4NK_IA_back/services/host_api/tasks/notary_tasks.py

"""
Tâches de traitement des documents notariaux
"""
import asyncio
import logging
from typing import Dict, Any, Optional
from fastapi import UploadFile
import uuid
import time

from domain.models import ProcessingRequest
from utils.ocr_processor import OCRProcessor
from utils.document_classifier import DocumentClassifier
from utils.entity_extractor import EntityExtractor
from utils.external_apis import ExternalAPIManager
from utils.verification_engine import VerificationEngine
from utils.llm_client import LLMClient
from utils.storage import StorageManager

logger = logging.getLogger(__name__)

class NotaryDocumentProcessor:
    """Processeur principal pour les documents notariaux"""

    def __init__(self):
        self.ocr_processor = OCRProcessor()
        self.classifier = DocumentClassifier()
        self.entity_extractor = EntityExtractor()
        self.external_apis = ExternalAPIManager()
        self.verification_engine = VerificationEngine()
        self.llm_client = LLMClient()
        self.storage = StorageManager()

    async def process_document(
        self,
        document_id: str,
        file: UploadFile,
        request_data: ProcessingRequest,
        reprocess: bool = False,
        force_reclassification: bool = False,
        force_reverification: bool = False
    ) -> Dict[str, Any]:
        """
        Traitement complet d'un document notarial
        """
        start_time = time.time()
        logger.info(f"Début du traitement du document {document_id}")

        try:
            # 1. Sauvegarde du document original
            original_path = await self.storage.save_original_document(document_id, file)

            # 2. OCR et extraction du texte
            logger.info(f"OCR du document {document_id}")
            ocr_result = await self.ocr_processor.process_document(original_path)

            # 3. Classification du document
            logger.info(f"Classification du document {document_id}")
            classification_result = await self.classifier.classify_document(
                ocr_result["text"],
                expected_type=request_data.type_document_attendu,
                force_reclassification=force_reclassification
            )

            # 4. Extraction des entités
            logger.info(f"Extraction des entités du document {document_id}")
            entities = await self.entity_extractor.extract_entities(
                ocr_result["text"],
                document_type=classification_result["type"]
            )

            # 5. Vérifications externes
            logger.info(f"Vérifications externes du document {document_id}")
            verifications = await self._perform_external_verifications(entities)

            # 6. Calcul du score de vraisemblance
            logger.info(f"Calcul du score de vraisemblance du document {document_id}")
            credibility_score = await self.verification_engine.calculate_credibility_score(
                ocr_result,
                classification_result,
                entities,
                verifications
            )

            # 7. Génération de l'avis de synthèse via LLM
            logger.info(f"Génération de l'avis de synthèse du document {document_id}")
            synthesis = await self.llm_client.generate_synthesis(
                document_type=classification_result["type"],
                extracted_text=ocr_result["text"],
                entities=entities,
                verifications=verifications,
                credibility_score=credibility_score
            )

            # 8. Sauvegarde des résultats
            processing_result = {
                "document_id": document_id,
                "processing_time": time.time() - start_time,
                "ocr_result": ocr_result,
                "classification": classification_result,
                "entities": entities,
                "verifications": verifications,
                "credibility_score": credibility_score,
                "synthesis": synthesis,
                "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
                "request_data": request_data.dict()
            }

            await self.storage.save_processing_result(document_id, processing_result)

            logger.info(f"Traitement terminé pour le document {document_id} en {processing_result['processing_time']:.2f}s")

            return processing_result

        except Exception as e:
            logger.error(f"Erreur lors du traitement du document {document_id}: {e}")
            await self.storage.save_error_result(document_id, str(e))
            raise

    async def _perform_external_verifications(self, entities: Dict[str, Any]) -> Dict[str, Any]:
        """
        Effectue les vérifications externes basées sur les entités extraites
        """
        verifications = {}

        try:
            # Vérifications des adresses
            if "adresses" in entities:
                for address in entities["adresses"]:
                    # Vérification Cadastre
                    cadastre_result = await self.external_apis.verify_cadastre(address["adresse"])
                    verifications["cadastre"] = cadastre_result

                    # Vérification Géorisques
                    georisques_result = await self.external_apis.check_georisques(address["adresse"])
                    verifications["georisques"] = georisques_result

            # Vérifications des identités
            if "identites" in entities:
                for identity in entities["identites"]:
                    # Vérification BODACC
                    bodacc_result = await self.external_apis.check_bodacc(identity["nom"], identity["prenom"])
                    verifications["bodacc"] = bodacc_result

                    # Vérification Gel des avoirs
                    gel_result = await self.external_apis.check_gel_avoirs(identity["nom"], identity["prenom"])
                    verifications["gel_avoirs"] = gel_result

            # Vérifications des entreprises (si présentes)
            if "entreprises" in entities:
                for company in entities["entreprises"]:
                    # Vérification Infogreffe
                    infogreffe_result = await self.external_apis.check_infogreffe(company["nom"])
                    verifications["infogreffe"] = infogreffe_result

                    # Vérification RBE
                    rbe_result = await self.external_apis.check_rbe(company["nom"])
                    verifications["rbe"] = rbe_result

        except Exception as e:
            logger.error(f"Erreur lors des vérifications externes: {e}")
            verifications["error"] = str(e)

        return verifications

# Instance globale du processeur
processor = NotaryDocumentProcessor()

async def process_notary_document(
    document_id: str,
    file: UploadFile,
    request_data: ProcessingRequest,
    reprocess: bool = False,
    force_reclassification: bool = False,
    force_reverification: bool = False
):
    """
    Fonction principale de traitement d'un document notarial
    """
    try:
        result = await processor.process_document(
            document_id=document_id,
            file=file,
            request_data=request_data,
            reprocess=reprocess,
            force_reclassification=force_reclassification,
            force_reverification=force_reverification
        )

        # TODO: Notifier l'utilisateur de la fin du traitement
        # via WebSocket ou webhook

        return result

    except Exception as e:
        logger.error(f"Erreur fatale lors du traitement du document {document_id}: {e}")
        # TODO: Notifier l'utilisateur de l'erreur
        raise