471 lines
20 KiB
Python
471 lines
20 KiB
Python
import json
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import logging
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import os
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from typing import List, Dict, Any, Optional, Tuple, Callable
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import re
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import sqlite3
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from application.strategies import OllamaExtractor
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from application.database import DatabaseManager
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from core.pdf_utils import document_to_images_bs64
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from core.utils import collect_radicado_docs
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logger = logging.getLogger(__name__)
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import shutil
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import pandas as pd
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class PreclassificationService:
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def __init__(self, extractor: OllamaExtractor):
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self.extractor = extractor
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self.db = DatabaseManager()
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def export_to_excel(self, run_id: int, output_path: str) -> bool:
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"""Exporta los resultados de una ejecución a Excel (xlsx) o CSV."""
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try:
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results = self.db.get_run_results(run_id)
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if not results:
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return False
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# Pivotar para tener filas únicas por archivo
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# get_run_results devuelve una fila por cada key encontrada en cada archivo
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# Necesitamos agrupar por (radicado, file_path)
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data_map = {}
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keys_seen = set()
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for row in results:
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file_id = f"{row['radicado']}|{row['file_path']}"
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if file_id not in data_map:
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data_map[file_id] = {
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"Radicado": row["radicado"],
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"Archivo": row["file_path"],
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"Status": row["status"]
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}
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k = row["key"]
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if k:
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# found_value puede ser None
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val = row["found_value"]
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exists = row["exists_bool"]
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# Podemos poner el valor o un indicador
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data_map[file_id][k] = val if exists else "NO_FOUND"
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keys_seen.add(k)
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# Convertir a lista
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final_data = list(data_map.values())
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df = pd.DataFrame(final_data)
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# Asegurar todas las columnas de keys
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for k in keys_seen:
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if k not in df.columns:
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df[k] = None
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if output_path.endswith(".csv"):
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df.to_csv(output_path, index=False, encoding="utf-8-sig", sep=";")
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else:
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df.to_excel(output_path, index=False)
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return True
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except Exception as e:
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logger.exception("Error exportando a Excel")
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raise e
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def organize_files(self, run_id: int, base_output_path: str) -> tuple[int, int]:
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"""
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Organiza los archivos MATCH en carpetas jerárquicas.
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Devuelve (total_copiados, total_errores).
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"""
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# Obtener modo del Run
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# Necesitamos leer config del Run
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conn = sqlite3.connect(self.db.db_path)
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c = conn.cursor()
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c.execute("SELECT config FROM runs WHERE id = ?", (run_id,))
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row = c.fetchone()
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conn.close()
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mode = "radicados"
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if row and row[0]:
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try:
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cfg = json.loads(row[0])
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mode = str(cfg.get("mode", "radicados") or "radicados").strip().lower()
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except Exception:
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mode = "radicados"
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if mode not in ("radicados", "general"):
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mode = "radicados"
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results = self.db.get_run_results(run_id)
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# Filtrar solo MATCH
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match_rows = [r for r in results if r["status"] == "MATCH"]
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if not match_rows:
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return 0, 0
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# Agrupar por archivo para tener el "set" de datos de cada uno
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files_data = {} # { file_path: {key: val, ...}, 'radicado': ... }
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key_values_all = {} # { key: set(values) }
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for row in match_rows:
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fpath = row["file_path"]
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if not fpath: continue
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if fpath not in files_data:
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files_data[fpath] = {"radicado": row["radicado"], "keys": {}}
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k = row["key"]
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v = row["found_value"]
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if k and v:
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files_data[fpath]["keys"][k] = v
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if k not in key_values_all:
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key_values_all[k] = set()
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key_values_all[k].add(v)
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# Determinar orden de jerarquía por cardinalidad
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# Lista de (key, cardinalidad)
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hierarchy = []
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for k, val_set in key_values_all.items():
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hierarchy.append((k, len(val_set)))
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# Ordenar ascendente: primero los que tienen menos valores (ej. Categorias generales)
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hierarchy.sort(key=lambda x: x[1])
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ordered_keys = [x[0] for x in hierarchy]
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logger.info(f"Jerarquía de carpetas calculada: {ordered_keys}")
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count_ok = 0
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count_err = 0
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for fpath, info in files_data.items():
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try:
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# Construir ruta relativa
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# Sanitizar valores para que sean nombres de carpeta válidos
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path_parts = []
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for k in ordered_keys:
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val = info["keys"].get(k, "DESCONOCIDO")
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safe_val = self._sanitize_filename(str(val))
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# KeyName / ValueName
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safe_key = self._sanitize_filename(k)
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path_parts.append(safe_key)
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path_parts.append(safe_val)
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# Añadir radicado al final solo si NO es modo general (en general el radicado es el archivo)
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# Si mode es radicados: ... / Radicado / Archivo
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# Si mode es general: ... / Archivo
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if mode != "general":
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path_parts.append(self._sanitize_filename(info["radicado"]))
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dest_dir = os.path.join(base_output_path, *path_parts)
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os.makedirs(dest_dir, exist_ok=True)
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fname = os.path.basename(fpath)
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dest_file = os.path.join(dest_dir, fname)
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# Evitar sobrescrituras (especialmente en modo general sin radicado)
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if os.path.exists(dest_file):
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base, ext = os.path.splitext(fname)
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n = 1
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while True:
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candidate = os.path.join(dest_dir, f"{base}_{n}{ext}")
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if not os.path.exists(candidate):
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dest_file = candidate
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break
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n += 1
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# Copiar
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shutil.copy2(fpath, dest_file)
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count_ok += 1
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except Exception as e:
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logger.error(f"Error organizando {fpath}: {e}")
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count_err += 1
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return count_ok, count_err
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def _sanitize_filename(self, name: str) -> str:
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"""Limpia caracteres inválidos para rutas de archivo."""
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# Reemplazar caracteres no alfanuméricos (excepto espacio, guion, punto)
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clean = re.sub(r'[\\/*?:"<>|]', "", name)
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clean = clean.replace("\n", " ").replace("\r", "").strip()
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if not clean:
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return "UNTITLED"
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return clean[:50] # Limitar longitud
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def run_preclassification(
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self,
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root_path: str,
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patterns: List[Dict[str, str]], # [{key, mode, value?}, ...]
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target_info: Dict[str, Any], # {enabled, definition, pattern}
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page_range: Tuple[bool, int, int], # (enabled, start, end)
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on_progress: Optional[Callable[[str, int, int], None]] = None,
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mode: str = "radicados" # "radicados" | "general"
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) -> int:
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"""
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Ejecuta el flujo de preclasificación.
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Devuelve el run_id.
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"""
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# 1. Crear Run en BD
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mode = str(mode or "radicados").strip().lower()
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if mode not in ("radicados", "general"):
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mode = "radicados"
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run_config = {
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"patterns": patterns,
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"target": target_info,
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"range": page_range,
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"mode": mode
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}
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run_id = self.db.create_run(root_path, run_config)
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# 2. Descubrir items a procesar
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radicados = []
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if os.path.isdir(root_path):
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if mode == "general":
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# En modo general, consideramos que la raiz es el único "contenedor"
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# o iteramos sobre una lista dummy.
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# Para simplificar el loop principal, usaremos una lista con un elemento "."
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radicados = ["."]
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else:
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radicados = sorted([
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d for d in os.listdir(root_path)
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if os.path.isdir(os.path.join(root_path, d))
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])
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total = len(radicados)
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if mode == "general":
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# Si es general, quizás queramos reportar progreso por archivo, no por carpeta "."
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# Pero _identify_files nos da los archivos.
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pass
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# Preparar prompt
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keys_to_extract = [p["key"] for p in patterns]
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prompt = self._build_prompt(keys_to_extract)
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for idx, rad in enumerate(radicados, start=1):
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# Si es general, rad es ".", no es util para el usuario ver "Procesando ."
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# Ajustaremos el reporte dentro del loop de archivos si es general
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if mode != "general" and on_progress:
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on_progress(rad, idx, total)
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rad_path = os.path.join(root_path, rad)
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# 3. Identificar archivos a procesar
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files_to_process = self._identify_files(rad_path, target_info, mode)
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logger.info(f"Radicado/Carpeta: {rad} - Archivos identificados: {len(files_to_process)}")
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# Ordenar archivos para consistencia
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files_to_process.sort()
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if not files_to_process:
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if mode == "general":
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# Registrar una sola vez que no había archivos
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self.db.add_result(run_id, "GENERAL", None, "NO_FILES", "")
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return run_id
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else:
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self.db.add_result(run_id, rad, None, "NO_FILES", "")
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continue
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total_files = len(files_to_process)
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for f_idx, file_path in enumerate(files_to_process, 1):
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# En modo general, el "radicado" lógico será el nombre del archivo sin extensión
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# En modo radicados, es el nombre de la carpeta (variable rad)
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current_radicado = rad
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# Definir radicado para modo general ANTES de cualquier lógica
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if mode == "general":
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base_name = os.path.basename(file_path)
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name_no_ext = os.path.splitext(base_name)[0]
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# Validar que no quede vacio ni sea un punto
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if name_no_ext and name_no_ext.strip() not in (".", ""):
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current_radicado = name_no_ext.strip()
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else:
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current_radicado = base_name
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# --- CORRECCIÓN FORZADA: Si sigue siendo un punto o vacío, usar timestamp o hash ---
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if str(current_radicado).strip() in (".", ""):
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import time
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current_radicado = f"FILE_{int(time.time()*1000)}_{f_idx}"
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logger.info(f"Procesando archivo [{f_idx}/{total_files}]: {file_path} -> Radicado: {current_radicado}")
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if on_progress:
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try:
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# Asegurar que pasamos un string válido
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safe_rad = str(current_radicado)
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on_progress(safe_rad, f_idx, total_files)
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except Exception:
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pass # Evitar romper el loop si la GUI falla
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try:
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# 4. Convertir a imágenes
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# Manejo de rangos
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tiff_range = None
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if page_range[0]: # enabled
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# document_to_images_bs64 espera (start, end)
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# Nota: pdf_pages_to_images_bs64 usa start, end.
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# document_to_images_bs64 usa tiff_page_range=(start, end).
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# Para PDF hay que ver como lo manejamos si usamos document_to_images_bs64
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# La función document_to_images_bs64 no expone rango para PDF directamente,
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# pero pdf_to_images_bs64 sí via pdf_pages_to_images_bs64?
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# No, pdf_to_images_bs64 convierte todo.
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# Vamos a usar document_to_images_bs64 y si es PDF y hay rango, lo cortamos post-procesamiento o usamos función específica.
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# Mejor: document_to_images_bs64 llama a pdf_to_images_bs64 que NO filtra.
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# Debemos implementar logica de rango aqui.
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pass
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images_b64 = self._get_images(file_path, page_range)
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if not images_b64:
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self.db.add_result(run_id, rad, file_path, "ERROR_IMG", "No images generated")
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continue
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# 5. LLM
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response = self.extractor.extract(images_b64, prompt)
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raw_text = response.get("raw", "")
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# 6. Parseo y Validación
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parsed = self._parse_json_response(raw_text)
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# Evaluar reglas
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is_match = True
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# Si no hay patrones definidos, quizás asumimos match si responde JSON válido?
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# Pero el usuario pidió validar patrones.
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# Guardar resultado cabecera
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result_id = self.db.add_result(run_id, current_radicado, file_path, "PROCESSED", raw_text)
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# Guardar detalles y calcular match global
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global_match = True
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if not parsed:
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global_match = False
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# Guardar error de parseo?
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else:
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for pattern in patterns:
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key = pattern["key"]
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pat_mode = pattern.get("mode", "key")
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expected_val = pattern.get("value", "").lower().strip()
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# Buscar en respuesta
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# Normalizamos claves para busqueda insensible a mayusculas
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found_item = None
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for k, v in parsed.items():
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if k.lower() == key.lower():
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found_item = v
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break
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exists = False
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found_val = None
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if found_item and isinstance(found_item, dict):
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exists = bool(found_item.get("exists", False))
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found_val = str(found_item.get("value", "") or "")
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# Regla
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rule_ok = False
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if pat_mode == "key":
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rule_ok = exists
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elif pat_mode == "key+value":
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# Comparacion laxa
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rule_ok = exists and (expected_val in found_val.lower())
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if not rule_ok:
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global_match = False
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self.db.add_match(result_id, key, expected_val if pat_mode=="key+value" else None, found_val, exists)
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# Actualizar status final
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final_status = "MATCH" if global_match else "NO_MATCH"
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self._update_result_status(result_id, final_status)
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except Exception as e:
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logger.exception(f"Error procesando {file_path}")
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self.db.add_result(run_id, current_radicado, file_path, f"ERROR: {str(e)}", "")
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return run_id
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def _identify_files(self, rad_path: str, target_info: Dict[str, Any], mode: str = "radicados") -> List[str]:
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"""Devuelve lista de rutas de archivos a procesar."""
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if not os.path.exists(rad_path):
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return []
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# Si mode es general, rad_path es la raiz y buscamos archivos directos.
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# Si mode es radicados, rad_path es la subcarpeta del radicado.
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# La logica es la misma: listar archivos en el directorio dado.
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all_files = []
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try:
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for f in os.listdir(rad_path):
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full_path = os.path.join(rad_path, f)
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if os.path.isfile(full_path) and f.lower().endswith(('.pdf', '.tif', '.tiff')):
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all_files.append(full_path)
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except Exception:
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pass
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if not target_info.get("enabled"):
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return all_files
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# Filtrar por patron
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pattern = target_info.get("pattern", "*").lower()
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# Convertir glob simple a regex o usar fnmatch
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import fnmatch
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filtered = [f for f in all_files if fnmatch.fnmatch(os.path.basename(f).lower(), pattern)]
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return filtered
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def _get_images(self, file_path: str, page_range: Tuple[bool, int, int]) -> List[str]:
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"""Convierte archivo a imagenes, aplicando rango si es necesario."""
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enabled, start, end = page_range
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# Si rango habilitado, usar lógica especifica
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if enabled:
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ext = os.path.splitext(file_path)[1].lower()
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if ext == ".pdf":
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from core.pdf_utils import pdf_pages_to_images_bs64
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# pdf_pages_to_images_bs64 espera start, end (inclusive)
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# Validar indices positivos
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s = max(1, start)
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e = max(1, end)
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if s > e: s, e = e, s
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return pdf_pages_to_images_bs64(file_path, s, e, dpi=300) # DPI medio para velocidad?
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elif ext in (".tif", ".tiff"):
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# document_to_images_bs64 soporta tiff_page_range
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s = max(1, start)
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e = max(1, end)
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if s > e: s, e = e, s
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return document_to_images_bs64(file_path, tiff_page_range=(s, e), dpi=300)
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# Sin rango
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return document_to_images_bs64(file_path, dpi=300)
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def _build_prompt(self, keys: List[str]) -> str:
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keys_str = ", ".join(keys)
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return f"""Responde con true o false si tiene los siguientes campos, si existe extrae cuales son.
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Responde ÚNICAMENTE en JSON con el siguiente formato, sin bloques de código markdown:
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{{
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"nombre_campo": {{
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"exists": true,
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"value": "valor extraído"
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}},
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...
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}}
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Campos a buscar: {keys_str}"""
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def _parse_json_response(self, text: str) -> Dict[str, Any]:
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try:
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# Limpiar bloques markdown si el modelo los pone
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clean = text.replace("```json", "").replace("```", "").strip()
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return json.loads(clean)
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except Exception:
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# Intento de rescate simple con regex si falla json puro
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# Por ahora retornamos vacio para indicar fallo
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return {}
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def _update_result_status(self, result_id: int, status: str):
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# Actualización hacky directa porque DatabaseManager no tiene update expuesto
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# Podría agregar update_result en DatabaseManager, pero por brevedad:
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import sqlite3
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conn = sqlite3.connect(self.db.db_path)
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c = conn.cursor()
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c.execute("UPDATE results SET status = ? WHERE id = ?", (status, result_id))
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conn.commit()
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conn.close()
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