Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+
+processor = AutoImageProcessor.from_pretrained("anasmkh/customied_vit")
+model = AutoModelForImageClassification.from_pretrained("anasmkh/customied_vit")
+
+
+def classify_image(image):
+    
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+  
+    logits = outputs.logits
+    probs = torch.softmax(logits, dim=1)[0]
+    best_idx = torch.argmax(probs).item()
+    label = model.config.id2label[best_idx]
+    score = float(probs[best_idx])
+
+    return {label: score}
+
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Custom Vision Transformer Classifier",
+    description="Upload an image to get classification results from the custom ViT model."
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)