app.py

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
import gradio as gr

# Load the model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")

# Define prediction function
def classify_image(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    predicted_class = outputs.logits.argmax(-1).item()
    return model.config.id2label[predicted_class]

# Create a Gradio app
app = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    outputs="text",
    title="Image Classifier"
)

app.launch()