app.py

from transformers import pipeline
import gradio as gr

# Load model ONCE at startup
pipe = pipeline("image-classification", model="skshmjn/Pokemon-classifier-gen9-1025")

def classify_pokemon(img):
    predictions = pipe(img)
    # Return top 3 as list of (label, confidence)
    return {pred["label"]: pred["score"] for pred in predictions[:3]}

# Gradio interface
gr.Interface(
    fn=classify_pokemon,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Pokémon Classifier",
    description="Upload an image of a Pokémon to identify it!"
).launch()