Update arbitration_service.py

Author: KOSASIH

quantum-ai-arbitration/src/main/arbitration/arbitration_service.py

@@ -1,16 +1,74 @@
 # src/main/arbitration/arbitration_service.py
 
+import logging
+from quantum.quantum_solver import QuantumSolver
+from ai.predictive_model import PredictiveModel
+from ai.fraud_detection import FraudDetection
+from arbitration.risk_assessment import RiskAssessment
+
 class ArbitrationService:
-    def __init__(self, quantum_solver, predictive_model):
+    def __init__(self, quantum_solver: QuantumSolver, predictive_model: PredictiveModel, 
+                 fraud_detection: FraudDetection, risk_assessment: RiskAssessment):
         self.quantum_solver = quantum_solver
         self.predictive_model = predictive_model
+        self.fraud_detection = fraud_detection
+        self.risk_assessment = risk_assessment
+        logging.info("ArbitrationService initialized.")
+
+    def process_arbitration(self, raw_data):
+        """
+        Process an arbitration case using quantum and AI components.
+        
+        Args:
+            raw_data (dict): The raw input data for arbitration processing.
+
+        Returns:
+            dict: The results of the arbitration processing.
+        """
+        logging.info("Starting arbitration processing...")
+
+        # Step 1: Preprocess the data
+        processed_data = self.preprocess_data(raw_data)
+
+        # Step 2: Assess risk
+        risk_result = self.risk_assessment.assess_risk(processed_data)
+        logging.info("Risk assessment result: %s", risk_result)
+
+        # Step 3: Detect fraud
+        fraud_result = self.fraud_detection.detect_fraud(processed_data)
+        logging.info("Fraud detection result: %s", fraud_result)
 
-    def process_arbitration(self, processed_data):
-        # Mock arbitration processing
-        print("Processing arbitration with processed data...")
-        optimization_result = self.quantum_solver.solve_optimization(processed_data["historical_data"])
-        prediction = self.predictive_model.predict_outcome(processed_data["historical_data"])
-        return {
+        # Step 4: Solve optimization problem using quantum solver
+        optimization_result = self.quantum_solver.solve_optimization(processed_data['historical_data'])
+        logging.info("Optimization result: %s", optimization_result)
+
+        # Step 5: Predict outcome using predictive model
+        prediction_result = self.predictive_model.predict_outcome(processed_data)
+        logging.info("Prediction result: %s", prediction_result)
+
+        # Compile results
+        results = {
+            "risk_assessment": risk_result,
+            "fraud_detection": fraud_result,
             "optimization": optimization_result,
-            "prediction": prediction
+            "prediction": prediction_result
         }
+
+        logging.info("Arbitration processing completed.")
+        return results
+
+    def preprocess_data(self, raw_data):
+        """
+        Preprocess the raw input data for arbitration processing.
+        
+        Args:
+            raw_data (dict): The raw input data.
+
+        Returns:
+            dict: The processed data ready for analysis.
+        """
+        # Example preprocessing logic
+        logging.info("Preprocessing raw data...")
+        # Convert raw data to DataFrame or any other necessary format
+        # Here we assume raw_data is already in a suitable format
+        return raw_data