Cleanup: MB 2.0 Gap Analysis előtti állapot (adatok kizárva)

backend/app/services/ai_service1.1.0.py

@@ -0,0 +1,141 @@
+import os
+import json
+import logging
+import asyncio
+import re
+import base64
+import httpx
+from typing import Dict, Any, Optional, List
+from sqlalchemy import select
+from app.db.session import SessionLocal
+from app.models import SystemParameter
+
+logger = logging.getLogger("AI-Service")
+
+class AIService:
+    """
+    AI Service v1.3.5 - Private High-Performance Edition
+    - Engine: Local Ollama (GPU Accelerated)
+    - Features: DVLA Integration, 50-char Normalization, Private OCR
+    """
+    
+    # A Docker belső hálózatán a szerviznév 'ollama'
+    OLLAMA_BASE_URL = "http://ollama:11434/api/generate"
+    TEXT_MODEL = "vehicle-pro"
+    VISION_MODEL = "llava:7b"
+    DVLA_API_KEY = os.getenv("DVLA_API_KEY")
+
+    @classmethod
+    async def get_config_delay(cls) -> float:
+        """Késleltetés lekérése az adatbázisból."""
+        try:
+            async with SessionLocal() as db:
+                stmt = select(SystemParameter).where(SystemParameter.key == "AI_REQUEST_DELAY")
+                res = await db.execute(stmt)
+                param = res.scalar_one_or_none()
+                return float(param.value) if param else 0.1
+        except Exception: 
+            return 0.1
+
+    @classmethod
+    async def get_clean_vehicle_data(cls, make: str, raw_model: str, v_type: str, sources: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Robot 2: Adat-összefésülés és normalizálás."""
+        # Várjunk egy kicsit a GPU kímélése érdekében
+        await asyncio.sleep(await cls.get_config_delay())
+
+        prompt = f"""
+        FELADAT: Normalizáld a jármű adatait több forrás alapján.
+        GYÁRTÓ: {make}
+        NYERS MODELLNÉV: {raw_model}
+        FORRÁSOK NYERS ADATAI: {json.dumps(sources, ensure_ascii=False)}
+
+        SZIGORÚ SZABÁLYOK:
+        1. 'marketing_name': MAXIMUM 50 KARAKTER!
+        2. 'synonyms': Gyűjtsd ide az összes többi névváltozatot.
+        3. 'technical_code': Keresd meg a gyári kódokat.
+        
+        VÁLASZ FORMÁTUM (Csak tiszta JSON):
+        {{
+            "marketing_name": "string (max 50)",
+            "synonyms": ["string"],
+            "technical_code": "string",
+            "ccm": int,
+            "kw": int,
+            "euro_class": int,
+            "year_from": int,
+            "year_to": int vagy null,
+            "maintenance": {{
+                "oil_type": "string",
+                "oil_qty": float,
+                "spark_plug": "string"
+            }},
+            "is_duplicate_potential": bool
+        }}
+        """
+
+        payload = {
+            "model": cls.TEXT_MODEL,
+            "prompt": prompt,
+            "stream": False,
+            "format": "json",
+            "options": {"temperature": 0.1}
+        }
+
+        try:
+            async with httpx.AsyncClient(timeout=90.0) as client:
+                logger.info(f"📡 AI kérés küldése: {make} {raw_model}...")
+                response = await client.post(cls.OLLAMA_BASE_URL, json=payload)
+                response.raise_for_status()
+                res_json = response.json()
+                clean_data = json.loads(res_json.get("response", "{}"))
+                
+                if clean_data.get("marketing_name"):
+                    clean_data["marketing_name"] = clean_data["marketing_name"][:50].strip()
+                
+                return clean_data
+        except Exception as e:
+            logger.error(f"❌ AI hiba ({make} {raw_model}): {e}")
+            return None
+
+    @classmethod
+    async def get_dvla_data(cls, vrm: str) -> Optional[Dict[str, Any]]:
+        """Brit rendszám alapú adatok lekérése."""
+        if not cls.DVLA_API_KEY: return None
+        url = "https://driver-vehicle-licensing.api.gov.uk/vehicle-enquiry/v1/vehicles"
+        headers = {"x-api-key": cls.DVLA_API_KEY, "Content-Type": "application/json"}
+        try:
+            async with httpx.AsyncClient() as client:
+                resp = await client.post(url, json={"registrationNumber": vrm}, headers=headers)
+                return resp.json() if resp.status_code == 200 else None
+        except Exception as e:
+            logger.error(f"❌ DVLA API hiba: {e}")
+            return None
+
+    @classmethod
+    async def analyze_document_image(cls, image_data: bytes, doc_type: str) -> Optional[Dict[str, Any]]:
+        """Robot 3: Helyi OCR és dokumentum elemzés (Llava:7b)."""
+        await asyncio.sleep(await cls.get_config_delay())
+        prompts = {
+            "identity": "Extract ID card data (name, id_number, expiry) as JSON.",
+            "vehicle_reg": "Extract vehicle registration (plate, VIN, power_kw, engine_ccm) as JSON.",
+            "invoice": "Extract invoice details (vendor, total_amount, date) as JSON.",
+            "odometer": "Identify the number on the odometer and return as JSON: {'value': int}."
+        }
+        img_b64 = base64.b64encode(image_data).decode('utf-8')
+        payload = {
+            "model": cls.VISION_MODEL,
+            "prompt": prompts.get(doc_type, "Perform OCR and return JSON"),
+            "images": [img_b64],
+            "stream": False,
+            "format": "json"
+        }
+        try:
+            async with httpx.AsyncClient(timeout=120.0) as client:
+                response = await client.post(cls.OLLAMA_BASE_URL, json=payload)
+                res_data = response.json()
+                clean_json = res_data.get("response", "{}")
+                match = re.search(r'\{.*\}', clean_json, re.DOTALL)
+                return json.loads(match.group()) if match else json.loads(clean_json)
+        except Exception as e:
+            logger.error(f"❌ Helyi OCR hiba: {e}")
+            return None