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# rvv-karma/Human-Action-Recognition
## Creating prediction pipeline
from PIL import Image
from transformers import pipeline
pipe = pipeline("image-classification", "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224")
def classify_image(input):
image = Image.fromarray(input.astype('uint8'), 'RGB')
predictions = pipe(image)
return {prediction["label"]: prediction["score"] for prediction in predictions}
# RUNNING WEB UI
import gradio as gr
image = gr.Image()
label = gr.Label(num_top_classes=5)
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")
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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