141 lines
5.5 KiB
Python
Executable File
141 lines
5.5 KiB
Python
Executable File
import os
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import json
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import logging
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import asyncio
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import re
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import base64
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import httpx
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from typing import Dict, Any, Optional, List
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from sqlalchemy import select
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from app.db.session import SessionLocal
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from app.models import SystemParameter
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logger = logging.getLogger("AI-Service")
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class AIService:
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"""
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AI Service v1.3.5 - Private High-Performance Edition
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- Engine: Local Ollama (GPU Accelerated)
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- Features: DVLA Integration, 50-char Normalization, Private OCR
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"""
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# A Docker belső hálózatán a szerviznév 'ollama'
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OLLAMA_BASE_URL = "http://ollama:11434/api/generate"
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TEXT_MODEL = "vehicle-pro"
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VISION_MODEL = "llava:7b"
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DVLA_API_KEY = os.getenv("DVLA_API_KEY")
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@classmethod
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async def get_config_delay(cls) -> float:
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"""Késleltetés lekérése az adatbázisból."""
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try:
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async with SessionLocal() as db:
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stmt = select(SystemParameter).where(SystemParameter.key == "AI_REQUEST_DELAY")
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res = await db.execute(stmt)
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param = res.scalar_one_or_none()
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return float(param.value) if param else 0.1
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except Exception:
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return 0.1
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@classmethod
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async def get_clean_vehicle_data(cls, make: str, raw_model: str, v_type: str, sources: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""Robot 2: Adat-összefésülés és normalizálás."""
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# Várjunk egy kicsit a GPU kímélése érdekében
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await asyncio.sleep(await cls.get_config_delay())
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prompt = f"""
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FELADAT: Normalizáld a jármű adatait több forrás alapján.
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GYÁRTÓ: {make}
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NYERS MODELLNÉV: {raw_model}
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FORRÁSOK NYERS ADATAI: {json.dumps(sources, ensure_ascii=False)}
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SZIGORÚ SZABÁLYOK:
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1. 'marketing_name': MAXIMUM 50 KARAKTER!
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2. 'synonyms': Gyűjtsd ide az összes többi névváltozatot.
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3. 'technical_code': Keresd meg a gyári kódokat.
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VÁLASZ FORMÁTUM (Csak tiszta JSON):
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{{
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"marketing_name": "string (max 50)",
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"synonyms": ["string"],
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"technical_code": "string",
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"ccm": int,
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"kw": int,
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"euro_class": int,
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"year_from": int,
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"year_to": int vagy null,
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"maintenance": {{
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"oil_type": "string",
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"oil_qty": float,
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"spark_plug": "string"
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}},
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"is_duplicate_potential": bool
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}}
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"""
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payload = {
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"model": cls.TEXT_MODEL,
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"prompt": prompt,
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"stream": False,
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"format": "json",
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"options": {"temperature": 0.1}
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}
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try:
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async with httpx.AsyncClient(timeout=90.0) as client:
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logger.info(f"📡 AI kérés küldése: {make} {raw_model}...")
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response = await client.post(cls.OLLAMA_BASE_URL, json=payload)
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response.raise_for_status()
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res_json = response.json()
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clean_data = json.loads(res_json.get("response", "{}"))
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if clean_data.get("marketing_name"):
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clean_data["marketing_name"] = clean_data["marketing_name"][:50].strip()
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return clean_data
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except Exception as e:
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logger.error(f"❌ AI hiba ({make} {raw_model}): {e}")
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return None
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@classmethod
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async def get_dvla_data(cls, vrm: str) -> Optional[Dict[str, Any]]:
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"""Brit rendszám alapú adatok lekérése."""
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if not cls.DVLA_API_KEY: return None
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url = "https://driver-vehicle-licensing.api.gov.uk/vehicle-enquiry/v1/vehicles"
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headers = {"x-api-key": cls.DVLA_API_KEY, "Content-Type": "application/json"}
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try:
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async with httpx.AsyncClient() as client:
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resp = await client.post(url, json={"registrationNumber": vrm}, headers=headers)
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return resp.json() if resp.status_code == 200 else None
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except Exception as e:
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logger.error(f"❌ DVLA API hiba: {e}")
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return None
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@classmethod
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async def analyze_document_image(cls, image_data: bytes, doc_type: str) -> Optional[Dict[str, Any]]:
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"""Robot 3: Helyi OCR és dokumentum elemzés (Llava:7b)."""
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await asyncio.sleep(await cls.get_config_delay())
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prompts = {
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"identity": "Extract ID card data (name, id_number, expiry) as JSON.",
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"vehicle_reg": "Extract vehicle registration (plate, VIN, power_kw, engine_ccm) as JSON.",
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"invoice": "Extract invoice details (vendor, total_amount, date) as JSON.",
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"odometer": "Identify the number on the odometer and return as JSON: {'value': int}."
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}
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img_b64 = base64.b64encode(image_data).decode('utf-8')
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payload = {
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"model": cls.VISION_MODEL,
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"prompt": prompts.get(doc_type, "Perform OCR and return JSON"),
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"images": [img_b64],
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"stream": False,
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"format": "json"
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}
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try:
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async with httpx.AsyncClient(timeout=120.0) as client:
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response = await client.post(cls.OLLAMA_BASE_URL, json=payload)
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res_data = response.json()
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clean_json = res_data.get("response", "{}")
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match = re.search(r'\{.*\}', clean_json, re.DOTALL)
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return json.loads(match.group()) if match else json.loads(clean_json)
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except Exception as e:
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logger.error(f"❌ Helyi OCR hiba: {e}")
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return None |