| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from PIL import Image | |
| # Load the pre-trained BLIP model and processor | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| def generate_blip_caption(image_path): | |
| """ | |
| Generates a caption for a given image using the BLIP model. | |
| Args: | |
| image_path (str): The path to the image file. | |
| Returns: | |
| str: The generated caption. | |
| """ | |
| # Open the image | |
| image = Image.open(image_path).convert("RGB") | |
| # Preprocess the image and generate the caption | |
| inputs = processor(images=image, return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| # Decode the generated caption | |
| caption = processor.decode(outputs[0], skip_special_tokens=True) | |
| return caption | |