36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import gradio as gr
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from transformers import ViTImageProcessor, ViTForImageClassification
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
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#model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
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model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-224-in21k')
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def image_classification(image):
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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predicted_label = logits.argmax(-1).item()
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return model.config.id2label[predicted_label]
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demo = gr.Interface(fn=image_classification,
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inputs=gr.Image(),
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outputs=gr.Label(num_top_classes=1),
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title = "图像分类",
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theme = theme,
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examples = ['dog.jpeg'])
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if __name__ == "__main__":
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demo.queue(concurrency_count=10)
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demo.launch(server_name = "0.0.0.0", share = True)
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