Update app.py

app.py

@@ -35,6 +35,15 @@ model_ft = torch.load(GASTRIC_MODEL_PATH, map_location=device)
 model_ft.to(device)
 model_ft.eval()
 
+# Anomaly detection function
+def is_anomaly(clf, feature_extractor, input_image):
+    feature_extractor.to(device)
+    with torch.no_grad():
+        image_features = feature_extractor(input_image)
+
+    is_outlier = clf.predict(image_features.cpu().numpy().reshape(1, -1))
+    return is_outlier[0] == -1
+
 # Anomaly detection and classification function
 def classify_image(uploaded_image):
     image = Image.open(uploaded_image).convert('RGB')