Upload 6 files

requirements.txt

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+streamlit
+tensorflow-cpu
+numpy
+pandas

CarData.csv

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+predicted_label,jenis_mobil,merek_mobil,harga_mobil_min,harga_mobil_max
+0,Alphard,Toyota,1356100000,1950000000
+1,Innova 2015-2022,Toyota,215000000,480000000
+2,Innova Zennix,Toyota,425000000,610000000
+3,New Veloz,Toyota,288000000,350000000

SalaryData.csv

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+pekerjaan,rentang_usia,rentang_gaji
+Staff - Fresh Graduate,"(21, 25)","(2000000, 4000000)"
+Asisten Manajer,"(26, 29)","(5000000, 8000000)"
+Manajer,"(29, 35)","(10000000, 15000000)"
+General Manajer,"(36, 42)","(20000000, 30000000)"
+Kepala Divisi atau Direksi,"(42, 100)","(50000000, 100000000)"

app.py

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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 
+
+# 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
+    matching_income = df_income_info[
+    (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]
+        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')
+    uploaded_files = st.file_uploader("Please upload an only Toyota Car image for prediction", type=["jpg"])
+    
+    if uploaded_files is not None:
+        for fn in uploaded_files:
+            # Prediction with upload image
+            path = fn.name
+            img = Image.open(fn)
+            st.image(img, caption="Uploaded Image", use_column_width=True)
+            img = img.resize((224, 224))
+            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, df_car_info, df_income_info)
+
+            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()

car_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b8e31e92f98364cc8061a351141afa0190a6a3ae83afa98b562e74d725bc24a
+size 83272072