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| from PIL import Image | |
| import numpy as np | |
| from transformers import AutoProcessor, BlipForConditionalGeneration | |
| # Load pretrained processor and model (shared for all functions) | |
| processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| def caption_image(input_image: np.ndarray): | |
| raw_image = Image.fromarray(input_image).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=50) | |
| caption = processor.decode(outputs[0], skip_special_tokens=True) | |
| return caption | |