# from fastapi import FastAPI, HTTPException
# from pydantic import BaseModel
# import joblib
# import numpy as np

# app = FastAPI()

# model      = joblib.load("RF_InFlight.joblib")
# le_airports = joblib.load("le_airports.joblib")

# class FlightInput(BaseModel):
#     Year: int
#     Quarter: int
#     Month: int
#     DayofMonth: int
#     Origin: str
#     Dest: str
#     DepTime: float
#     DepDelayMinutes: float
#     DepDel15: int
#     CRSDepTime: float
#     tempF: float
#     WindChillF: float
#     humidity: float
#     windspeedKmph: float
#     WindGustKmph: float
#     winddirDegree: float
#     weatherCode: float
#     visibility: float
#     pressure: float
#     cloudcover: float
#     DewPointF: float
#     time: int

# @app.post("/predict")
# def predict(data: FlightInput):
#     try:
#         origin_encoded = int(le_airports.transform([data.Origin])[0])
#     except ValueError:
#         raise HTTPException(
#             status_code=422,
#             detail=f"Origin '{data.Origin}' غير موجود. المطارات المتاحة: {le_airports.classes_.tolist()}"
#         )

#     try:
#         dest_encoded = int(le_airports.transform([data.Dest])[0])
#     except ValueError:
#         raise HTTPException(
#             status_code=422,
#             detail=f"Dest '{data.Dest}' غير موجود. المطارات المتاحة: {le_airports.classes_.tolist()}"
#         )

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

#     prediction = model.predict(features)[0]

#     return {
#         "predicted_delay_minutes": round(float(prediction), 2)
#     }

# @app.get("/airports")
# def get_airports():
#     airports = le_airports.classes_.tolist()
#     return {"airports": airports}

# @app.get("/health")
# def health():
#     return {"status": "ok"}


from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import joblib
import numpy as np

app = FastAPI()

# إعدادات الـ CORS للسماح للواجهات الأمامية بالاتصال بالـ API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # يسمح باستقبال الطلبات من أي مكان. يمكنك تغييرها لرابط موقعك لاحقاً للحماية.
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# تحميل الموديل ومشفر البيانات مرة واحدة عند تشغيل السيرفر
model      = joblib.load("GradientBoostingRegressor-WithoutPrecipMM.joblib")
le_airports = joblib.load("le_airports.joblib")

class FlightInput(BaseModel):
    Year: int
    Quarter: int
    Month: int
    DayofMonth: int
    Origin: str
    Dest: str
    DepTime: float
    DepDelayMinutes: float
    DepDel15: int
    CRSDepTime: float
    tempF: float
    WindChillF: float
    humidity: float
    windspeedKmph: float
    WindGustKmph: float
    winddirDegree: float
    weatherCode: float
    visibility: float
    pressure: float
    cloudcover: float
    DewPointF: float
    time: int

# مسار ترحيبي للـ Hugging Face Space
@app.get("/")
def read_root():
    return {
        "message": "Flight Delay Prediction API is running perfectly! ✈️",
        "health_check": "/health",
        "airports_list": "/airports",
        "prediction_endpoint": "/predict"
    }

@app.post("/predict")
def predict(data: FlightInput):
    try:
        origin_encoded = int(le_airports.transform([data.Origin])[0])
    except ValueError:
        raise HTTPException(
            status_code=422,
            detail=f"Origin '{data.Origin}' غير موجود. المطارات المتاحة: {le_airports.classes_.tolist()}"
        )

    try:
        dest_encoded = int(le_airports.transform([data.Dest])[0])
    except ValueError:
        raise HTTPException(
            status_code=422,
            detail=f"Dest '{data.Dest}' غير موجود. المطارات المتاحة: {le_airports.classes_.tolist()}"
        )

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

    prediction = model.predict(features)[0]

    return {
        "predicted_delay_minutes": round(float(prediction), 2)
    }

@app.get("/airports")
def get_airports():
    airports = le_airports.classes_.tolist()
    return {"airports": airports}

@app.get("/health")
def health():
    return {"status": "ok"}