main

import.py

@@ -0,0 +1,92 @@
+import os
+import requests
+import pandas as pd
+import psycopg2
+from io import StringIO
+from datetime import datetime
+
+DATABASE_URL = os.getenv("DATABASE_URL")
+
+# EPA resmi dataset URL
+CSV_URL = "https://www.fueleconomy.gov/feg/epadata/vehicles.csv"
+
+print("CSV indiriliyor...")
+response = requests.get(CSV_URL)
+response.raise_for_status()
+
+print("CSV memory'e alınıyor...")
+df = pd.read_csv(StringIO(response.text), low_memory=False)
+
+print("Filtreleme başlıyor...")
+
+# 2000 sonrası
+df = df[df["year"] >= 2000]
+
+df = df[[
+    "make",
+    "model",
+    "year",
+    "fuelType1",
+    "trany",
+    "cylinders",
+    "comb08"
+]]
+
+df = df.dropna(subset=["make", "model", "year"])
+df = df.drop_duplicates(subset=["make", "model", "year", "fuelType1", "trany"])
+
+# MPG -> L/100km
+df["avgConsumption"] = df["comb08"].apply(
+    lambda mpg: 235.214 / mpg if mpg and mpg > 0 else None
+)
+
+df["createdAt"] = datetime.utcnow()
+
+df = df.rename(columns={
+    "make": "brand",
+    "fuelType1": "fuelType",
+    "trany": "transmission",
+    "comb08": "combinationMpg"
+})
+
+df = df[[
+    "brand",
+    "model",
+    "year",
+    "fuelType",
+    "transmission",
+    "cylinders",
+    "combinationMpg",
+    "avgConsumption",
+    "createdAt"
+]]
+
+print("DB bağlantısı kuruluyor...")
+conn = psycopg2.connect(DATABASE_URL)
+cur = conn.cursor()
+
+print("Eski veriler temizleniyor...")
+cur.execute('TRUNCATE TABLE "CarLibrary";')
+conn.commit()
+
+print("COPY başlıyor...")
+
+buffer = StringIO()
+df.to_csv(buffer, index=False, header=False)
+buffer.seek(0)
+
+cur.copy_expert(
+    """
+    COPY "CarLibrary"
+    (brand, model, year, fuelType, transmission, cylinders,
+     combinationMpg, avgConsumption, createdAt)
+    FROM STDIN WITH CSV
+    """,
+    buffer
+)
+
+conn.commit()
+cur.close()
+conn.close()
+
+print("🚀 IMPORT TAMAMLANDI.")