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import streamlit as st
import pandas as pd
import joblib
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# Function to load data
def load_data():
df = pd.read_csv('h8dsft_P1G3_Muhammad_Arief_Kurniawan.csv')
return df
# Function to load model and preprocessing components
def load_components():
rf_model = joblib.load('rf_model.pkl')
gb_model = joblib.load('gb_model.pkl')
stacked_model = joblib.load('stacked_es_model.pkl')
scaler = joblib.load('model_scaler.pkl')
numeric_columns = ['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time']
categorical_columns = ['anaemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking']
trained_features = rf_model.feature_names_in_
return rf_model, gb_model, stacked_model, scaler, numeric_columns, categorical_columns, trained_features
# Function to get user input
def get_user_input(numeric_columns, categorical_columns):
inputs = {}
for col in numeric_columns + categorical_columns:
if col in categorical_columns:
inputs[col] = st.selectbox(col, [0, 1])
else:
default_val = np.expm1(50.0) if col != 'time' else 50.0
inputs[col] = np.log1p(st.number_input(col, value=default_val))
user_data = pd.DataFrame([inputs])
user_data = user_data[numeric_columns + categorical_columns]
return user_data
def main():
st.title("Prediksi dan Eksplorasi Gagal Jantung")
# Sidebar for navigation
choice = st.sidebar.selectbox("Pilih Halaman", ["Beranda", "Eksplorasi Data", "Prediksi"])
if choice == "Beranda":
st.write("Selamat datang di aplikasi prediksi dan eksplorasi gagal jantung.")
st.write("Silakan pilih halaman di sidebar untuk melanjutkan.")
elif choice == "Eksplorasi Data":
data = load_data()
st.write("### Informasi Dasar Data")
st.write(data.info())
st.write("### Deskripsi Statistik")
st.write(data.describe())
st.write("### 5 Data Pertama")
st.write(data.head())
st.write("### Visualisasi Distribusi Target: DEATH_EVENT")
fig, ax = plt.subplots(figsize=(10, 6))
sns.countplot(x=data['DEATH_EVENT'], ax=ax)
st.pyplot(fig)
elif choice == "Prediksi":
rf_model, gb_model, stacked_model, scaler, numeric_columns, categorical_columns, trained_features = load_components()
user_input = get_user_input(numeric_columns, categorical_columns)
user_input_ordered = user_input[trained_features].to_numpy()
user_input_ordered[:, :len(numeric_columns)] = scaler.transform(user_input_ordered[:, :len(numeric_columns)])
# Predictions
rf_prediction = rf_model.predict(user_input_ordered)
gb_prediction = gb_model.predict(user_input_ordered)
stacked_prediction = stacked_model.predict(user_input_ordered)
st.write(f"Random Forest Prediction: {'Gagal Jantung' if rf_prediction[0] == 1 else 'Tidak Ada Gagal Jantung'}")
st.write(f"Gradient Boosting Prediction: {'Gagal Jantung' if gb_prediction[0] == 1 else 'Tidak Ada Gagal Jantung'}")
st.write(f"Stacked Model Prediction: {'Gagal Jantung' if stacked_prediction[0] == 1 else 'Tidak Ada Gagal Jantung'}")
st.subheader("Data Input Pengguna")
st.write(user_input)
if __name__ == "__main__":
main()
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