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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”»εƒγ‚­γƒ£γƒ—γ‚·γƒ§γƒ³η”Ÿζˆ",
             description="γ‚’γƒƒγƒ—γƒ­γƒΌγƒ‰γ•γ‚ŒγŸη”»εƒγ«ε―Ύγ—γ¦γ‚­γƒ£γƒ—γ‚·γƒ§γƒ³γ‚’θ‡ͺε‹•η”Ÿζˆγ—γΎγ™"
             ).launch()