modeldeploy

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import seaborn as sns
+import matplotlib.pyplot as plt
+import json
+
+# Fungsi untuk memuat data
+def load_data():
+    df = pd.read_csv('h8dsft_P1G3_Muhammad_Arief_Kurniawan.csv')
+    return df
+
+# Fungsi untuk memuat komponen model dan preprocessing
+def load_components():
+    model = joblib.load('stacked_es_model.pkl')
+    scaler = joblib.load('model_scaler.pkl')
+    with open('numeric_columns.txt', 'r') as file_1:
+        numeric_columns = json.load(file_1)
+    with open('categorical_columns.txt', 'r') as file_2:
+        categorical_columns = json.load(file_2)
+    return model, scaler, numeric_columns, categorical_columns
+
+# Fungsi untuk mendapatkan input dari pengguna
+def get_user_input(numeric_columns, categorical_columns):
+    inputs = {}
+    for col in numeric_columns + categorical_columns:
+        if col in categorical_columns:
+            inputs[col] = st.selectbox(col, [0, 1])
+        else:
+            default_val = np.expm1(50.0) if col != 'time' else 50.0
+            inputs[col] = np.log1p(st.number_input(col, value=default_val))
+    return pd.DataFrame([inputs])
+
+def main():
+    st.title("Prediksi dan Eksplorasi Gagal Jantung")
+
+    # Sidebar untuk navigasi
+    choice = st.sidebar.selectbox("Pilih Halaman", ["Beranda", "Eksplorasi Data", "Prediksi"])
+
+    if choice == "Beranda":
+        st.write("Selamat datang di aplikasi prediksi dan eksplorasi gagal jantung.")
+        st.write("Silakan pilih halaman di sidebar untuk melanjutkan.")
+
+    elif choice == "Eksplorasi Data":
+        data = load_data()
+        st.write("### Informasi Dasar Data")
+        st.write(data.info())
+        st.write("### Deskripsi Statistik")
+        st.write(data.describe())
+        st.write("### 5 Data Pertama")
+        st.write(data.head())
+        
+        st.write("### Visualisasi Distribusi Target: DEATH_EVENT")
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.countplot(x=data['DEATH_EVENT'], ax=ax) 
+        st.pyplot(fig)
+
+    elif choice == "Prediksi":
+        model, scaler, numeric_columns, categorical_columns = load_components()
+        user_input = get_user_input(numeric_columns, categorical_columns)
+        user_input[numeric_columns] = scaler.transform(user_input[numeric_columns])
+        prediction = model.predict(user_input)
+        if prediction[0] == 0:
+            st.write("Prediksi: Tidak Ada Gagal Jantung")
+        else:
+            st.write("Prediksi: Gagal Jantung")
+        st.subheader("Data Input Pengguna")
+        st.write(user_input)
+
+if __name__ == "__main__":
+    main()

categorical_columns.txt

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+["anaemia", "diabetes", "high_blood_pressure", "sex", "smoking"]

eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Fungsi untuk memuat data
+def load_data():
+    df = pd.read_csv('h8dsft_P1G3_Muhammad_Arief_Kurniawan.csv')  # Adjust the path if needed
+    return df
+
+def main():
+    st.title("Eksplorasi Data Analitik")
+
+    # Load data
+    data = load_data()
+
+    # Display basic data information
+    st.write("### Informasi Dasar Data")
+    st.write(data.info())
+    st.write("### Deskripsi Statistik")
+    st.write(data.describe())
+    st.write("### 5 Data Pertama")
+    st.write(data.head())
+
+    # Visualization with Streamlit
+    st.write("### Visualisasi Distribusi Target: DEATH_EVENT")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.countplot(x=data['DEATH_EVENT'], ax=ax) 
+    st.pyplot(fig)
+
+
+    # You can add more visualizations or features as needed
+
+if __name__ == "__main__":
+    main()

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ccba0dda6850d57aae324ed74dc8a9b7b65d71a5c478c1ddd1ca682eea6c68b
+size 810

numeric_columns.txt

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+["age", "creatinine_phosphokinase", "ejection_fraction", "platelets", "serum_creatinine", "serum_sodium", "time"]

prediction.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+import json
+
+# Fungsi untuk memuat model dan komponen preprocessing
+def load_components():
+    model = joblib.load('stacked_es_model.pkl')
+    scaler = joblib.load('model_scaler.pkl')
+    with open('numeric_columns.txt', 'r') as file_1:
+        numeric_columns = json.load(file_1)
+    with open('categorical_columns.txt', 'r') as file_2:
+        categorical_columns = json.load(file_2)
+    return model, scaler, numeric_columns, categorical_columns
+
+# Fungsi untuk mendapatkan input dari pengguna
+def get_user_input(numeric_columns, categorical_columns):
+    inputs = {}
+    for col in numeric_columns + categorical_columns:
+        if col in categorical_columns:
+            inputs[col] = st.selectbox(col, [0, 1])
+        else:
+            # Menggunakan np.expm1 untuk membalikkan transformasi log untuk tampilan dan input
+            default_val = np.expm1(50.0) if col != 'time' else 50.0
+            inputs[col] = np.log1p(st.number_input(col, value=default_val))
+    return pd.DataFrame([inputs])
+
+def main():
+    st.title("Prediksi Gagal Jantung")
+
+    # Muat model dan komponen preprocessing
+    model, scaler, numeric_columns, categorical_columns = load_components()
+
+    # Dapatkan input dari pengguna
+    user_input = get_user_input(numeric_columns, categorical_columns)
+
+    # Preprocess input
+    user_input[numeric_columns] = scaler.transform(user_input[numeric_columns])
+
+    # Lakukan prediksi
+    prediction = model.predict(user_input)
+    if prediction[0] == 0:
+        st.write("Prediksi: Tidak Ada Gagal Jantung")
+    else:
+        st.write("Prediksi: Gagal Jantung")
+
+    # Opsi: Tampilkan data input (untuk verifikasi)
+    st.subheader("Data Input Pengguna")
+    st.write(user_input)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn==1.2.2

stacked_es_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9053ef824aec6ff6f04fc049702d52b829c992e50e661641c38357dad77ed95
+size 641887