from PIL import Image
import requests
from transformers import Blip2Processor, Blip2ForConditionalGeneration, BlipProcessor, BlipForConditionalGeneration
import torch
from utils.util import resize_long_edge


class ImageCaptioning:
    def __init__(self, device):
        self.device = device
        self.processor, self.model = self.initialize_model()

    def initialize_model(self):
        if self.device == 'cpu':
            self.data_type = torch.float32
        else:
            self.data_type = torch.float16
        # uncomment for load stronger captioner
        # processor = Blip2Processor.from_pretrained("pretrained_models/blip2-opt-2.7b")
        # model = Blip2ForConditionalGeneration.from_pretrained(
        #     "pretrained_models/blip2-opt-2.7b", torch_dtype=self.data_type
        # )
        processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
        model.to(self.device)
        return processor, model

    def image_caption(self, image_src):
        image = Image.open(image_src)
        image = resize_long_edge(image)
        inputs = self.processor(images=image, return_tensors="pt").to(self.device, self.data_type)
        generated_ids = self.model.generate(**inputs)
        generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
        print('\033[1;35m' + '*' * 100 + '\033[0m')
        print('\nStep1, BLIP2 caption:')
        print(generated_text)
        print('\033[1;35m' + '*' * 100 + '\033[0m')
        return generated_text
    
    def image_caption_debug(self, image_src):
        return "A dish with salmon, broccoli, and something yellow."