jlimolina
a285021e6c
- Backend usa DATABASE_URL (resolvePgConnInfo) en vez de DB_* que no existían en el contenedor API, arreglando el fallo de pg_dump - Dockerfile backend: alpine 3.23 para pg_dump 18.4 (compatible con PostgreSQL 18 del servidor) - BackupDatabase ya no usa docker exec (no disponible en la imagen) - Nuevo endpoint POST /api/admin/restore para restaurar .sql o .zip vía psql - UI de Configuración: sección Restaurar Base de Datos - Regenerado go.sum para el build
540 lines
16 KiB
Python
540 lines
16 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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import fcntl
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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", 128)
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MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", 256)
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MAX_NEW_TOKENS = _env_int("MAX_NEW_TOKENS", 256)
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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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CT2_INTRA_THREADS = _env_int("CT2_INTRA_THREADS", 0)
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CT2_INTER_THREADS = _env_int("CT2_INTER_THREADS", 1)
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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",
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"es": "spa_Latn",
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"fr": "fra_Latn",
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"de": "deu_Latn",
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"it": "ita_Latn",
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"pt": "por_Latn",
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"nl": "nld_Latn",
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"sv": "swe_Latn",
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"da": "dan_Latn",
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"fi": "fin_Latn",
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"no": "nob_Latn",
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"pl": "pol_Latn",
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"cs": "ces_Latn",
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"sk": "slk_Latn",
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"sl": "slv_Latn",
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"hu": "hun_Latn",
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"ro": "ron_Latn",
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"el": "ell_Grek",
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"ru": "rus_Cyrl",
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"uk": "ukr_Cyrl",
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"tr": "tur_Latn",
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"ar": "arb_Arab",
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"fa": "pes_Arab",
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"he": "heb_Hebr",
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"zh": "zho_Hans",
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"ja": "jpn_Jpan",
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"ko": "kor_Hang",
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"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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# Check if model exists AND is complete (all required files present and non-empty)
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required_files = ["model.bin", "config.json", "shared_vocabulary.json"]
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model_exists = os.path.exists(model_bin)
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if model_exists:
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# Verify all files exist and have reasonable size
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all_files_ok = True
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for f in required_files:
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fpath = os.path.join(model_path, f)
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if not os.path.exists(fpath) or os.path.getsize(fpath) < 100:
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all_files_ok = False
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break
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if not all_files_ok:
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LOG.info(f"Model files incomplete or corrupted, re-converting...")
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# Clean up corrupted files
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for f in required_files:
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try:
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fpath = os.path.join(model_path, f)
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if os.path.exists(fpath):
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os.remove(fpath)
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except:
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pass
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model_exists = False
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if not model_exists:
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LOG.info(
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f"CTranslate2 model not found at {model_path}, converting from {UNIVERSAL_MODEL}..."
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)
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convert_model()
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device = os.environ.get("CT2_DEVICE", "cpu")
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LOG.info(f"Loading CTranslate2 model from {model_path} on {device}")
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_translator = ctranslate2.Translator(
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model_path,
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device=device,
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compute_type=CT2_COMPUTE_TYPE,
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inter_threads=CT2_INTER_THREADS,
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intra_threads=CT2_INTRA_THREADS,
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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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lock_file = os.path.join(model_path, ".converting.lock")
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# Clean up any corrupted files from previous failed conversions
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if os.path.exists(model_path):
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for f in os.listdir(model_path):
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if f.endswith(".lock") or f.startswith("."):
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try:
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os.remove(os.path.join(model_path, f))
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except:
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pass
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os.makedirs(model_path, exist_ok=True)
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# Use lock file to prevent multiple workers from converting simultaneously
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lock_fd = os.open(lock_file, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
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try:
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quantization = CT2_COMPUTE_TYPE if CT2_COMPUTE_TYPE != "auto" else "float16"
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cmd = [
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"ct2-transformers-converter",
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"--model",
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UNIVERSAL_MODEL,
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"--output_dir",
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model_path,
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"--quantization",
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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=3600)
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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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finally:
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os.close(lock_fd)
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try:
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os.remove(lock_file)
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except:
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pass
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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=1,
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max_decoding_length=MAX_NEW_TOKENS,
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repetition_penalty=1.2,
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no_repeat_ngram_size=2,
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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 = translate_texts(src, tgt, chunks)
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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(
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r.get("titulo") or ""
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)
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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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"""
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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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""",
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(titulo, resumen, r.get("tr_id")),
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)
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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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{
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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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)
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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(
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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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""",
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tr_ids,
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)
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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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# Collect all body chunks across all items for a single batched call
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flat_chunks = []
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flat_keys = []
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for item in items:
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body = (item["resumen"] or "").strip()
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if body:
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chunks = split_body_into_chunks(body)
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flat_chunks.extend(chunks)
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flat_keys.extend([(item["tr_id"], i) for i in range(len(chunks))])
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translated_bodies = []
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if flat_chunks:
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try:
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translated_bodies = translate_texts(lang_from, lang_to, flat_chunks)
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except Exception as e:
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LOG.error(f"Batch body translation error: {e}")
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translated_bodies = flat_chunks
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body_parts = defaultdict(list)
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for (tr_id, _), tr in zip(flat_keys, translated_bodies):
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if tr is None:
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continue
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body_parts[tr_id].append(tr)
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for idx, item in enumerate(items):
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tt = clean_text((translated_titles[idx] or "").strip())
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parts = body_parts.get(item["tr_id"])
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tb = clean_text(" ".join(parts).strip()) if parts else ""
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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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"""
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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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""",
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(tt, tb, item["tr_id"]),
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)
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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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"""
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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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""",
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[i["tr_id"] for i in items],
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)
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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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total_found = 0
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for lang in TARGET_LANGS:
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cursor.execute(
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"""
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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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""",
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(lang, BATCH_SIZE),
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)
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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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total_found += len(rows)
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cursor.close()
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return total_found
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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(
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f"CTranslate2 translator worker started (device={CT2_DEVICE}, instances={TRANSLATOR_TOTAL})"
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)
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ensure_model()
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|
|
while True:
|
|
try:
|
|
conn = connect_db()
|
|
total = fetch_pending_translations(conn)
|
|
conn.close()
|
|
|
|
if total == 0:
|
|
LOG.info("No pending translations, sleeping...")
|
|
else:
|
|
LOG.info(f"Processed {total} translations, sleeping...")
|
|
except Exception as e:
|
|
LOG.error(f"Error: {e}")
|
|
|
|
time.sleep(30)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|