Delete inference.py

inference.py

@@ -1,64 +0,0 @@
-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)}