101 lines
4.7 KiB
Python
101 lines
4.7 KiB
Python
# services/invoice_processor_service.py
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import logging
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from typing import Dict, List, Any, Optional
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from google.cloud.documentai_v1.types import Document
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from .gcp_document_ai_client import process_document_gcp
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from .utils import data_cleaner
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from core.config import settings
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def _extract_specific_fields(
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document: Document,
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default_confidence_override: Optional[float] = None
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) -> Dict[str, str]:
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"""
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Extrae datos usando una lógica de búsqueda contextual por palabra clave para
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resolver ambigüedades en el documento.
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"""
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extracted_data = {field: "Not found or low confidence" for field in settings.REQUIRED_FIELDS}
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default_threshold = default_confidence_override if default_confidence_override is not None else settings.CONFIDENCE_THRESHOLDS["__default__"]
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full_text_lines = document.text.split('\n')
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for entity in document.entities:
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entity_type = entity.type_
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if entity_type not in settings.REQUIRED_FIELDS or entity_type in ['total_tax_amount', 'subtotal_amount']:
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continue
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threshold = settings.CONFIDENCE_THRESHOLDS.get(entity_type, default_threshold)
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if entity.confidence >= threshold:
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raw_text = entity.mention_text.strip()
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if entity_type == 'invoice_date':
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extracted_data[entity_type] = data_cleaner.normalize_date(raw_text) or f"Unparseable Date: '{raw_text}'"
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elif entity_type == 'total_amount':
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# --- LÓGICA DE BÚSQUEDA CONTEXTUAL POR PALABRA CLAVE ---
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contextual_line = None
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logging.info(f"Buscando contexto para '{raw_text}' con la palabra clave 'Total'")
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for line in full_text_lines:
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# La línea debe contener el texto del importe Y la palabra "total"
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if raw_text in line and "total" in line.lower():
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contextual_line = line
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logging.info(f"Contexto definitivo para total_amount encontrado: '{contextual_line}'")
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break
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# Si no encontramos una línea contextual, usamos el texto original como fallback
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text_to_parse = contextual_line if contextual_line else raw_text
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parsed_amounts = data_cleaner.parse_total_and_tax(text_to_parse)
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total_str = parsed_amounts.get('total_amount')
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tax_str = parsed_amounts.get('total_tax_amount')
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if total_str:
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extracted_data['total_amount'] = total_str
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if tax_str:
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extracted_data['total_tax_amount'] = tax_str
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try:
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subtotal = float(total_str) - float(tax_str)
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subtotal_str = f"{subtotal:.2f}"
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extracted_data['subtotal_amount'] = subtotal_str
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extracted_data['net_amount'] = subtotal_str
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except (ValueError, TypeError):
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logging.error("Error de conversión para cálculo de subtotal.")
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else:
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extracted_data['total_tax_amount'] = '0.00'
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extracted_data['subtotal_amount'] = total_str
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if extracted_data.get('net_amount') == "Not found or low confidence":
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extracted_data['net_amount'] = total_str
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elif entity_type in ['net_amount', 'subtotal_amount']:
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# Evitamos procesar estos campos directamente si ya los hemos calculado
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if extracted_data.get(entity_type) == "Not found or low confidence":
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extracted_data[entity_type] = data_cleaner.clean_numeric_value(raw_text)
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else:
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extracted_data[entity_type] = raw_text.replace('\n', ' ').strip()
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return extracted_data
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def process_invoice_from_bytes(
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file_bytes: bytes,
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mime_type: str,
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default_confidence_override: Optional[float] = None
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) -> Dict[str, str]:
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""" Orquesta el proceso completo. """
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try:
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document = process_document_gcp(
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project_id=settings.GCP_PROJECT_ID,
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location=settings.GCP_LOCATION,
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processor_id=settings.DOCAI_PROCESSOR_ID,
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file_bytes=file_bytes,
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mime_type=mime_type,
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)
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validated_data = _extract_specific_fields(document, default_confidence_override)
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logging.info(f"Datos finales procesados: {validated_data}")
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return validated_data
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except Exception as e:
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logging.error(f"Error en el flujo de procesamiento de factura: {e}", exc_info=True)
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raise |