Update app.py

app.py

@@ -4,14 +4,28 @@ import streamlit as st
 from PIL import Image
 import numpy as np
 import pandas as pd
-import ast
 
 # Load Sequential Model
 model_cnn = load_model('car_model.h5')
 
 # Load DataFrame
-df_car_info = pd.read_csv ('CarData.csv')
-df_income_info = pd.read_csv ('SalaryData.csv')
+income_data = {
+    'pekerjaan': ["Staff - Fresh Graduate", "Asisten Manajer", "Manajer", "General Manajer", "Kepala Divisi atau Direksi"],
+    'rentang_usia': [(21, 25), (26, 29), (29, 35), (36, 42), (42, 100)],
+    'rentang_gaji': [(2000000, 4000000), (5000000, 8000000), (10000000, 15000000), (20000000, 30000000), (50000000, 100000000)]
+}
+
+data = {
+    'predicted_label': [0, 1, 2, 3],
+    'jenis_mobil': ["Alphard", "Innova 2015-2022", "Innova Zennix", "New Veloz"],
+    'merek_mobil': ["Toyota", "Toyota", "Toyota", "Toyota"],
+    'harga_mobil_min': [1356100000, 215000000, 425000000, 288000000],
+    'harga_mobil_max': [1950000000, 480000000, 610000000, 350000000]}
+
+# Create DataFrame
+df_car_info = pd.DataFrame(data)
+df_income_info = pd.DataFrame(income_data)
+
 
 def calculate_cicilan(predicted_label):
     # Get car info based on predicted_label
@@ -22,12 +36,9 @@ def calculate_cicilan(predicted_label):
     cicilan_per_bulan = harga_mobil_avg / 60  # 5 tahun (60 bulan)
 
     # Find the matching gaji range based on cicilan_per_bulan
-    df_income_info['rentang_gaji'] = df_income_info['rentang_gaji'].apply(lambda x: ast.literal_eval(x))
     matching_income = df_income_info[
-    (cicilan_per_bulan / 0.3 >= df_income_info['rentang_gaji'].apply(lambda x: float(x.split('-')[0]))) &
-    (cicilan_per_bulan / 0.3 <= df_income_info['rentang_gaji'].apply(lambda x: float(x.split('-')[1])))]
-
-
+    (cicilan_per_bulan / 0.3 >= df_income_info['rentang_gaji'].apply(lambda x: x[0])) &
+    (cicilan_per_bulan / 0.3 <= df_income_info['rentang_gaji'].apply(lambda x: x[1]))]
 
     if not matching_income.empty:
         rentang_usia = matching_income['rentang_usia'].iloc[0]