app.py

import torch
import gradio as gr
from transformers import pipeline

device = "cuda" if torch.cuda.is_available() else "cpu"

def predict(image):
    classifier = pipeline(task="image-classification")
    preds = classifier(image)
    return {pred["label"]: round(float(pred["score"]), 4) for pred in preds}

def clear():
    return None, None


description = """
"""

gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Image(label="Image to classify", type="pil"),
    ],
    clear = gr.Button("Clear"),
    outputs=gr.outputs.Label(),
    title="Image Classifier",
    description=description
    clear.click(lambda: None, None, inputs)
).launch()