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import gradio as gr |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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from PIL import Image |
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") |
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def caption_image(image): |
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inputs = processor(images=image, return_tensors="pt") |
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out = model.generate(**inputs) |
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caption = processor.decode(out[0], skip_special_tokens=True) |
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return caption |
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gr.Interface(fn=caption_image, inputs=gr.Image(), outputs="text").launch() |
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