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from __future__ import annotations |
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import gradio as gr |
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import PIL.Image |
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import spaces |
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import torch |
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from transformers import AutoProcessor, BlipForConditionalGeneration |
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DESCRIPTION = "# Image Captioning with LongCap" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_id = "unography/blip-long-cap" |
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processor = AutoProcessor.from_pretrained(model_id) |
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model = BlipForConditionalGeneration.from_pretrained(model_id).to(device) |
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@spaces.GPU(duration=30) |
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def run(image: PIL.Image.Image) -> str: |
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inputs = processor(images=image, return_tensors="pt").to(device) |
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out = model.generate(pixel_values=inputs.pixel_values, num_beams=3, max_length=300) |
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generated_caption = processor.decode(out[0], skip_special_tokens=True) |
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return generated_caption |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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input_image = gr.Image(type="pil") |
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run_button = gr.Button("Caption") |
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output = gr.Textbox(label="Result") |
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run_button.click( |
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fn=run, |
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inputs=input_image, |
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outputs=output, |
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api_name="caption", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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