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204d384 e1c2355 204d384 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import torch
from transformers import BlipProcessor, BlipForConditionalGeneration, T5Tokenizer, T5ForConditionalGeneration
import sentencepiece as spm
from PIL import Image
# Load the BLIP model and processor for image description
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
# Load the T5 model for text summarization
t5_tokenizer = T5Tokenizer.from_pretrained("t5-small")
t5_model = T5ForConditionalGeneration.from_pretrained("t5-small").to(device)
def generate_description(image: Image.Image) -> str:
"""
Generates a detailed description for the given image.
Parameters:
image (PIL.Image.Image): The input image.
Returns:
str: The generated description.
"""
inputs = processor(images=image, return_tensors="pt").to(device)
outputs = blip_model.generate(**inputs)
description = processor.decode(outputs[0], skip_special_tokens=True)
return description
def summarize_text_and_image(description: str, ocr_text: str) -> str:
"""
Generates a summary combining the image description and OCR-extracted text.
Parameters:
description (str): The generated description of the image.
ocr_text (str): The text extracted from the image using OCR.
Returns:
str: The generated summary.
"""
combined_input = f"Image Description: {description} Text: {ocr_text}"
input_ids = t5_tokenizer.encode(combined_input, return_tensors="pt", truncation=True).to(device)
outputs = t5_model.generate(input_ids, max_length=50, num_beams=4, early_stopping=True)
summary = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
return summary
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