import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from typing import Dict, List, Any
import torch

class EndpointHandler():
    def __init__(self, path=""):
        self.base_model_name = "Salesforce/blip2-opt-2.7b"
        self.model_name = "sooh-j/blip2-vizwizqa"
        self.base_model = Blip2ForConditionalGeneration.from_pretrained(self.base_model_name,
                                                               load_in_8bit=True)
        self.processor = Blip2Processor.from_pretrained(self.base_model_name)
        self.model = PeftModel.from_pretrained(self.model_name, self.base_model_name)

        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model.to(self.device)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        data = data.pop("inputs", data)

        image = data.image
        question = data.question
        
        prompt = f"Question: {question}, Answer:"
        processed = self.processor(images=image, prompt, return_tensors="pt").to(self.device)

        out = self.model.generate(**processed)

        return self.processor.decode(out[0], skip_special_tokens=True)