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):
    image = Image.fromarray(input.astype('uint8'), 'RGB')
    predictions = pipe(image)
    return {prediction["label"]: prediction["score"] for prediction in predictions}


import gradio as gr

ex=[['cat2.jpg'],
    ['dog2.jpeg'],  
     ['cat3.jpg'],
     ['dog.jpeg']]

ex = []

"""
## RUNNING WEB UI"""

image = gr.Image()
label = gr.Label(num_top_classes=5)

description = "Categories: " + "'" + "', '".join(pipe.model.config.label2id.keys()) + "'"

gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Human Action Recognition',
             description=description, examples=ex, theme='peach').launch(height=1000, width=1600, debug=True, share=True)