app.py

import gradio as gr
from transformers import pipeline

# Load model once at startup (downloaded and cached on first run)
pipe = pipeline(
    "image-classification",
    model="therealcyberlord/stanford-car-vit-patch16",
    top_k=5,
)


def classify_car(image):
    if image is None:
        return {}
    results = pipe(image)
    return {r["label"]: float(r["score"]) for r in results}


demo = gr.Interface(
    fn=classify_car,
    inputs=gr.Image(type="pil", label="Upload a car photo"),
    outputs=gr.Label(num_top_classes=5, label="Top 5 Predictions"),
    title="🚗 Car Model Classifier",
    description=(
        "Upload a photo of a car to identify its **make, model, and year**.\n\n"
        "Powered by a **Vision Transformer (ViT)** fine-tuned on the "
        "[Stanford Cars dataset](https://ai.stanford.edu/~jkrause/cars/car_dataset.html) "
        "— 196 categories covering car make, model, and year (2002–2012)."
    ),
    theme=gr.themes.Soft(),
)

demo.launch()