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

class EndpointHandler():
    def __init__(self, path=""):
        """
        path: 
        """
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.processor = Blip2Processor.from_pretrained(path)
        self.model = Blip2ForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to(self.device)
        
    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        data args:
            inputs (:obj: `str` | `PIL.Image` | `np.array`)
            kwargs
        Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        result = {}
        inputs = data.pop("inputs", data)
        image_url = inputs['image_url']
        if "prompt" in inputs:
            prompt = inputs["prompt"]
        else:
            prompt = None
        image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
        if prompt:
            processed_image = self.processor(images=image, text=prompt, return_tensors="pt").to(self.device, torch.float16)
        else:
            processed_image = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
        output = self.model.generate(**processed_image)
        text_output = self.processor.decode(output[0], skip_special_tokens=True)
        result["text_output"] = text_output
        return result