app_v4.py

import gradio as gr
from transformers import pipeline

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

def predict(image):
    if image is None:
        return None
    predictions = classifier(image)
    return {p["label"]: p["score"] for p in predictions}

# Updated examples list to use cat.jpeg instead of cat.png
examples = [
    "animal_images/hippo.png",
    "animal_images/jaguar.png",
    "animal_images/toucan.png",
    "animal_images/sloth.png",
    "animal_images/cat.jpeg",  # Changed to jpeg
    "animal_images/frog.png",
    "animal_images/turtle.png"
]

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(
        type="pil", 
        label="Input Image", 
        interactive=True,
        height=None
    ),
    outputs=gr.Label(num_top_classes=3, label="Predictions"),
    examples=examples,
    title="Animal Classifier",
    description="Upload an image of an animal."
)

if __name__ == "__main__":
    demo.launch()