135 lines
5.6 KiB
Python
Executable File
135 lines
5.6 KiB
Python
Executable File
# /opt/docker/dev/service_finder/backend/app/services/document_service.py
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import os
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import logging
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import asyncio
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from PIL import Image
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from uuid import uuid4
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from datetime import datetime, timezone
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from fastapi import UploadFile, BackgroundTasks, HTTPException
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func, and_
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from app.models.document import Document
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from app.models.identity import User
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from app.services.config_service import config # 2.0 Dinamikus beállítások
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from app.workers.ocr.robot_1_ocr_processor import OCRRobot # Robot 1 hívása
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logger = logging.getLogger("Document-Service-2.0")
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class DocumentService:
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"""
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Document Service 2.0 - Admin-vezérelt Pipeline.
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Feladata: Tárolás, Optimalizálás, Kvótamanagement és Robot Trigger.
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"""
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@staticmethod
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async def process_upload(
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db: AsyncSession,
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user_id: int,
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file: UploadFile,
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parent_type: str, # pl. "asset", "organization", "transfer"
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parent_id: str,
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doc_type: str, # pl. "invoice", "registration_card", "sale_contract"
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background_tasks: BackgroundTasks
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):
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try:
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# --- 1. ADMIN KVÓTA ELLENŐRZÉS ---
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user_stmt = select(User).where(User.id == user_id)
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user = (await db.execute(user_stmt)).scalar_one()
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# Lekérjük a csomagnak megfelelő havi limitet (pl. Free: 1, Premium: 10)
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limits = await config.get_setting(db, "ocr_monthly_limit", default={"free": 1, "premium": 10, "vip": 100})
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user_role = user.role.value if hasattr(user.role, 'value') else str(user.role)
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allowed_ocr = limits.get(user_role, 1)
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# Megnézzük a havi felhasználást
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now = datetime.now(timezone.utc)
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start_of_month = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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count_stmt = select(func.count(Document.id)).where(
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and_(
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Document.user_id == user_id,
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Document.created_at >= start_of_month
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)
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)
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used_count = (await db.execute(count_stmt)).scalar()
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if used_count >= allowed_ocr:
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raise HTTPException(
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status_code=403,
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detail=f"Havi dokumentum limit túllépve ({allowed_ocr}). Válts csomagot a folytatáshoz!"
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)
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# --- 2. DINAMIKUS TÁROLÁS ÉS OPTIMALIZÁLÁS ---
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file_uuid = str(uuid4())
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nas_base = await config.get_setting(db, "storage_nas_path", default="/mnt/nas/app_data")
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# Útvonal sablonok az adminból (Pl. "vault/{parent_type}/{parent_id}")
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vault_dir = os.path.join(nas_base, parent_type, parent_id, "vault")
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thumb_dir = os.path.join(getattr(config, "STATIC_DIR", "static"), "previews", parent_type, parent_id)
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os.makedirs(vault_dir, exist_ok=True)
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os.makedirs(thumb_dir, exist_ok=True)
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content = await file.read()
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temp_path = f"/tmp/{file_uuid}_{file.filename}"
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with open(temp_path, "wb") as f: f.write(content)
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# Kép feldolgozása PIL-lel
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img = Image.open(temp_path)
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# Thumbnail generálás (SSD/Static területre)
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thumb_filename = f"{file_uuid}_thumb.webp"
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thumb_path = os.path.join(thumb_dir, thumb_filename)
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thumb_img = img.copy()
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thumb_img.thumbnail((300, 300))
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thumb_img.save(thumb_path, "WEBP", quality=80)
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# Optimalizált eredeti (NAS / Vault területre)
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max_width = await config.get_setting(db, "img_max_width", default=1600)
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vault_filename = f"{file_uuid}.webp"
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vault_path = os.path.join(vault_dir, vault_filename)
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if img.width > max_width:
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ratio = max_width / img.width
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img = img.resize((max_width, int(img.height * ratio)), Image.Resampling.LANCZOS)
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img.save(vault_path, "WEBP", quality=85)
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# --- 3. MENTÉS ---
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new_doc = Document(
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id=uuid4(),
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user_id=user_id,
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parent_type=parent_type,
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parent_id=parent_id,
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doc_type=doc_type,
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original_name=file.filename,
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file_hash=file_uuid,
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file_ext="webp",
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mime_type="image/webp",
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file_size=os.path.getsize(vault_path),
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has_thumbnail=True,
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thumbnail_path=f"/static/previews/{parent_type}/{parent_id}/{thumb_filename}",
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status="uploaded"
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)
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db.add(new_doc)
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await db.flush()
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# --- 4. ROBOT TRIGGER (OCR AUTOMATIZMUS) ---
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# Megnézzük, hogy ez a típus (pl. invoice) igényel-e automatikus OCR-t
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auto_ocr_types = await config.get_setting(db, "ocr_auto_trigger_types", default=["invoice", "registration_card", "sale_contract"])
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if doc_type in auto_ocr_types:
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# Robot 1 (OCR) sorba állítása háttérfolyamatként
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background_tasks.add_task(OCRRobot.process_document, db, new_doc.id)
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new_doc.status = "processing"
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logger.info(f"🤖 Robot 1 (OCR) riasztva: {new_doc.id}")
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await db.commit()
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os.remove(temp_path)
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return new_doc
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except Exception as e:
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if 'temp_path' in locals() and os.path.exists(temp_path): os.remove(temp_path)
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logger.error(f"Document Upload Error: {e}")
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raise e |