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64999ea fa307e3 64999ea fa307e3 64999ea fa307e3 64999ea c2c75a9 64999ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import pandas as pd
import joblib
import json
import numpy as np
import os
import streamlit as st
def predict():
#Load model dan metadata
@st.cache_resource
def load_model():
"""try:
with open('src/health_risk_model.pkl', "rb") as f1:
model = cloudpickle.load(f1)
with open('src/model_metadata.json', "r") as f:
metadata = json.load(f)
return model, metadata
except Exception as e:
st.error(f"Error loading model: {str(e)}")
return None, None"""
try:
model_path = os.path.join(os.path.dirname(__file__), "..", "src/health_risk_model.pkl")
with open(model_path, "rb") as f:
model = joblib.load(f)
with open('src/model_metadata.json', "r") as f:
metadata = json.load(f)
return model, metadata
except Exception as e:
st.error(f"Error loading model: {str(e)}")
return None, None
model, metadata = load_model()
# Fungsi prediksi
def predict_health_risk(input_data: dict) -> dict:
try:
if model is None or metadata is None:
raise ValueError("Model not loaded")
# Konversi ke DataFrame
input_df = pd.DataFrame([input_data])
# Validasi fitur
required_features = metadata['feature_names']
missing_features = [f for f in required_features if f not in input_df.columns]
if missing_features:
raise ValueError(f"Missing features: {', '.join(missing_features)}")
# Pastikan urutan kolom
input_df = input_df[required_features]
# Prediksi
prediction = model.predict(input_df)[0]
probabilities = model.predict_proba(input_df)[0]
# Konversi tipe data
if isinstance(prediction, np.bool_):
prediction = int(prediction)
elif isinstance(prediction, bool):
prediction = 1 if prediction else 0
# Mapping hasil
# prediction_label = metadata['target_mapping'].get(str(prediction), "unknown")
if probabilities[1] > 0.2:
prediction_label = "IYA"
else :
prediction_label = "TIDAK"
return {
'prediction': prediction_label,
'prob_ya': float(probabilities[1]),
'prob_tidak': float(probabilities[0]),
'success': True
}
except Exception as e:
return {
'error': str(e),
'success': False
}
# UI Streamlit
st.title('Prediksi Risiko Kesehatan 🩺')
st.markdown("""
Aplikasi ini memprediksi risiko masalah kesehatan berdasarkan profil Anda.
Silakan isi form di bawah ini:
""")
with st.form("prediction_form"):
col1, col2 = st.columns(2)
with col1:
st.subheader("Data Demografis")
age = st.slider("Usia", 18, 100, 40)
gender = st.selectbox("Jenis Kelamin", ["Male", "Female"])
annual_income = st.number_input("Pendapatan Tahunan (USD)", 0, 500000, 50000)
with col2:
st.subheader("Gaya Hidup")
smokes_per_day = st.slider("Rokok per Hari", 0, 40, 0)
drinks_per_week = st.slider("Minuman Alkohol per Minggu", 0, 50, 0)
mental_health = st.selectbox("Status Kesehatan Mental",
["Stable", "Unstable", "Critical"])
social_support = st.selectbox("Dukungan Sosial",
["Weak", "Moderate", "Strong"])
submitted = st.form_submit_button("Prediksi Risiko Kesehatan")
if submitted:
input_data = {
'age': age,
'gender': gender,
'annual_income_usd': annual_income,
'smokes_per_day': smokes_per_day,
'drinks_per_week': drinks_per_week,
'mental_health_status': mental_health,
'social_support': social_support
}
with st.spinner('Menganalisis data...'):
result = predict_health_risk(input_data)
if result.get('success', False):
st.success("Prediksi Berhasil!")
# Tampilkan hasil
st.subheader("Hasil Prediksi")
# Progress bar untuk probabilitas
prob_ya = result['prob_ya']
col_res1, col_res2 = st.columns(2)
with col_res1:
st.metric("Status Risiko Kesehatan",
result['prediction'].upper(),
"YA" if result['prediction'] == 'ya' else "TIDAK")
with col_res2:
st.metric("Probabilitas Risiko YA",
f"{prob_ya:.2%}")
# Visualisasi probabilitas
st.progress(prob_ya, text="Tingkat Risiko Kesehatan")
# Detail input
st.divider()
st.subheader("Detail Input Anda")
input_df = pd.DataFrame([input_data])
st.dataframe(input_df.T.rename(columns={0: 'Nilai'}), hide_index=True)
else:
st.error(f"Prediksi gagal: {result.get('error', 'Unknown error')}")
# Footer
st.divider()
st.caption("© 2025 Health Risk Prediction App - Powered by Hugging Face Spaces")
if __name__ == "main":
predict() |