from typing import  Dict, List, Any
from PIL import Image
import requests
import torch
from blip import blip_decoder
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

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


class PreTrainedPipeline():
    def __init__(self, path=""):
        # load the optimized model
        self.model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
        self.model = blip_decoder(pretrained=self.model_url, image_size=384, vit='large')
        self.model.eval()
        self.model = model.to(device)
        
        image_size = 384
        self.transform = transforms.Compose([
            transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
            transforms.ToTensor(),
            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
            ]) 
     


    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        image = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        image = transform(image).unsqueeze(0).to(device)   
        with torch.no_grad():
            caption = self.model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
        # postprocess the prediction
        return caption