app.py

# 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_data):
    image = Image.fromarray(input_data.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)