from typing import  Dict, List, Any
from PIL import Image
import base64
import torch
import os
from io import BytesIO
from transformers import BlipForConditionalGeneration, BlipProcessor

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class EndpointHandler():
    def __init__(self, path=""):
        # load the optimized model
        
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") 
        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
        self.model.eval()
        self.model = self.model.to(device)
        


    def __call__(self, data: Any) -> Dict[str, Any]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`dict`:. The object returned should be a dict of one list like {"captions": ["A hugging face at the office"]} containing :
                - "caption": A string corresponding to the generated caption.
        """
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", {})

        print(inputs)
        if isinstance(inputs, Image.Image):
            raw_images = [inputs]
        else:
            inputs = isinstance(inputs, str) and [inputs] or inputs
            raw_images = [Image.open(BytesIO(base64.b64decode(_img))) for _img in inputs]
                                     
        processed_images = self.processor(images=raw_images, return_tensors="pt")
        processed_images["pixel_values"] = processed_images["pixel_values"].to(device)
        processed_images = {**processed_images, **parameters}
        
        with torch.no_grad():
            out = self.model.generate(**processed_images)
        captions = self.processor.batch_decode(out, skip_special_tokens=True)

        return {"captions": captions}