src/pipeline/predict_pipeline.py

"""
Prediction Pipeline

Loads trained models and scaler, applies preprocessing,
and returns final prediction using an ensemble threshold.
"""

import joblib
import os
import pandas as pd

class PredictPipeline:

    def __init__(self):
        BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
        ARTIFACTS_PATH = os.path.join(BASE_DIR, "artifacts")

        self.rf_model = joblib.load(os.path.join(ARTIFACTS_PATH, "rf_model.pkl"))
        self.xgb_model = joblib.load(os.path.join(ARTIFACTS_PATH, "xgb_model.pkl"))
        self.scaler = joblib.load(os.path.join(ARTIFACTS_PATH, "scaler.pkl"))
    
    def preprocess(self, data: pd.DataFrame):
        """Apply same preprocessing as training"""
        data = data.copy()
        
        # 🔥 THE FIX: Add .flatten() to strip away the array brackets
        # This turns [[value]] into a raw, safe float for SHAP
        data["Amount"] = self.scaler.transform(data[["Amount"]]).flatten()
        
        return data

    def predict(self, data: pd.DataFrame):
        """Make prediction using ensemble logic"""
        # Preprocess the data first
        data = self.preprocess(data)

        # Get fraud probabilities from both models
        rf_prob = self.rf_model.predict_proba(data)[:, 1]
        xgb_prob = self.xgb_model.predict_proba(data)[:, 1]

        # Ensemble: Average the probabilities
        final_prob = (rf_prob + xgb_prob) / 2

        # 🔥 FIX: Lowered Decision Threshold from 0.5 down to 0.15
        final_pred = (final_prob > 0.15).astype(int)

        return final_pred[0], final_prob[0]