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import gradio as gr |
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from transformers import pipeline, AutoImageProcessor, Swinv2ForImageClassification, Swinv2Model |
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from torchvision import transforms |
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image_processor = AutoImageProcessor.from_pretrained("haywoodsloan/ai-image-detector-deploy") |
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model = Swinv2ForImageClassification.from_pretrained("haywoodsloan/ai-image-detector-deploy") |
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clf = pipeline(model=model, task="image-classification", image_processor=image_processor) |
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class_names = ['artificial', 'real'] |
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def predict_image(img): |
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img = transforms.ToPILImage()(img) |
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img = transforms.Resize((256,256))(img) |
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prediction=clf.predict(img) |
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return {class_names[i]: float(prediction[i]["score"]) for i in range(2)} |
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image = gr.Image(label="Image to Analyze", sources=['upload']) |
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label = gr.Label(num_top_classes=2) |
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gr.Interface(fn=predict_image, inputs=image, outputs=label, title="AI Generated Classification").launch() |
