Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,106 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+# import joblib
+import pickle
+
+# scaler = joblib.load('scaler.pkl')
+with open('scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+with open('credit_default.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+st.title('Prediksi Default Loan Customer')
+
+Name  = st.text_input('Name', placeholder='Input Your Name..')
+
+# Streamlit input widgets
+GENDER = st.radio("Jenis Kelamin", ["Laki-laki", "Perempuan"])
+AGE = st.slider('Umur (Tahun)', 0, 130, 20)
+Type_Occupation = st.selectbox(
+    "Jenis Pekerjaan",
+    ("High skill tech staff", 'Core staff', 'Sales staff', 'Laborers',
+     'Cooking staff', 'Managers', 'Accountants', 'Cleaning staff', 'Drivers',
+     'Private service staff', 'Low-skill Laborers', 'IT staff',
+     'Waiters/barmen staff', 'Medicine staff', 'Security staff', 'HR staff',
+     'Secretaries', 'Realty agents'),
+    placeholder="Pilih Pekerjaanmu...",
+)
+Marital_status = st.selectbox(
+    "Status Pernikahan",
+    ('Married', 'Single / not married', 'Civil marriage', 'Separated', 'Widow'),
+    placeholder="Pilih Jenis Pendapatanmu...",
+)
+Family_Members = st.slider('Jumlah Anggota Keluarga', 0, 20, 2)
+Type_Income = st.selectbox(
+    "Jenis Pendapatan",
+    ('Commercial associate', 'Pensioner', 'Working', 'State servant'),
+    placeholder="Pilih Jenis Pendapatanmu...",
+)
+YEAR_EMPLOYED = st.slider('Lama Bekerja (Tahun)', 0, 60, 5)
+EDUCATION = st.selectbox(
+    "Pendidikan",
+    ('Higher education', 'Secondary / secondary special', 'Lower secondary',
+     'Incomplete higher', 'Academic degree'),
+    placeholder="Pilih Pendidikan Terakhirmu...",
+)
+Housing_type = st.selectbox(
+    "Tipe Rumah",
+    ('House / apartment', 'With parents', 'Rented apartment',
+     'Municipal apartment', 'Co-op apartment', 'Office apartment'),
+    placeholder="Pilih Tipe Rumahmu...",
+)
+
+# Mapping dictionaries
+Housing_type_map = {
+    'House / apartment': 0, 'Rented apartment': 1, 'With parents': 2,
+    'Municipal apartment': 3, 'Co-op apartment': 4, 'Office apartment': 5
+}
+EDUCATION_map = {
+    'Higher education': 0, 'Secondary / secondary special': 1, 'Lower secondary': 2,
+    'Incomplete higher': 3, 'Academic degree': 4
+}
+Type_Occupation_map = {
+    'Private service staff': 0, 'Laborers': 1, 'Managers': 2, 'Medicine staff': 3,
+    'Cooking staff': 4, 'Sales staff': 5, 'Accountants': 6, 'High skill tech staff': 7,
+    'Cleaning staff': 8, 'Drivers': 9, 'Low-skill Laborers': 10, 'IT staff': 11,
+    'Waiters/barmen staff': 12, 'Core staff': 13, 'Security staff': 14, 'HR staff': 15,
+    'Secretaries': 16, 'Realty agents': 17
+}
+GENDER_map = {'Laki-laki': 1, 'Perempuan': 0}
+Marital_status_map = {
+    'Married': 0, 'Single / not married': 1, 'Civil marriage': 2, 'Separated': 3, 'Widow': 4
+}
+Type_Income_map = {
+    'Commercial associate': 0, 'Pensioner': 1, 'Working': 2, 'State servant': 3
+}
+
+df = pd.DataFrame()
+
+if st.button('Prediksi Loan Customer'):
+    Name = Name
+    Housing_type_value = Housing_type_map[Housing_type]
+    EDUCATION_value = EDUCATION_map[EDUCATION]
+    Type_Occupation_value = Type_Occupation_map[Type_Occupation]
+    GENDER_value = GENDER_map[GENDER]
+    Marital_status_value = Marital_status_map[Marital_status]
+    Type_Income_value = Type_Income_map[Type_Income]
+
+    card_credit = [GENDER_value, Type_Occupation_value, Type_Income_value, Marital_status_value, EDUCATION_value, AGE, Housing_type_value, YEAR_EMPLOYED]
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    df = pd.DataFrame([card_credit], columns=['GENDER', 'Type_Occupation', 'Type_Income', 'Marital_status', 'EDUCATION', 'AGE', 'Housing_type', 'YEAR_EMPLOYED'])
+    if not df.empty:
+        c_scaler = scaler.transform(df.values.reshape(1, -1))
+        
+        loan_prediction = model.predict(c_scaler)
+        
+        if loan_prediction[0] == 1:
+            loan_diagnose = f"Pengajuan Kartu Kredit Atas Nama {Name} Ditolak"
+        else:
+            loan_diagnose = f"Pengajuan Kartu Kredit Atas Nama {Name} Diterima"
+        
+        if loan_prediction[0] == 1:
+            st.error(loan_diagnose, icon="❌")
+        else:
+            st.success(loan_diagnose, icon="✅")
+    else:
+        st.error("Harap isi semua form terlebih dahulu.")