28 lines
933 B
Python
28 lines
933 B
Python
import torch
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import requests
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from PIL import Image
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from transformers import BlipProcessor, BlipForQuestionAnswering
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import gradio as gr
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processor = BlipProcessor.from_pretrained("ybelkada/blip-vqa-base")
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model = BlipForQuestionAnswering.from_pretrained("ybelkada/blip-vqa-base", torch_dtype=torch.float16).to("cuda")
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def vqa(image, question):
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inp = Image.fromarray(image.astype('uint8'), 'RGB')
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inputs = processor(inp, question, return_tensors="pt").to("cuda", torch.float16)
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out = model.generate(**inputs)
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return processor.decode(out[0], skip_special_tokens=True)
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demo = gr.Interface(fn=vqa,
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inputs=['image', 'text'],
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outputs='text',
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title = "vqa",
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examples = [['soccer.jpg', 'how many people in the picture?']])
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7021)
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