": "ukr_Cyrl",
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"tr": "tur_Latn", "ar": "arb_Arab", "fa": "pes_Arab",
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"he": "heb_Hebr", "zh": "zho_Hans", "ja": "jpn_Jpan",
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"ko": "kor_Hang", "vi": "vie_Latn",
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}
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_tokenizer = None
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_translator = None
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def ensure_model():
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global _tokenizer, _translator
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if _translator:
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return
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model_path = CT2_MODEL_PATH
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model_bin = os.path.join(model_path, "model.bin")
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if not os.path.exists(model_bin):
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LOG.info(f"CTranslate2 model not found at {model_path}, converting from {UNIVERSAL_MODEL}...")
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convert_model()
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LOG.info(f"Loading CTranslate2 model from {model_path} on {CT2_DEVICE}")
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_translator = ctranslate2.Translator(
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model_path,
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device=CT2_DEVICE,
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compute_type=CT2_COMPUTE_TYPE,
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)
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_tokenizer = AutoTokenizer.from_pretrained(UNIVERSAL_MODEL)
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LOG.info("CTranslate2 model loaded successfully")
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def convert_model():
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import subprocess
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model_path = CT2_MODEL_PATH
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os.makedirs(model_path, exist_ok=True)
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quantization = CT2_COMPUTE_TYPE if CT2_COMPUTE_TYPE != "auto" else "int8"
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cmd = [
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"ct2-transformers-converter",
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"--model", UNIVERSAL_MODEL,
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"--output_dir", model_path,
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"--quantization", quantization,
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"--force"
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]
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LOG.info(f"Running: {' '.join(cmd)}")
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=1800)
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if result.returncode != 0:
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LOG.error(f"Model conversion failed: {result.stderr}")
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raise RuntimeError("Failed to convert model")
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LOG.info("Model conversion completed")
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def translate_texts(src: str, tgt: str, texts: List[str]) -> List[str]:
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if not texts:
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return []
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ensure_model()
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clean = [(t or "").strip() for t in texts]
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if all(not t for t in clean):
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return ["" for _ in clean]
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src_code = LANG_CODE_MAP.get(src, f"{src}_Latn")
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tgt_code = LANG_CODE_MAP.get(tgt, "spa_Latn")
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try:
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_tokenizer.src_lang = src_code
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except Exception:
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pass
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sources = []
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for t in clean:
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if t:
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ids = _tokenizer.encode(t, truncation=True, max_length=MAX_SRC_TOKENS)
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tokens = _tokenizer.convert_ids_to_tokens(ids)
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sources.append(tokens)
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else:
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sources.append([])
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target_prefix = [[tgt_code]] * len(sources)
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results = _translator.translate_batch(
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sources,
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target_prefix=target_prefix,
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beam_size=2,
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max_decoding_length=MAX_NEW_TOKENS,
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repetition_penalty=2.0,
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no_repeat_ngram_size=3,
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)
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translated = []
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for result in results:
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try:
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if result.hypotheses and len(result.hypotheses) > 0:
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hyp = result.hypotheses[0]
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if isinstance(hyp, list) and len(hyp) > 0:
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first_hyp = hyp[0]
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if isinstance(first_hyp, dict) and "token_ids" in first_hyp:
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tokens = first_hyp["token_ids"]
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text = _tokenizer.decode(tokens)
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translated.append(text.strip())
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elif isinstance(first_hyp, str):
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token_strings = hyp[1:] if len(hyp) > 1 else []
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if token_strings:
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text = _tokenizer.convert_tokens_to_string(token_strings)
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translated.append(text.strip())
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else:
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translated.append("")
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else:
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translated.append("")
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else:
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translated.append("")
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else:
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translated.append("")
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except Exception as e:
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LOG.error(f"Error processing result: {e}")
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translated.append("")
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return translated
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def split_body_into_chunks(text: str) -> List[str]:
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text = (text or "").strip()
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if len(text) <= BODY_CHARS_CHUNK:
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return [text] if text else []
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parts = re.split(r'(\n\n+|(?<=[\.\!\?؛؟。])\s+)', text)
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chunks = []
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current = ""
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for part in parts:
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if not part:
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continue
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if len(current) + len(part) <= BODY_CHARS_CHUNK:
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current += part
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else:
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if current.strip():
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chunks.append(current.strip())
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current = part
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if current.strip():
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chunks.append(current.strip())
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return chunks if chunks else [text]
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def translate_body_long(src: str, tgt: str, body: str) -> str:
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body = (body or "").strip()
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if not body:
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return ""
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chunks = split_body_into_chunks(body)
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if len(chunks) == 1:
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return translate_texts(src, tgt, [body])[0]
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translated_chunks = []
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for ch in chunks:
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tr = translate_texts(src, tgt, [ch])[0]
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translated_chunks.append(tr)
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return " ".join(translated_chunks)
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def normalize_lang(lang: Optional[str], default: str = "es") -> Optional[str]:
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if not lang:
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return default
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lang = lang.strip().lower()[:2]
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return lang if lang else default
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def detect_lang(text: str) -> str:
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if not text or len(text) < 10:
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return "en"
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try:
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return detect(text)
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except Exception:
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return "en"
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def process_batch(conn, rows):
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todo = []
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for r in rows:
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lang_to = normalize_lang(r.get("lang_to"), "es") or "es"
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lang_from = normalize_lang(r.get("lang_from")) or detect_lang(r.get("titulo") or "")
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titulo = (r.get("titulo") or "").strip()
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resumen = (r.get("resumen") or "").strip()
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if lang_from == lang_to:
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# Mark as done and copy original text if languages match
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE traducciones
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SET titulo_trad = %s, resumen_trad = %s, status = 'done'
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WHERE id = %s
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""", (titulo, resumen, r.get("tr_id")))
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conn.commit()
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cursor.close()
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continue
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todo.append({
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"tr_id": r.get("tr_id"),
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"lang_from": lang_from,
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"lang_to": lang_to,
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"titulo": titulo,
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"resumen": resumen,
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})
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if not todo:
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return
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# 1. FAST LOCKING: Commit locked_at immediately to inform other workers
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cursor = conn.cursor()
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tr_ids = [item["tr_id"] for item in todo]
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cursor.execute(f"""
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UPDATE traducciones
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SET locked_at = NOW()
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WHERE id = ANY(ARRAY[{','.join(['%s'] * len(tr_ids))}])
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""", tr_ids)
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conn.commit()
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cursor.close()
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from collections import defaultdict
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groups = defaultdict(list)
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for item in todo:
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key = (item["lang_from"], item["lang_to"])
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groups[key].append(item)
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for (lang_from, lang_to), items in groups.items():
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LOG.info(f"Translating {lang_from} -> {lang_to} ({len(items)} items)")
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try:
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titles = [i["titulo"] for i in items]
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translated_titles = translate_texts(lang_from, lang_to, titles)
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for item, tt in zip(items, translated_titles):
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body = (item["resumen"] or "").strip()
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tb = ""
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if body:
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try:
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tb = translate_body_long(lang_from, lang_to, body)
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except Exception as e:
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LOG.error(f"Body translation error for ID {item['tr_id']}: {e}")
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tb = item["resumen"]
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tt = clean_text((tt or "").strip())
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tb = clean_text((tb or "").strip())
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if not tt:
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tt = item["titulo"]
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if not tb:
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tb = item["resumen"]
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# 2. INDIVIDUAL COMMIT: Save each item as it's done
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try:
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE traducciones
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SET titulo_trad = %s, resumen_trad = %s, status = 'done', locked_at = NULL
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WHERE id = %s
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""", (tt, tb, item["tr_id"]))
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conn.commit()
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cursor.close()
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except Exception as e:
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LOG.error(f"Update error for ID {item['tr_id']}: {e}")
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conn.rollback()
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LOG.info(f"Finished group {lang_from} -> {lang_to}")
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except Exception as e:
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LOG.error(f"Batch group error {lang_from} -> {lang_to}: {e}")
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# Mark these as error to avoid infinite loop if it's a model crash
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try:
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE traducciones SET status = 'error', locked_at = NULL
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WHERE id = ANY(ARRAY[{','.join(['%s'] * len(items))}])
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""", [i["tr_id"] for i in items])
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conn.commit()
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cursor.close()
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except:
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conn.rollback()
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def fetch_pending_translations(conn):
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cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
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worker_id = os.environ.get("HOSTNAME", f"worker-{os.getpid()}")
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for lang in TARGET_LANGS:
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cursor.execute("""
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SELECT t.id as tr_id, t.lang_from, t.lang_to,
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n.titulo, n.resumen, n.id as noticia_id
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FROM traducciones t
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JOIN noticias n ON n.id = t.noticia_id
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WHERE t.lang_to = %s
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AND (t.titulo_trad IS NULL OR t.resumen_trad IS NULL)
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AND (t.locked_at IS NULL OR t.locked_at < NOW() - INTERVAL '10 minutes')
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ORDER BY n.fecha DESC
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LIMIT %s
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FOR UPDATE SKIP LOCKED
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""", (lang, BATCH_SIZE))
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rows = cursor.fetchall()
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if rows:
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LOG.info(f"Found {len(rows)} pending translations for {lang}")
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process_batch(conn, rows)
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cursor.close()
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def connect_db():
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return psycopg2.connect(**DB_CONFIG)
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def main():
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LOG.info(f"CTranslate2 translator worker started (device={CT2_DEVICE}, instances={TRANSLATOR_TOTAL})")
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ensure_model()
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while True:
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try:
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conn = connect_db()
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fetch_pending_translations(conn)
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conn.close()
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except Exception as e:
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LOG.error(f"Error: {e}")
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time.sleep(30)
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if __name__ == "__main__":
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main()
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