app.py

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

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

def classify_image(image):
    # Preprocess the image
    inputs = feature_extractor(images=image, return_tensors="pt")
    
    # Make prediction
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get the predicted class
    predicted_class_idx = outputs.logits.argmax(-1).item()
    predicted_class = model.config.id2label[predicted_class_idx]
    
    return predicted_class

# Create Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Textbox(label="Predicted Class"),
    title="Image Classification",
    description="Upload an image to classify it using a pre-trained ViT model."
)

# Launch the app
iface.launch()