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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()
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