utils.py

# utils.py
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch

class ImageCaptioningModel:
    def __init__(self, model_name="Salesforce/blip-image-captioning-base"):
        """
        Initialize BLIP Image Captioning model.
        """
        self.processor = BlipProcessor.from_pretrained(model_name)
        self.model = BlipForConditionalGeneration.from_pretrained(model_name)
        self.model.eval()

    def generate_caption(self, image_path):
        """
        Generate a caption for the given image.
        :param image_path: Path to the input image
        :return: Generated caption (string)
        """
        image = Image.open(image_path).convert("RGB")
        inputs = self.processor(images=image, return_tensors="pt")

        with torch.no_grad():
            output = self.model.generate(**inputs)

        caption = self.processor.tokenizer.decode(output[0], skip_special_tokens=True)
        return caption