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| import gradio as gr | |
| from transformers import ViTForImageClassification, ViTProcessor | |
| # Load the model and processor | |
| model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224") | |
| processor = ViTProcessor.from_pretrained("google/vit-base-patch16-224") | |
| def predict_image(img): | |
| inputs = processor(img, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = outputs.logits.argmax(-1) | |
| return model.config.id2label[predictions.item()] | |
| # Create the interface | |
| iface = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.Image(shape=(224, 224)), | |
| outputs="text", | |
| live=True, | |
| capture_session=True, | |
| title="Image recognition", | |
| description="Upload an image you want to categorize.", | |
| theme="Monochrome" | |
| ) | |
| iface.launch() |