295 lines
9.6 KiB
Python
295 lines
9.6 KiB
Python
import streamlit as st
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import os
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import mysql.connector
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import pandas as pd
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import json
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# Fungsi untuk mendapatkan gambar sebagai base64
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def get_image_as_base64(image_path):
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import base64
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with open(image_path, "rb") as img_file:
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return base64.b64encode(img_file.read()).decode("utf-8")
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# Fungsi untuk koneksi ke database
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def connect_to_db():
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try:
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conn = mysql.connector.connect(
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host=st.secrets["mysql"]["host"],
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user=st.secrets["mysql"]["user"],
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password=st.secrets["mysql"]["password"],
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database=st.secrets["mysql"]["dbname"],
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port=st.secrets["mysql"]["port"]
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)
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return conn
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except mysql.connector.Error as e:
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st.error(f"Koneksi ke database gagal: {e}")
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return None
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# Fungsi untuk mengambil data dari tabel history_prediction
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def get_all_predictions():
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conn = connect_to_db()
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if conn:
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try:
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query = """SELECT employee_id as ID_Karyawan, hasil_prediksi_klasifikasi as Hasil_Prediksi_Retensi,
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hasil_prediksi_regresi as Hasil_Prediksi_Lama_Kerja, waktu_prediksi as Waktu_Prediksi
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FROM history_prediction"""
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df = pd.read_sql(query, conn) # Menggunakan Pandas untuk membaca data
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return df
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except mysql.connector.Error as e:
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st.error(f"Terjadi kesalahan saat mengambil data dari database: {e}")
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return pd.DataFrame() # Kembalikan DataFrame kosong jika terjadi error
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finally:
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conn.close()
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# Fungsi untuk mengambil data dari tabel shap_pred_result
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def get_shap_top_features():
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conn = connect_to_db()
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if conn:
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try:
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query = "SELECT employee_id as ID_Karyawan, shap_values FROM shap_pred_result"
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df = pd.read_sql(query, conn)
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# Ekstraksi dan format ulang shap_values
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result = []
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for _, row in df.iterrows():
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employee_id = row['ID_Karyawan']
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shap_values = json.loads(row['shap_values'])
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# Pastikan nilai SHAP berupa angka tunggal, jika list maka ambil rata-rata
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normalized_shap_values = {
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feature: (sum(value) / len(value) if isinstance(value, list) else value)
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for feature, value in shap_values.items()
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}
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# Ambil 5 fitur dengan SHAP value tertinggi (absolut)
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top_features = sorted(
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normalized_shap_values.items(),
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key=lambda x: abs(x[1]),
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reverse=True
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)[:5]
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# Buat format tabel baru
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formatted_row = {
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"ID_Karyawan": employee_id
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}
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for i, (feature, value) in enumerate(top_features, start=1):
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formatted_row[f"Nama_Fitur_{i}"] = feature
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formatted_row[f"Besar_Value_{i}"] = value
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result.append(formatted_row)
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# Konversi ke DataFrame
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formatted_df = pd.DataFrame(result)
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return formatted_df
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except mysql.connector.Error as e:
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st.error(f"Terjadi kesalahan saat mengambil data: {e}")
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return pd.DataFrame()
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finally:
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conn.close()
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# Fungsi untuk menampilkan navbar
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def navbar():
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current_page = st.session_state.get("page", "Home")
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logo_path = os.path.join(os.path.dirname(__file__), "../asset/logo.png")
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# Cek status login
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if 'logged_in' in st.session_state and st.session_state['logged_in']:
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login_button_text = "Logout"
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login_button_link = "?page=Login&logout=true" # Tambahkan parameter logout
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else:
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login_button_text = "Logout"
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login_button_link = "?page=Login"
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st.markdown(
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f"""
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<style>
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.navbar {{
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display: flex;
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align-items: center;
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justify-content: space-between;
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padding: 10px 20px;
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font-family: 'Poppins', sans-serif;
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margin-top: 20px; /* Hilangkan jarak atas */
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background-color: #D0EEFF; /* Background navbar */
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border-radius: 15px; /* Membulatkan sudut navbar */
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}}
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.navbar .logo {{
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display: flex;
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align-items: center;
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}}
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.navbar .logo img {{
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height: 40px;
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margin-right: 10px;
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}}
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.navbar .nav-links {{
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display: flex;
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gap: 60px;
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}}
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.navbar .nav-links a {{
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color: black;
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text-decoration: none;
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font-size: 16px;
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font-weight: bold;
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}}
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.navbar .nav-links a:hover {{
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color: royalblue;
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}}
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.navbar .nav-links a.active {{
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color: #264CBE; /* Warna saat aktif */
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text-decoration: underline; /* Garis bawah saat aktif */
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}}
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.navbar .login-button {{
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background-color: #264CBE;
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color: white;
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border: none;
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padding: 8px 15px;
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border-radius: 5px;
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font-size: 16px;
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font-weight: bold;
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cursor: pointer;
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text-decoration: none;
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}}
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.navbar .login-button:hover {{
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background-color: white;
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color: #264CBE;
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}}
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</style>
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<div class="navbar">
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<div class="logo">
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<img src="data:image/png;base64,{get_image_as_base64(logo_path)}" alt="Logo">
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</div>
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<div class="nav-links">
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<a href="?page=Prediksi" class="{ 'active' if st.session_state.page == 'Prediksi' else '' }">Prediksi</a>
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<a href="?page=exploration" class="{ 'active' if st.session_state.page == 'exploration' else '' }">Dashboard</a>
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<a href="?page=report" class="{ 'active' if st.session_state.page == 'report' else '' }">Laporan</a>
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</div>
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<a class="login-button" href="{login_button_link}">{login_button_text}</a>
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</div>
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""",
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unsafe_allow_html=True,
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)
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def show_report():
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# Tampilkan navbar
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navbar()
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st.markdown("""
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<style>
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.stDownloadButton > button {
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background-color: #264CBE;
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color: white;
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font-family: 'Poppins', sans-serif;
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font-size: 16px;
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font-weight: 600;
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border: none;
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border-radius: 5px;
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padding: 10px;
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cursor: pointer;
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margin-top: 20px;
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width: 100%;
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}
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.stDownloadButton > button:hover {
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background-color: #ffffff;
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color: #264CBE;
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}
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/* Footer */
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.footer {
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width: 100%;
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background-color: #D0EEFF;
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padding: 20px !important;
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text-align: center;
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font-family: 'Poppins', sans-serif;
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border-radius: 10px;
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margin-top: 50px !important;
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}
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.footer p {
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margin: 5px 0;
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font-size: 14px;
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color: #333333;
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}
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</style>
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""", unsafe_allow_html=True)
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# Konten halaman Laporan
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st.markdown(
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"""
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<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&display=swap" rel="stylesheet">
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<h3 style="text-align: center; font-family: 'Poppins', sans-serif;">
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Halaman Laporan
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</h3>
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""", unsafe_allow_html=True
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)
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# Menu dropdown
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menu_option = st.selectbox(
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"Pilih data yang ingin ditampilkan:",
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["History Prediksi", "History SHAP Values"]
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)
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if menu_option == "History Prediksi":
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st.markdown(
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"""
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<h5 style="text-align: center; font-family: 'Poppins', sans-serif;">
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Tabel Histori Prediksi
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</h5>
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""", unsafe_allow_html=True
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)
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# Ambil data dari tabel history_prediction
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df = get_all_predictions()
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if not df.empty:
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# Tampilkan data dalam bentuk tabel
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st.dataframe(df)
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# Tombol untuk mendownload CSV
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csv = df.to_csv(index=False) # Konversi DataFrame ke CSV tanpa indeks
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st.download_button(
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label="Download Tabel sebagai CSV",
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data=csv,
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file_name="history_prediction.csv",
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mime="text/csv",
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)
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else:
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st.write("Tidak ada data yang tersedia di tabel history_prediction.")
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elif menu_option == "History SHAP Values":
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st.markdown(
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"""
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<h5 style="text-align: center; font-family: 'Poppins', sans-serif;">
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Tabel Histori SHAP Values
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</h5>
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""", unsafe_allow_html=True
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)
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# Ambil data dari tabel shap_pred_result
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df = get_shap_top_features()
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if not df.empty:
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# Tampilkan data dalam bentuk tabel
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st.dataframe(df)
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# Tombol untuk mendownload CSV
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csv = df.to_csv(index=False) # Konversi DataFrame ke CSV tanpa indeks
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st.download_button(
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label="Download Tabel sebagai CSV",
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data=csv,
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file_name="shap_pred_result.csv",
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mime="text/csv",
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)
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else:
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st.write("Tidak ada data yang tersedia di tabel shap_pred_result.")
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# Footer
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st.markdown(
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"""
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<div class="footer">
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<p><strong>2025 © Jesselyn Mu</strong></p>
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<p>Untuk informasi lebih lanjut, dapat mengirim email ke mujesselyn@gmail.com</p>
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</div>
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""",
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unsafe_allow_html=True
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)
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# Jalankan fungsi show_report
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if __name__ == "__main__":
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show_report() |