app.py

import gradio as gr
from transformers import pipeline
from PIL import Image

# Load pretrained image classification pipeline
classifier = pipeline(
    "image-classification",
    model="google/vit-base-patch16-224"
)

def classify_image(image):
    results = classifier(image)
    return {r["label"]: float(r["score"]) for r in results}

# Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil", label="Upload an animal image"),
    outputs=gr.Label(num_top_classes=5),
    title="Animal Image Classifier",
    description="Upload an image of an animal and see the predicted class.",
    examples=[
        "animal_images/cat.jpg",
        "animal_images/dog.jpg",
        "animal_images/bird.jpg"
    ]
)

demo.launch()