app.py

import streamlit as st
import os
from pyspark.sql import SparkSession
from pyspark.ml.classification import LogisticRegressionModel
from pyspark.ml.feature import VectorAssembler

# --- 1. SAYFA AYARLARI ---
st.set_page_config(page_title="Heart Predictor", page_icon="🫀", layout="wide")

@st.cache_resource
def initialize_app():
    spark = SparkSession.builder \
        .appName("HeartApp") \
        .master("local[*]") \
        .config("spark.driver.bindAddress", "127.0.0.1") \
        .getOrCreate()
    model = LogisticRegressionModel.load("heart_model")
    return spark, model

try:
    spark, model = initialize_app()
except Exception as e:
    st.error(f"Error / Hata: {e}")
    st.stop()

# --- 2. BAŞLIKLAR ---
st.title("🫀 Heart Disease Risk Prediction")
st.subheader("Kalp Hastalığı Risk Tahmini")

st.markdown("---")

# --- 3. HIZLI TEST BUTONLARI ---
st.markdown("### 🧪 Quick Test Scenarios / Hızlı Test Senaryoları")
col_b1, col_b2 = st.columns(2)

if 'preset' not in st.session_state:
    st.session_state.preset = "default"

if col_b1.button("🟢 Load Low Risk (Healthy) / Düşük Risk (Sağlıklı)"):
    st.session_state.preset = "low"
if col_b2.button("🔴 Load High Risk (Patient) / Yüksek Risk (Hasta)"):
    st.session_state.preset = "high"

# Senaryo değerlerini tanımlayalım
if st.session_state.preset == "low":
    d = [25, 0, 1, 110, 180, 0, 0, 180, 0, 0.0, 2, 0, 1]
elif st.session_state.preset == "high":
    d = [65, 1, 0, 160, 290, 1, 2, 105, 1, 3.2, 1, 3, 3]
else:
    d = [50, 1, 1, 120, 210, 0, 1, 145, 0, 1.0, 1, 0, 2]

st.markdown("---")

# --- 4. GİRİŞ ALANLARI (SIDEBAR) ---
st.sidebar.header("📋 Input Data / Veri Girişi")

age = st.sidebar.slider("Age / Yaş", 1, 100, d[0])
sex = st.sidebar.radio("Sex / Cinsiyet", [1, 0], index=(0 if d[1]==1 else 1), format_func=lambda x: "Male/Erkek (1)" if x == 1 else "Female/Kadın (0)")
cp = st.sidebar.selectbox("Chest Pain / Göğüs Ağrısı (0-3)", [0, 1, 2, 3], index=d[2])
trestbps = st.sidebar.number_input("Blood Pressure / Tansiyon", 50, 250, d[3])
chol = st.sidebar.number_input("Cholesterol / Kolesterol", 100, 600, d[4])
fbs = st.sidebar.selectbox("Fasting Sugar / Şeker > 120", [0, 1], index=d[5], format_func=lambda x: "Yes/Evet (1)" if x == 1 else "No/Hayır (0)")
restecg = st.sidebar.selectbox("Resting ECG / EKG (0-2)", [0, 1, 2], index=d[6])
thalach = st.sidebar.slider("Max Heart Rate / Maks. Kalp Hızı", 50, 220, d[7])
exang = st.sidebar.radio("Exercise Angina / Egzersiz Ağrısı", [0, 1], index=d[8], format_func=lambda x: "Yes/Evet (1)" if x == 1 else "No/Hayır (0)")
oldpeak = st.sidebar.number_input("Oldpeak", 0.0, 10.0, d[9], step=0.1)
slope = st.sidebar.selectbox("Slope (0-2)", [0, 1, 2], index=d[10])
ca = st.sidebar.selectbox("Major Vessels / Damar Sayısı (0-4)", [0, 1, 2, 3, 4], index=d[11])
thal = st.sidebar.selectbox("Thalassemia / Thal (0-3)", [0, 1, 2, 3], index=d[12])

# --- 5. TAHMİN BÖLÜMÜ (MANTIK TERSİNE ÇEVRİLDİ) ---
if st.button("🚀 RUN ANALYSIS / ANALİZİ ÇALIŞTIR"):
    input_data = spark.createDataFrame([{
        'age': age, 'sex': sex, 'cp': cp, 'trestbps': trestbps, 'chol': chol,
        'fbs': fbs, 'restecg': restecg, 'thalach': thalach, 'exang': exang,
        'oldpeak': oldpeak, 'slope': slope, 'ca': ca, 'thal': thal
    }])
    
    feature_cols = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
    assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
    final_df = assembler.transform(input_data)
    
    prediction = model.transform(final_df)
    res = prediction.select("prediction").collect()[0][0]
    prob = prediction.select("probability").collect()[0][0]

    st.markdown("### 📊 Result / Sonuç:")
    
    # MANTIK DÜZELTİLDİ: 
    # Eğer senin modelinde 0=Risk, 1=Sağlıklı ise aşağısı doğru çalışacaktır.
    if res == 0.0: 
        st.error(f"🚨 **HIGH RISK / YÜKSEK RİSK** (Prob: %{round(prob[0]*100, 2)})")
        st.write("The model indicates potential risk. / Modele göre riskli durum tespit edildi.")
    else:
        st.success(f"💚 **LOW RISK / DÜŞÜK RİSK** (Prob: %{round(prob[1]*100, 2)})")
        st.write("The model indicates a healthy profile. / Modele göre risk düşük görünmektedir.")

st.divider()
st.caption("Disclaimer: For educational purposes only. / Tıbbi tavsiye niteliği taşımaz.")