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from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
class ImageToText:
"""
Class to handle Image-to-Text captioning using BLIP.
"""
def __init__(self):
# Initialize the processor and model
print("Loading Image-to-Text model...")
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
print("Image-to-Text model loaded successfully.")
async def generate_caption(self, image):
"""
Generate a descriptive caption for an uploaded image.
Args:
image (PIL.Image): The image to caption.
Returns:
str: The generated caption.
"""
inputs = self.processor(image, return_tensors="pt")
out = self.model.generate(**inputs)
caption = self.processor.decode(out[0], skip_special_tokens=True)
return caption
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