Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+import xgboost as xgb
+
+# Sayfa ayarları
+st.set_page_config(page_title="BMW Model Predictor", layout="centered")
+st.title("🚗 BMW Model Predictor")
+
+st.markdown("This app estimates the BMW model based on price, mileage, fuel type, engine power and vehicle type information.")
+
+# Modeli yükle
+model = xgb.XGBClassifier()
+model.load_model("bmw_model.json")
+
+# Sınıf listelerini yükle
+fuel_classes = np.load("fuel_classes.npy", allow_pickle=True)
+car_type_classes = np.load("car_type_classes.npy", allow_pickle=True)
+target_classes = np.load("target_classes.npy", allow_pickle=True)
+
+# Kullanıcı girişleri
+price = st.number_input("💶 Price (EUR)", min_value=1000, max_value=200000, step=1000)
+mileage = st.number_input("🛣️ Mileage (km)", min_value=0, max_value=400000, step=1000)
+engine_power = st.number_input("⚙️ Engine Power (hp)", min_value=50, max_value=1000, step=10)
+
+fuel_type = st.selectbox("⛽ Fuel Type", fuel_classes)
+car_type = st.selectbox("🚘 Car Type", car_type_classes)
+
+# Tahmin butonu
+if st.button("Predict"):
+    try:
+        # Girdileri encode et
+        fuel_encoded = list(fuel_classes).index(fuel_type)
+        car_type_encoded = list(car_type_classes).index(car_type)
+
+        # Özellik vektörü oluştur
+        input_features = np.array([[engine_power, mileage, price, fuel_encoded, car_type_encoded]])
+
+        # Tahmin
+        prediction = model.predict(input_features)[0]
+        predicted_label = target_classes[prediction]
+
+        st.success(f"Predicted BMW model: **{predicted_label}**")
+    except Exception as e:
+        st.error(f"Something went wrong: {e}")