Upload the app.py

app.py

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+import torch
+import clip
+from PIL import Image
+import gradio as gr
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+# Load normal class image embeddings
+# For real use, you should create multiple embeddings and average them
+normal_image = preprocess(Image.open("normal_sample.jpg")).unsqueeze(0).to(device)
+with torch.no_grad():
+    normal_embedding = model.encode_image(normal_image)
+    normal_embedding /= normal_embedding.norm()
+
+def detect_anomaly(img):
+    img = preprocess(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        test_embedding = model.encode_image(img)
+        test_embedding /= test_embedding.norm()
+        similarity = (test_embedding @ normal_embedding.T).item()
+    
+    if similarity < 0.8:  # example threshold
+        result = "Anomaly Detected"
+    else:
+        result = "Normal"
+    return f"Similarity: {similarity:.2f} | {result}"
+
+gr.Interface(fn=detect_anomaly,
+             inputs=gr.Image(),
+             outputs="text").launch()