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app.py

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+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()