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)
    preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
    return [pred["label"] for pred in preds]  # Return a list of labels only

description = """
"""

gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Image(label="Image to classify", type="pil"),
    ],
    outputs=gr.outputs.Label(),  # Use Label output instead of JSON
    title="Image Classifier",
    description=description
).launch()