app.py

import gradio as gr
from transformers import ViTForImageClassification
import torch
from PIL import Image
import torchvision.transforms as transforms

# Load the model
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
model.eval()

# Define the image preprocessing pipeline
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])

def predict_image(img):
    # Apply the transformations
    tensor_img = transform(img).unsqueeze(0)
    
    # Make prediction
    with torch.no_grad():
        outputs = model(tensor_img)
        predictions = outputs.logits.argmax(-1)
    
    return model.config.id2label[predictions.item()]

# Create the interface
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(shape=(224, 224)),
    outputs="text",
    live=True,
    capture_session=True,
    title="Image recognition",
    description="Upload an image you want to categorize.",
    theme="Monochrome"
)

iface.launch()