Create SHAP.py

SHAP.py

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+# Example: Add SHAP explanation (requires SHAP library)
+import shap
+
+class FraudDetection:
+    def __init__(self):
+        self.explainer = shap.TreeExplainer(model)  # Replace with your model
+
+    def predict(self, transaction_amount, time_of_day, role, keystroke_speed, error_rate):
+        # Your existing prediction logic
+        fraud_prob = 0.1  # Replace with your model's prediction
+        explanation = "No fraud detected."  # Replace with your model's explanation
+        G = nx.Graph()
+        G.add_node("Transaction")
+        G.add_node("User")
+        G.add_edge("Transaction", "User")
+        image = self.plot_graph(G)
+        # Generate SHAP explanation
+        shap_values = self.explainer.shap_values(X)
+        explanation = "SHAP values: " + str(shap_values)
+        return fraud_prob, image, explanation