Add the code to app file

app.py

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+import torch
+from transformers import AutoModel, AutoImageProcessor
+from PIL import Image
+import gradio as gr
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load DINOv2 model
+model_name = "facebook/dinov2-base"
+model = AutoModel.from_pretrained(model_name).to(device)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Load OK reference image (built-in normal)
+ok_image = Image.open("https://huggingface.co/datasets/Soooma/sofc/OK1.jpg")
+
+# Precompute OK feature embedding
+with torch.no_grad():
+    ok_input = processor(images=ok_image, return_tensors="pt").to(device)
+    ok_feat = model(**ok_input).last_hidden_state.mean(dim=1)
+    ok_feat = ok_feat.cpu().numpy()
+
+def detect_anomaly(image):
+    if image is None:
+        return "No image uploaded."
+
+    with torch.no_grad():
+        inputs = processor(images=image, return_tensors="pt").to(device)
+        feat = model(**inputs).last_hidden_state.mean(dim=1)
+        feat = feat.cpu().numpy()
+
+    similarity = cosine_similarity(feat, ok_feat)[0][0]
+
+    if similarity < 0.90:
+        return f"Anomaly Detected | Similarity: {similarity:.3f}"
+    else:
+        return f"Normal | Similarity: {similarity:.3f}"
+
+gr.Interface(
+    fn=detect_anomaly,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Anomaly Detector (DINOv2)",
+    description="Upload an image of a stack. The model compares it to a known OK sample using DINOv2 features."
+).launch()