46dea3304f
This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage. Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import os
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from app.services.ai_service import AIModelManager, AIService
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import logging
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import random
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from typing import Tuple
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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# ----------------------
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# Lazy-initialized AI service
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# ----------------------
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_ai_service: AIService = None
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def init_ai_service() -> AIService:
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"""Initializes the AI service if not already initialized."""
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global _ai_service
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if _ai_service is None:
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logger.debug("Initializing AI service...")
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try:
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model_manager = AIModelManager()
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_ai_service = AIService(model_manager)
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logger.info("AI service ready.")
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except Exception as e:
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logger.warning(f"Failed to initialize AI service: {e}. Using mock service.")
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# Create a mock AI service for now
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_ai_service = MockAIService()
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return _ai_service
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def get_ai_service() -> AIService:
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"""Returns the initialized AI service."""
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return init_ai_service()
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# Mock AI service for testing when models can't be loaded
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class MockAIService:
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def classify_category(self, image_path: str) -> str:
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categories = ["pothole", "streetlight", "garbage", "signage", "drainage", "other"]
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return random.choice(categories)
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def detect_pothole_severity(self, image_path: str) -> Tuple[str, str]:
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severities = ["High", "Medium", "Low"]
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severity = random.choice(severities)
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return severity, image_path # Return same path as annotated path
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