handler.py

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
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
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)



        self.processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
        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.
        """
        print("********* Helllo ***********")
        print(data)
        img_data = data.pop("input", data)
        prompt = data.pop("prompt", None)
        print("#########")
        # parameters = data.pop("parameters", {})

        if isinstance(img_data, Image.Image):
            raw_image = img_data
        else:
            inputs = isinstance(img_data, str) and [img_data] or img_data
            # raw_image = [Image.open(BytesIO(base64.b64decode(_img))) for _img in inputs]
            raw_image = Image.open(BytesIO(base64.b64decode(img_data)))
                                     
        # 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)

        ##############
        # img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
        # raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
        
        inputs = processor(raw_image, prompt, return_tensors="pt")
        
        out = model.generate(**inputs)
        captions = processor.decode(out[0], skip_special_tokens=True)
        
        return {"captions": captions}