# services/invoice_processor_service.py
import logging
from typing import Dict, List, Any, Optional
import json # Necesario para formatear el JSON de salida

# IMPORTAMOS EL TIPO Document y el MessageToJson para la depuración
from google.cloud.documentai_v1.types import Document
from google.protobuf.json_format import MessageToJson

from .gcp_document_ai_client import process_document_gcp
from .utils import data_cleaner
from core.config import settings

# --- La función _extract_specific_fields NO necesita cambios en esta fase de depuración ---
# Puedes dejar la versión anterior, ya que el problema está en los datos de entrada que recibe.
def _extract_specific_fields(
    document: Document,
    default_confidence_override: Optional[float] = None
) -> Dict[str, str]:
    # ... (código de la respuesta anterior, no es necesario cambiarlo ahora)
    extracted_data = {field: "Not found or low confidence" for field in settings.REQUIRED_FIELDS}
    default_threshold = default_confidence_override if default_confidence_override is not None else settings.CONFIDENCE_THRESHOLDS["__default__"]
    full_text_lines = document.text.split('\n')
    for entity in document.entities:
        entity_type = entity.type_
        if entity_type not in settings.REQUIRED_FIELDS or entity_type in ['total_tax_amount', 'subtotal_amount']:
            continue
        threshold = settings.CONFIDENCE_THRESHOLDS.get(entity_type, default_threshold)
        if entity.confidence >= threshold:
            raw_text = entity.mention_text.strip()
            if entity_type == 'invoice_date':
                extracted_data[entity_type] = data_cleaner.normalize_date(raw_text) or f"Unparseable Date: '{raw_text}'"
            elif entity_type == 'total_amount':
                contextual_line = None
                logging.info(f"Buscando contexto para '{raw_text}' con la palabra clave 'Total'")
                for line in full_text_lines:
                    if raw_text in line and "total" in line.lower():
                        contextual_line = line
                        logging.info(f"Contexto definitivo para total_amount encontrado: '{contextual_line}'")
                        break
                text_to_parse = contextual_line if contextual_line else raw_text
                parsed_amounts = data_cleaner.parse_total_and_tax(text_to_parse)
                total_str = parsed_amounts.get('total_amount')
                tax_str = parsed_amounts.get('total_tax_amount')
                if total_str:
                    extracted_data['total_amount'] = total_str
                    if tax_str:
                        extracted_data['total_tax_amount'] = tax_str
                        try:
                            subtotal = float(total_str) - float(tax_str)
                            subtotal_str = f"{subtotal:.2f}"
                            extracted_data['subtotal_amount'] = subtotal_str
                            extracted_data['net_amount'] = subtotal_str
                        except (ValueError, TypeError):
                            logging.error("Error de conversión para cálculo de subtotal.")
                    else:
                        extracted_data['total_tax_amount'] = '0.00'
                        extracted_data['subtotal_amount'] = total_str
                        if extracted_data.get('net_amount') == "Not found or low confidence":
                             extracted_data['net_amount'] = total_str
            elif entity_type in ['net_amount', 'subtotal_amount']:
                 if extracted_data.get(entity_type) == "Not found or low confidence":
                    extracted_data[entity_type] = data_cleaner.clean_numeric_value(raw_text)
            else:
                extracted_data[entity_type] = raw_text.replace('\n', ' ').strip()
    return extracted_data


def process_invoice_from_bytes(
    file_bytes: bytes, 
    mime_type: str,
    default_confidence_override: Optional[float] = None
) -> Dict[str, str]:
    """ Orquesta el proceso completo e imprime la respuesta de GCP para depuración. """
    try:
        document = process_document_gcp(
            project_id=settings.GCP_PROJECT_ID,
            location=settings.GCP_LOCATION,
            processor_id=settings.DOCAI_PROCESSOR_ID,
            file_bytes=file_bytes,
            mime_type=mime_type,
        )

        # --- INICIO DEL BLOQUE DE DEPURACIÓN ---
        # Convertimos la respuesta completa del objeto 'Document' a un JSON legible.
        document_json = MessageToJson(document._pb)
        # Lo cargamos como un objeto Python para poder formatearlo bonito (indentado).
        document_dict = json.loads(document_json)
        
        # Imprimimos en el log de la consola con un formato claro.
        logging.critical("\n\n" + "="*20 + " INICIO RESPUESTA COMPLETA DE DOCUMENT AI " + "="*20)
        logging.critical(json.dumps(document_dict, indent=2, ensure_ascii=False))
        logging.critical("="*20 + " FIN RESPUESTA COMPLETA DE DOCUMENT AI " + "="*20 + "\n\n")
        # --- FIN DEL BLOQUE DE DEPURACIÓN ---

        validated_data = _extract_specific_fields(document, default_confidence_override)
        logging.info(f"Datos finales procesados: {validated_data}")
        return validated_data
        
    except Exception as e:
        logging.error(f"Error en el flujo de procesamiento de factura: {e}", exc_info=True)
        raise