30 lines
962 B
Python
30 lines
962 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, BlipForConditionalGeneration
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import gradio as gr
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
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def image2text(image):
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inp = Image.fromarray(image.astype('uint8'), 'RGB')
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text = "a photography of"
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inputs = processor(inp, text, 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=image2text,
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inputs='image',
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outputs='text',
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title = "image2text",
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examples = ['soccer.jpg'])
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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 = 7017)
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