Create fraud_detection_pipeline.py

fraud_detection_pipeline.py

@@ -0,0 +1,58 @@
+from transformers import Pipeline
+import numpy as np
+import joblib
+from typing import Dict, List, Union
+
+class FraudDetectionPipeline(Pipeline):
+    def __init__(self):
+        super().__init__()
+        # Load the model and scaler
+        self.model = joblib.load("random_forest_model.joblib")
+        self.scaler = joblib.load("rf_scaler.joblib")
+    
+    def preprocess(self, features: Dict[str, Union[int, float]]) -> np.ndarray:
+        """
+        Preprocess the input features
+        Expected features:
+        - account_age: int (months)
+        - cred_changes_freq: float (per year)
+        - return_order_ratio: float
+        - vpn_usage: int (0 or 1)
+        - credit_score: int
+        """
+        # Convert input to correct format
+        input_data = np.array([[
+            features['account_age'],
+            features['cred_changes_freq'],
+            features['return_order_ratio'],
+            features['vpn_usage'],
+            features['credit_score']
+        ]])
+        
+        # Scale the features
+        scaled_input = self.scaler.transform(input_data)
+        return scaled_input
+    
+    def _forward(self, features: Dict[str, Union[int, float]]) -> Dict[str, Union[str, float]]:
+        """
+        Make prediction using the model
+        """
+        # Preprocess
+        scaled_input = self.preprocess(features)
+        
+        # Get prediction and probability
+        prediction = self.model.predict(scaled_input)[0]
+        probabilities = self.model.predict_proba(scaled_input)[0]
+        
+        # Return prediction and confidence
+        return {
+            "prediction": "Fraud" if prediction == 1 else "Not Fraud",
+            "confidence": float(probabilities[prediction]),
+            "fraud_probability": float(probabilities[1])
+        }
+    
+    def postprocess(self, model_outputs):
+        return model_outputs
+
+def load_pipeline():
+    return FraudDetectionPipeline()