from transformers import AutoImageProcessor, AutoModelForObjectDetection
import torch
from PIL import Image
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

try:
    logger.info("Loading facebook/detr-resnet-50 model and processor...")
    processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
    logger.info("Model and processor loaded successfully.")
except Exception as e:
    logger.error(f"Failed to load model or processor: {str(e)}")
    raise

def run_inference(image: Image.Image) -> dict:
    """
    Run object detection inference on the input image.
    
    Args:
        image (PIL.Image.Image): Input image for object detection.
    
    Returns:
        dict: Processed results containing bounding boxes, scores, and labels.
    """
    try:
        # Preprocess the image using AutoImageProcessor
        inputs = processor(images=image, return_tensors="pt")
        
        # Run inference
        with torch.no_grad():  # Disable gradient calculation for inference
            outputs = model(**inputs)
        
        # Post-process the output (get bounding boxes)
        target_sizes = torch.tensor([image.size[::-1]])  # Format: [height, width]
        results = processor.post_process_object_detection(
            outputs, target_sizes=target_sizes, threshold=0.9
        )[0]
        
        return results
    except Exception as e:
        logger.error(f"Error during inference: {str(e)}")
        raise