predict.py

import pandas as pd
import joblib

# Load model and encoders
model = joblib.load("model/model.pkl")
encoders = joblib.load("model/encoders.pkl")

def predict_transaction(data_dict):
    # Convert dict to dataframe
    df = pd.DataFrame([data_dict])

    # Process time
    df["hour"] = pd.to_datetime(df["time"], format="%H:%M").dt.hour
    df.drop(columns=["check_id", "time"], inplace=True)

    # Encode categorical features
    for col in ["employee_id", "terminal_id"]:
        df[col] = encoders[col].transform(df[col])

    # Predict
    prediction = model.predict(df)[0]
    return "Suspicious" if prediction == 1 else "Not Suspicious"

# Example usage
if __name__ == "__main__":
    sample = {
        "check_id": 1005,
        "employee_id": "E101",
        "total": 100,
        "discount_amount": 90,
        "item_count": 1,
        "time": "12:10",
        "terminal_id": "T1"
    }

    result = predict_transaction(sample)
    print("Prediction:", result)