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| import gradio as gr | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from PIL import Image | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| def generate_caption(image): | |
| inputs = processor(images=image, return_tensors="pt") | |
| out = model.generate(**inputs) | |
| caption = processor.decode(out[0], skip_special_tokens=True) | |
| return caption | |
| gr.Interface(fn=generate_caption, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title="η»εγγ£γγ·γ§γ³ηζ", | |
| description="γ’γγγγΌγγγγη»εγ«ε―Ύγγ¦γγ£γγ·γ§γ³γθͺεηζγγΎγ" | |
| ).launch() | |