import joblib
import numpy as np
from sklearn.preprocessing import StandardScaler

# Load the model and scaler
model = joblib.load("classification_model.joblib")
scaler = joblib.load("scaler.pkl")

def predict(features):
    # Scale the features
    scaled_features = scaler.transform(np.array(features).reshape(1, -1))
    prediction = model.predict(scaled_features)
    return prediction[0]

# Sample usage
if __name__ == "__main__":
    # Sample feature data (replace with real data when calling)
    sample_data = [0.5, 1.2, -0.3, 2.0]
    result = predict(sample_data)
    print(f"Prediction: {result}")