app.py

import streamlit as st
import pandas as pd
import joblib

st.set_page_config(page_title="Insurance Predictor", layout="centered")

@st.cache_resource
def load_assets():
    model = joblib.load("lgbm_model.pkl")
    columns = joblib.load("model_columns.pkl")
    return model, columns

model, model_columns = load_assets()

st.title("🏥 Sağlık Sigortası Fiyat Tahmini")

# Kullanıcı Girişleri
col1, col2 = st.columns(2)
with col1:
    age = st.number_input("Yaş", 18, 100, 30)
    bmi = st.number_input("BMI", 10.0, 60.0, 25.0)
    children = st.number_input("Çocuk Sayısı", 0, 10, 0)
with col2:
    sex = st.selectbox("Cinsiyet", ["male", "female"])
    smoker = st.selectbox("Sigara", ["yes", "no"])
    region = st.selectbox("Bölge", ["southeast", "southwest", "northwest", "northeast"])

if st.button("Tahmin Et"):
    # 1. Temel DataFrame
    input_df = pd.DataFrame([[age, bmi, children]], columns=['age', 'bmi', 'children'])
    input_df['sex'] = 1 if sex == "male" else 0
    input_df['smoker'] = 1 if smoker == "yes" else 0
    
    # 2. Arka Planda Feature Engineering (Notebook'undaki Mantık)
    # BMI_CAT
    bmi_cat = "ideal"
    if bmi < 18.5: bmi_cat = "underweight"
    elif 25 <= bmi < 30: bmi_cat = "overweight"
    elif bmi >= 30: bmi_cat = "obese"
    
    # AGE_CAT
    age_cat = "young"
    if 35 < age <= 55: age_cat = "middle"
    elif age > 55: age_cat = "old"

    # Smoker_Obese Etkileşimi
    input_df['is_smoker_obese'] = 1 if (smoker == "yes" and bmi >= 30) else 0

    # 3. One-Hot Encoding Simülasyonu
    for col in model_columns:
        if col not in input_df.columns:
            # Region, BMI_CAT ve AGE_CAT sütunlarını kontrol et
            if f"region_{region}" == col or f"BMI_CAT_{bmi_cat}" == col or f"AGE_CAT_{age_cat}" == col:
                input_df[col] = 1
            else:
                input_df[col] = 0

    # Sütunları modelin beklediği sıraya diz
    input_df = input_df[model_columns]
    
    res = model.predict(input_df)[0]
    st.success(f"Tahmini Yıllık Ücret: ${res:,.2f}")