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import gradio as gr | |
from transformers import BlipForConditionalGeneration | |
from transformers import AutoProcessor | |
model = BlipForConditionalGeneration.from_pretrained( | |
"Salesforce/blip-image-captioning-base") | |
processor = AutoProcessor.from_pretrained( | |
"Salesforce/blip-image-captioning-base") | |
def image_caption(input): | |
inputs = processor(input, return_tensors="pt") | |
out = model.generate(**inputs) | |
return processor.decode(out[0], skip_special_tokens = True) | |
iface = gr.Interface(fn = image_caption, | |
inputs = gr.Image(type='pil'), | |
outputs = "text") | |
iface.launch() |