40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import gradio as gr
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from transformers import AutoImageProcessor, ResNetForImageClassification
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import torch
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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 = 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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css = "footer {visibility: hidden}",
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allow_flagging = "never",
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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")
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