Create summarization_model.py

summarization_model.py

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+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration, T5Tokenizer, T5ForConditionalGeneration
+
+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