app.py

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

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

# Define the prediction function
def classify_image(image):
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    label = model.config.id2label[predicted_class_idx]
    return f"Predicted Class: {label}"

# Create Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Image Classification App",
    description="Upload an image to classify it using the Vision Transformer model.",
)

# Launch the app
if __name__ == "__main__":
    interface.launch()