app.py

from transformers import pipeline
import gradio as gr

# Step 1: Load a pre-trained model for image classification
model = pipeline("image-classification", model="google/vit-base-patch16-224")

# Step 2: Define a function for classifying images
def classify_image(image):
    predictions = model(image)
    return predictions

# Step 3: Create a Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs="image",  # Input is an image
    outputs="label",  # Output is a label (e.g., "sitting", "standing")
    title="Pose Detection: Sitting or Standing"
)

# Step 4: Launch the app
if __name__ == "__main__":
    interface.launch()