import joblib
import numpy as np

# تحميل الموديل مرة واحدة
def load_model():
    model = joblib.load("RandomForestRegressor.joblib")
    le_airports = joblib.load("le_airports.joblib")
    return model, le_airports

model, le_airports = load_model()


def predict(inputs):
    try:
        data = inputs["data"]

        # تنظيف البيانات
        origin = data["Origin"][0].upper().strip()
        dest = data["Dest"][0].upper().strip()

        # Encoding
        origin_encoded = le_airports.transform([origin])[0]
        dest_encoded = le_airports.transform([dest])[0]

        # تجهيز الفيتشرز
        features = np.array([[
            data["Year"][0],
            data["Quarter"][0],
            data["Month"][0],
            data["DayofMonth"][0],
            origin_encoded,
            dest_encoded,
            data["CRSDepTime"][0],
            data["DepTime"][0],
            data["DepDelayMinutes"][0],
            data["DepDel15"][0],
            data["time"][0],
            data["tempF"][0],
            data["WindChillF"][0],
            data["humidity"][0],
            data["windspeedKmph"][0],
            data["WindGustKmph"][0],
            data["winddirDegree"][0],
            data["weatherCode"][0],
            data["precipMM"][0],
            data["visibility"][0],
            data["pressure"][0],
            data["cloudcover"][0],
            data["DewPointF"][0]
        ]])

        # التنبؤ
        pred = float(model.predict(features)[0])
        pred = max(0.0, round(pred, 1))

        status = "Delayed" if pred >= 15 else "On Time"

        return {
            "predicted_delay_minutes": pred,
            "status": status
        }

    except Exception as e:
        return {"error": str(e)}