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jlimolina
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6784d81c2c
141 changed files with 25219 additions and 0 deletions
414
workers/ner_worker.py
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414
workers/ner_worker.py
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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 string
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from typing import List, Tuple
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from collections import Counter
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import psycopg2
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import psycopg2.extras
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import spacy
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from bs4 import BeautifulSoup
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# ==========================================================
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# Logging
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# ==========================================================
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logging.basicConfig(
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level=logging.INFO,
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format='[NER] %(asctime)s %(levelname)s: %(message)s'
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)
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log = logging.getLogger("ner_worker")
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# ==========================================================
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# Config DB
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# ==========================================================
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DB = dict(
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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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NER_LANG = os.environ.get("NER_LANG", "es").strip().lower()
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BATCH = int(os.environ.get("NER_BATCH", 64))
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# ==========================================================
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# Mapeo de entidades spaCy → nuestro modelo SQL
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# ==========================================================
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ENT_LABELS = {
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"PERSON": "persona",
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"PER": "persona",
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"ORG": "organizacion",
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"GPE": "lugar",
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"LOC": "lugar",
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"MISC": "tema",
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}
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# ==========================================================
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# Limpieza avanzada
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# ==========================================================
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_ws_re = re.compile(r"\s+")
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HTML_TRASH_PATTERNS = [
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r"<[^>]+>",
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r"&[a-z]+;",
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r"&#\d+;?",
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r'width="\d+"',
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r'height="\d+"',
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]
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GENERIC_BAD_TAGS = {
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"república", "estado", "centro", "gobierno", "el gobierno",
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"gobiernos", "report", "sp", "unión", "union", "dólares",
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"dolar", "dólar", "the post", "post", "artículo", "el artículo",
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"la ciudad", "mundo", "país", "pais", "países", "paises",
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"la noche", "la publicación", "este miércoles", "el miércoles",
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"hoy", "ayer", "mañana", "servicio", "servicios", "el presidente",
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"presidente", "el ministro", "ministro", "la guerra", "guerra",
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"seguridad", "wp-content", "internal_photos", "/internal_photos",
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"https", "http", "src"
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}
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STOPWORDS = set()
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ARTICLES = {
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"el", "la", "los", "las", "un", "una", "uno", "al", "del"
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}
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# Límites
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TOPIC_MIN_CHARS = 4
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TOPIC_MAX_WORDS = 6
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TOPIC_MAX_PER_DOC = 15
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# ==========================================================
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# Helpers
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# ==========================================================
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def get_conn():
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return psycopg2.connect(**DB)
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def _looks_like_attr_or_path(text_lower: str) -> bool:
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"""Filtra basura tipo rutas, html, atributos, URLs, etc."""
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if text_lower.startswith("/"):
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return True
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if "http://" in text_lower or "https://" in text_lower:
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return True
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if any(ext in text_lower for ext in (".jpg", ".jpeg", ".png", ".gif", ".webp")):
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return True
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if re.search(r"\b(src|alt|style|class)\s*=", text_lower):
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return True
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if "data-" in text_lower:
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return True
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if re.search(r"&#\d+;?", text_lower):
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return True
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if "=" in text_lower and " " not in text_lower.strip():
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return True
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# tokens-hash tipo "ajsdh7287sdhjshd8" (solo si no tiene espacios)
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if " " not in text_lower and re.fullmatch(r"[a-z0-9_]{15,}", text_lower.replace("-", "")):
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return True
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# palabras sin espacios largas con guiones
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if "-" in text_lower and " " not in text_lower:
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return True
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return False
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# ==========================================================
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# Limpieza de entidades
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# ==========================================================
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def clean_tag_text(text: str) -> str | None:
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if not text:
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return None
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text = BeautifulSoup(text, "html.parser").get_text()
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for pat in HTML_TRASH_PATTERNS:
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text = re.sub(pat, "", text)
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text = _ws_re.sub(" ", text).strip()
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text = text.strip(string.punctuation + " ")
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if len(text) < 3:
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log.debug(f"Clean reject (too short): {text}")
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return None
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if re.search(r"[<>/\\]", text):
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log.debug(f"Clean reject (bad chars): {text}")
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return None
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lower = text.lower()
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if lower.startswith("href="):
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log.debug(f"Clean reject (href): {text}")
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return None
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if _looks_like_attr_or_path(lower):
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log.debug(f"Clean reject (attr/path): {text}")
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return None
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if lower in GENERIC_BAD_TAGS:
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log.debug(f"Clean reject (generic bad): {text}")
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return None
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replacements = {
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"ee.uu.": "Estados Unidos",
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"los estados unidos": "Estados Unidos",
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"eeuu": "Estados Unidos",
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"eu": "Unión Europea",
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"ue": "Unión Europea",
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"kosova": "Kosovo",
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# Specific User Requests
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"trump": "Donald Trump",
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"mr. trump": "Donald Trump",
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"mr trump": "Donald Trump",
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"doland trump": "Donald Trump",
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"el presidente trump": "Donald Trump",
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"president trump": "Donald Trump",
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"ex-president trump": "Donald Trump",
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"expresidente trump": "Donald Trump",
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"putin": "Vladimir Putin",
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"vladimir putin": "Vladimir Putin",
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"v. putin": "Vladimir Putin",
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"presidente putin": "Vladimir Putin",
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# New requests
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"sanchez": "Pedro Sánchez",
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"pedro sanchez": "Pedro Sánchez",
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"p. sanchez": "Pedro Sánchez",
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"mr. sanchez": "Pedro Sánchez",
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"sánchez": "Pedro Sánchez", # explicit match just in case
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"pedro sánchez": "Pedro Sánchez",
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"maduro": "Nicolás Maduro",
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"nicolas maduro": "Nicolás Maduro",
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"mr. maduro": "Nicolás Maduro",
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"lula": "Lula da Silva",
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"lula da silva": "Lula da Silva",
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"luiz inácio lula da silva": "Lula da Silva",
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}
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if lower in replacements:
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return replacements[lower]
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# Blacklist (explicit removals requested)
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blacklist = {
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"getty images", "netflix", "fiscalia", "segun", "estoy", # People blacklist
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"and more", "app", "estamos", "ultra", # Orgs blacklist
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"hacienda", "fiscalía"
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}
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if lower in blacklist:
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log.debug(f"Clean reject (blacklist): {text}")
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return None
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return text
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# ==========================================================
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# Limpieza de topics (noun-chunks)
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# ==========================================================
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def clean_topic_text(text: str) -> str | None:
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if not text:
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return None
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text = BeautifulSoup(text, "html.parser").get_text()
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for pat in HTML_TRASH_PATTERNS:
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text = re.sub(pat, "", text)
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text = _ws_re.sub(" ", text).strip()
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text = text.strip(string.punctuation + " ")
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if len(text) < TOPIC_MIN_CHARS:
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return None
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lower = text.lower()
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if _looks_like_attr_or_path(lower):
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return None
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tokens = [
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t.strip(string.punctuation)
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for t in lower.split()
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if t.strip(string.punctuation)
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]
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if not tokens:
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return None
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# remover artículos iniciales
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if tokens[0] in ARTICLES:
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tokens = tokens[1:]
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if not tokens:
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return None
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norm = " ".join(tokens).strip()
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if len(norm) < TOPIC_MIN_CHARS:
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return None
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if norm in GENERIC_BAD_TAGS:
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return None
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if len(tokens) > TOPIC_MAX_WORDS:
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return None
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if all(t in STOPWORDS for t in tokens):
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return None
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if re.fullmatch(r"[0-9\s\.,\-:/]+", norm):
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return None
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return norm
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# ==========================================================
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# Extracción NER + Topics
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# ==========================================================
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def extract_entities_and_topics(nlp, text: str) -> Tuple[List[Tuple[str, str]], List[Tuple[str, str]]]:
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ents = []
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topics = []
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if not text:
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return ents, topics
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doc = nlp(text)
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# log.debug(f"Processing text ({len(text)} chars): {text[:30]}...")
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# log.debug(f"Entities found: {len(doc.ents)}")
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# --- ENTIDADES ---
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for ent in doc.ents:
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tipo = ENT_LABELS.get(ent.label_)
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if not tipo:
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continue
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cleaned = clean_tag_text(ent.text)
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if not cleaned:
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# log.debug(f"Rejected entity: {ent.text} ({ent.label_})")
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continue
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if tipo == "persona":
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lower_cleaned = cleaned.lower()
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# Aggressive normalization rules for VIPs
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# Use token checks or substring checks carefully
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if "trump" in lower_cleaned.split():
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# Token 'trump' exists? e.g. "donald trump", "trump", "mr. trump"
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# Exclude family members
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family = ["ivanka", "melania", "eric", "tiffany", "barron", "lara", "mary", "fred"]
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if not any(f in lower_cleaned for f in family):
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cleaned = "Donald Trump"
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elif "sanchez" in lower_cleaned or "sánchez" in lower_cleaned:
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# Be careful of other Sanchez? But user context implies Pedro.
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if "pedro" in lower_cleaned or "presidente" in lower_cleaned or lower_cleaned in ["sanchez", "sánchez"]:
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cleaned = "Pedro Sánchez"
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elif "maduro" in lower_cleaned:
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cleaned = "Nicolás Maduro"
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elif "lula" in lower_cleaned:
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cleaned = "Lula da Silva"
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elif "putin" in lower_cleaned:
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cleaned = "Vladimir Putin"
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# log.debug(f"Accepted entity: {cleaned} ({tipo})")
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ents.append((cleaned, tipo))
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# --- TOPICS ---
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topic_counter = Counter()
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for chunk in doc.noun_chunks:
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cleaned = clean_topic_text(chunk.text)
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if cleaned:
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topic_counter[cleaned] += 1
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ent_values = {v for (v, _) in ents}
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for val, _count in topic_counter.most_common(TOPIC_MAX_PER_DOC):
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if val in ent_values:
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continue
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topics.append((val, "tema"))
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return list(set(ents)), list(set(topics))
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# ==========================================================
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# Worker principal
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# ==========================================================
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def main():
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global STOPWORDS
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# Cargar spaCy
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log.info("Cargando modelo spaCy es_core_news_md...")
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nlp = spacy.load("es_core_news_md", disable=["lemmatizer", "textcat"])
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STOPWORDS = set(nlp.Defaults.stop_words)
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log.info("Modelo spaCy cargado correctamente.")
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while True:
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try:
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with get_conn() as conn, conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
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cur.execute(
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"""
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SELECT t.id, t.titulo_trad, t.resumen_trad
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FROM traducciones t
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WHERE t.status = 'done'
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AND t.lang_to = %s
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AND NOT EXISTS (
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SELECT 1 FROM tags_noticia tn WHERE tn.traduccion_id = t.id
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)
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ORDER BY t.id DESC
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LIMIT %s;
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""",
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(NER_LANG, BATCH),
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)
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rows = cur.fetchall()
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if not rows:
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time.sleep(5)
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continue
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log.info(f"Procesando {len(rows)} traducciones para NER/temas...")
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inserted_links = 0
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for r in rows:
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text = f"{r['titulo_trad'] or ''}\n{r['resumen_trad'] or ''}".strip()
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if not text:
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continue
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ents, topics = extract_entities_and_topics(nlp, text)
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tags = ents + topics
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if not tags:
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continue
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for valor, tipo in tags:
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try:
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cur.execute(
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"""
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INSERT INTO tags (valor, tipo)
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VALUES (%s, %s)
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ON CONFLICT (valor, tipo)
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DO UPDATE SET valor = EXCLUDED.valor
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RETURNING id;
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""",
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(valor, tipo),
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)
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tag_id = cur.fetchone()[0]
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cur.execute(
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"""
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INSERT INTO tags_noticia (traduccion_id, tag_id)
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VALUES (%s, %s)
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ON CONFLICT DO NOTHING;
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""",
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(r["id"], tag_id),
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)
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if cur.rowcount > 0:
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inserted_links += 1
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except Exception:
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log.exception("Error insertando tag/relación")
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conn.commit()
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log.info(f"Lote NER OK. Nuevas relaciones tag_noticia: {inserted_links}")
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except Exception:
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log.exception("Error general en NER loop")
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time.sleep(5)
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if __name__ == "__main__":
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main()
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