app.py

from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch
import torch.nn.functional as F
import time

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(device)

def predict(image):
    inputs = feature_extractor(images=image, return_tensors="pt").to(device)
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_prob = F.softmax(logits, dim=-1).detach().cpu().numpy().max()
    predicted_class_idx = logits.argmax(-1).item()
    label = model.config.id2label[predicted_class_idx].split(",")[0]
    time.sleep(2)
    return {label: float(predicted_class_prob)}
 
import gradio as gr

gr.Interface(predict, gr.Image(type="pil"), "label").queue(concurrency_count=1).launch()