import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load the model + processor
model_id = "facebook/deit-base-patch16-224"
model = AutoModelForImageClassification.from_pretrained(model_id)
processor = AutoImageProcessor.from_pretrained(model_id)

# Define prediction function
def classify_image(image):
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    label = model.config.id2label[predicted_class_idx]
    return label

# Create Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Animal Species Classifier",
    description="Using facebook/deit-base-patch16-224"
)

demo.launch()