Create app.py

app.py

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+from transformers import ViTImageProcessor, 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')
+ 
+processor = ViTImageProcessor.from_pretrained("LCZs_v2",local_files_only=True)
+model = ViTForImageClassification.from_pretrained("LCZs_v2",local_files_only=True).to(device)
+ 
+def predict(image):
+    inputs = processor(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").launch()