27 lines
863 B
Python
27 lines
863 B
Python
import gradio as gr
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from transformers import AutoImageProcessor, ResNetForImageClassification
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import torch
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processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
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model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
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def image_classification(image):
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inputs = processor(image, return_tensors="pt")
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with torch.no_grad():
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logits = model(**inputs).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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examples = ['dog.jpeg'])
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if __name__ == "__main__":
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demo.queue(concurrency_count=3)
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demo.launch(server_name = "0.0.0.0", server_port = 7027)
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