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| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| 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') | |
| def calculate_cicilan(predicted_label): | |
| # Get car info based on predicted_label | |
| car_info = df_car_info[df_car_info['predicted_label'] == predicted_label].iloc[0] | |
| # Calculate cicilan_per_bulan | |
| harga_mobil_avg = (car_info['harga_mobil_min'] + car_info['harga_mobil_max']) / 2 | |
| 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])))] | |
| if not matching_income.empty: | |
| rentang_usia = matching_income['rentang_usia'].iloc[0] | |
| rentang_gaji_min = int(matching_income['rentang_gaji'].iloc[0][0]) | |
| rentang_gaji_max = int(matching_income['rentang_gaji'].iloc[0][1]) | |
| pekerjaan = matching_income['pekerjaan'].iloc[0] | |
| return { | |
| 'Jenis Mobil': car_info['jenis_mobil'], | |
| 'Merek Mobil': car_info['merek_mobil'], | |
| 'Rentang Usia': f"{rentang_usia[0]}-{rentang_usia[1]} Tahun", | |
| 'Rentang Gaji': f"Rp {rentang_gaji_min} - {rentang_gaji_max}", | |
| 'Pekerjaan': pekerjaan | |
| } | |
| else: | |
| return None | |
| def run(): | |
| st.title('Toyota Car Prediction') | |
| st.subheader('This Prediction use for provide passanger information from his car') | |
| st.subheader('Only Uploaded Image New Alphard, Innova 2015 - 2022, Innova Zennix, New Veloz') | |
| st.image("Yow.jpeg") | |
| st.markdown('---') | |
| st.subheader('Car Prediction') | |
| upload = st.file_uploader("Please upload an only Toyota Car image for prediction", type=["jpg"]) | |
| if upload is not None: | |
| img = Image.open(upload) | |
| img = img.resize((224, 224)).convert("RGB") | |
| img_array = np.array(img) | |
| img_array = img_array / 255.0 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| # Model inference | |
| images = np.vstack([img_array]) | |
| classes = model_cnn.predict(images) | |
| # Memilih label kelas | |
| predicted_label = np.argmax(classes) | |
| # Assuming df_car_info and df_income_info are available | |
| result = calculate_cicilan(predicted_label) | |
| st.write("Hasil Prediksi:") | |
| if result is not None: | |
| for key, value in result.items(): | |
| st.write(f"{key}: {value}") | |
| else: | |
| st.write("Maaf, tidak ditemukan.") | |
| if __name__ == '__main__': | |
| run() | |