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