from transformers import pipeline
import torch
from PIL import Image
from io import BytesIO
import base64
from typing import Dict, List, Any

class EndpointHandler():
    def __init__(self, model_path=""):
        # Check if a GPU is available
        if torch.cuda.is_available():
            device = 0
            gpu_info = torch.cuda.get_device_name(device)
            print(f"Using GPU: {gpu_info}")
        else:
            device = -1
            cpu_info = torch.get_num_threads()
            print(f"Using CPU with {cpu_info} threads")
            
        # Initialize the pipeline with the specified model and set the device to GPU
        self.pipeline = pipeline(task="zero-shot-object-detection", model=model_path, device=device)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Process an incoming request for zero-shot object detection.
        
        Args:
            data (Dict[str, Any]): The input data containing an encoded image and candidate labels.
            
        Returns:
            A list of dictionaries, each containing a label and its corresponding score.
        """
        # Correctly accessing the 'inputs' key and fixing the typo in 'candidates'
        inputs = data.get("inputs", {})
        
        # Decode the base64 image to a PIL image
        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))

        # Get candidate labels
        candidate_labels=inputs["candidates"]

        # Correctly passing the image and candidate labels to the pipeline
        detection_results = self.pipeline(image=image, candidate_labels=inputs["candidates"], threshold = 0)
        
        # Adjusting the return statement to match the expected output structure
        return detection_results