app.py

# Use a pipeline as a high-level helper
from transformers import pipeline
import gradio as gr

image_processor = pipeline("image-classification", model="google/vit-base-patch16-224")

# Define a Gradio function for classification
def classify_image(image):
    # Use the image_classification pipeline to classify the image
    result = image_processor(image)
    # Return the class label and confidence score
    return result[0]["label"], round(result[0]["score"], 4)

# Create a Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    live=True,
    title="Image Classification",
)

# Start the Gradio interface
interface.launch()