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| from PIL import Image |
| from transformers import pipeline |
| pipe = pipeline("image-classification", "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224") |
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| def classify_image(input_data): |
| image = Image.fromarray(input_data.astype('uint8'), 'RGB') |
| predictions = pipe(image) |
| return {prediction["label"]: prediction["score"] for prediction in predictions} |
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| import gradio as gr |
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| image = gr.Image() |
| label = gr.Label(num_top_classes=5) |
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| description = "## Categories: \n" + ", ".join(pipe.model.config.label2id.keys()) |
| examples = [["samples/cycling.jpg"], ["samples/dancing.webp"], ["samples/listening music.png"], ["samples/running.jpg"], ["samples/sleeping.webp"]] |
| theme = gr.themes.Default(primary_hue="red", secondary_hue="pink") |
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| gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Human Action Recognition', |
| description=description, examples=examples, theme=theme |
| ).launch(height=1000, width=1600, debug=True) |
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