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1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 | import os
# Fix httpx NO_PROXY parsing bug on Windows when it contains IPv6 loopback '::1'
for env_var in ["NO_PROXY", "no_proxy"]:
if env_var in os.environ:
os.environ[env_var] = os.environ[env_var].replace(",::1", "").replace("::1", "")
import sqlite3
import datetime
import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
from xgboost import XGBRegressor
import plotly.graph_objects as go
# --- KONFIGURASI HALAMAN ---
st.set_page_config(page_title="BTA Smart Monitor", page_icon="🏗️", layout="wide")
# ============================================================
# CUSTOM CSS — Estetika Warm Cream & Minimalis ala Replicate
# ============================================================
st.markdown("""
<style>
/* Google Fonts */
@import url('https://fonts.googleapis.com/css2?family=Bricolage+Grotesque:wght@600;700;800&family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500;600&display=swap');
/* Root Variables - Replicate Color System */
:root {
--colors-canvas: #f9f7f3; /* Warm Cream Canvas */
--colors-surface-bone: #f3f0e8; /* Half-step deeper cream */
--colors-surface-card: #ffffff; /* Pure White */
--colors-surface-dark: #202020; /* Surface Dark / Ink */
--colors-primary: #ea2804; /* Replicate Brand Orange */
--colors-primary-deep: #c01f00; /* Orange Pressed */
--colors-badge-success: #2b9a66; /* Semantic Green */
--colors-hairline: rgba(32,32,32,0.12); /* 1px low-contrast dividers */
--colors-hairline-strong: #202020;
--text-ink: #202020;
--text-body: #3a3a3a;
--text-charcoal: #575757;
--text-mute: #646464;
--text-on-dark: #fcfcfc;
--text-on-dark-mute: rgba(252,252,252,0.72);
}
/* Global App Styles */
.stApp {
background-color: var(--colors-canvas) !important;
color: var(--text-ink) !important;
font-family: 'Inter', sans-serif !important;
}
/* Hide Streamlit Default UI Elements */
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
header {visibility: hidden;}
/* Remove Default Sidebar Padding since we are NOT using it */
section[data-testid="stSidebar"] {
display: none !important;
}
.main .block-container {
padding-top: 48px !important;
padding-bottom: 60px !important;
max-width: 1280px !important; /* Max content width */
margin: 0 auto;
}
/* Hero Block styling */
.hero-container {
margin-bottom: 32px;
position: relative;
}
.orange-stamp {
display: inline-flex;
align-items: center;
background-color: var(--colors-primary);
color: var(--colors-surface-card);
font-family: 'Bricolage Grotesque', sans-serif;
font-size: 11px;
font-weight: 800;
padding: 4px 12px;
border-radius: 9999px; /* rounded.full */
text-transform: uppercase;
letter-spacing: 1px;
margin-bottom: 16px;
}
.display-title {
font-family: 'Bricolage Grotesque', sans-serif;
font-size: 64px;
font-weight: 800;
line-height: 0.95;
letter-spacing: -2.5px;
color: var(--text-ink);
margin: 0 0 12px 0;
}
.display-subtitle {
font-family: 'Inter', sans-serif;
font-size: 17px;
font-weight: 400;
color: var(--text-charcoal);
line-height: 1.5;
max-width: 720px;
margin: 0 0 24px 0;
}
/* Streamlit Tabs Customization to Match Replicate Sub-nav-pills */
.stTabs [data-baseweb="tab-list"] {
gap: 8px;
border-bottom: 1px solid var(--colors-hairline) !important;
padding-bottom: 12px;
margin-bottom: 32px;
}
.stTabs [data-baseweb="tab"] {
background-color: var(--colors-surface-bone) !important;
color: var(--text-charcoal) !important;
font-family: 'Inter', sans-serif;
font-weight: 600 !important;
font-size: 14px !important;
padding: 8px 22px !important;
border-radius: 9999px !important; /* rounded.full */
border: 1px solid rgba(32,32,32,0.06) !important;
transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1);
height: auto !important;
}
.stTabs [data-baseweb="tab"]:hover {
background-color: var(--colors-surface-card) !important;
color: var(--text-ink) !important;
border-color: var(--colors-hairline-strong) !important;
}
.stTabs [aria-selected="true"] {
background-color: var(--colors-surface-dark) !important; /* Surface Dark */
border-color: var(--colors-surface-dark) !important;
}
.stTabs [aria-selected="true"],
.stTabs [aria-selected="true"] p,
.stTabs [aria-selected="true"] span,
.stTabs [aria-selected="true"] div,
.stTabs [aria-selected="true"] * {
color: var(--text-on-dark) !important;
font-weight: 600 !important;
}
/* Tab active indicator bar removal */
.stTabs [data-baseweb="tab-highlight-indicator"] {
display: none !important;
}
/* Replicate Model Cards / KPI Grid */
.rep-kpi-container {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(180px, 1fr));
gap: 16px;
margin-bottom: 24px;
}
.rep-kpi-card {
background-color: var(--colors-surface-card); /* Surface Card */
border: 1px solid var(--colors-hairline); /* Hairline */
border-radius: 10px; /* rounded.md */
padding: 20px;
transition: all 0.2s ease-in-out;
position: relative;
}
.rep-kpi-card:hover {
border-color: var(--colors-hairline-strong);
transform: translateY(-2px);
}
.rep-kpi-label {
font-family: 'Inter', sans-serif;
font-size: 11px;
font-weight: 600;
color: var(--text-mute);
text-transform: uppercase;
letter-spacing: 0.5px;
margin-bottom: 8px;
}
.rep-kpi-value {
font-family: 'Bricolage Grotesque', sans-serif;
font-size: 32px;
font-weight: 700;
color: var(--text-ink);
line-height: 1;
}
.rep-kpi-value-mono {
font-family: 'JetBrains Mono', monospace;
font-size: 28px;
font-weight: 600;
color: var(--text-ink);
line-height: 1;
}
.rep-kpi-unit {
font-family: 'Inter', sans-serif;
font-size: 13px;
font-weight: 400;
color: var(--text-charcoal);
}
/* Status Badge System */
.rep-status-container {
margin-bottom: 24px;
}
.rep-status-pill {
display: inline-flex;
align-items: center;
gap: 8px;
padding: 6px 16px;
border-radius: 9999px; /* rounded.full */
font-family: 'Inter', sans-serif;
font-size: 13px;
font-weight: 600;
}
.rep-status-safe {
background-color: rgba(43, 154, 102, 0.1);
color: var(--colors-badge-success);
border: 1px solid rgba(43, 154, 102, 0.2);
}
.rep-status-warning {
background-color: rgba(234, 40, 4, 0.06);
color: var(--colors-primary);
border: 1px solid rgba(234, 40, 4, 0.12);
}
.rep-status-critical {
background-color: rgba(234, 40, 4, 0.12);
color: var(--colors-primary);
border: 1px solid var(--colors-primary);
animation: pulse-border 1.5s infinite;
}
.rep-pulse {
width: 8px;
height: 8px;
border-radius: 50%;
}
.rep-pulse-green { background-color: var(--colors-badge-success); }
.rep-pulse-orange { background-color: var(--colors-primary); }
@keyframes pulse-border {
0%, 100% { transform: scale(1); opacity: 1; }
50% { transform: scale(0.95); opacity: 0.8; }
}
/* Chart Block (Print-style pull quote container) */
.rep-chart-well {
background-color: var(--colors-surface-card);
border: 1px solid var(--colors-hairline);
border-radius: 10px; /* rounded.md */
padding: 24px;
margin-bottom: 28px;
}
/* Section Openers inside tabs */
.tab-section-header {
font-family: 'Bricolage Grotesque', sans-serif;
font-size: 28px;
font-weight: 700;
letter-spacing: -0.8px;
color: var(--text-ink);
margin-bottom: 8px;
line-height: 1.1;
}
.tab-section-desc {
font-family: 'Inter', sans-serif;
font-size: 14px;
color: var(--text-charcoal);
margin-bottom: 24px;
line-height: 1.5;
}
/* Style Form & Border Containers bawaan Streamlit secara langsung (Native integration) */
div[data-testid="stForm"],
div[data-testid="stVerticalBlockBorder"] {
background-color: var(--colors-surface-bone) !important;
border: 1px solid rgba(32,32,32,0.06) !important;
border-radius: 10px !important;
padding: 24px !important;
box-shadow: none !important;
margin-bottom: 16px !important;
}
/* Style Streamlit Inputs to align with Replicate Form System and prevent dark corner leak */
div[data-baseweb="input"] {
border-radius: 9999px !important; /* rounded.full */
border: 1px solid var(--colors-hairline) !important;
background-color: var(--colors-surface-card) !important;
padding: 0px 10px !important;
transition: border-color 0.2s ease-in-out;
}
div[data-baseweb="input"]:focus-within {
border-color: var(--colors-hairline-strong) !important;
box-shadow: 0 0 0 2px rgba(32,32,32,0.06) !important;
}
/* Force BaseWeb inner wrapper to be transparent to prevent dark background leak on number_input/date_input */
div[data-baseweb="input"] > div {
background-color: transparent !important;
}
/* Remove default inner input border and backgrounds */
.stTextInput input,
.stNumberInput input,
.stDateInput input {
border: none !important;
background-color: transparent !important;
box-shadow: none !important;
border-radius: 9999px !important;
color: var(--text-ink) !important;
font-family: 'Inter', sans-serif !important;
height: 40px !important;
padding: 8px 12px !important;
}
/* Style Selectbox to match rounded.full input */
div[data-baseweb="select"] {
border-radius: 9999px !important;
border: 1px solid var(--colors-hairline) !important;
background-color: var(--colors-surface-card) !important;
}
div[data-baseweb="select"] div[role="button"] {
border-radius: 9999px !important;
border: none !important;
background-color: transparent !important;
height: 40px !important;
color: var(--text-ink) !important;
}
/* Override number input +/- buttons to light mode design */
.stNumberInput button,
div[data-testid="stNumberInputStepDown"],
div[data-testid="stNumberInputStepUp"],
div[data-testid="stNumberInputStepDown"] *,
div[data-testid="stNumberInputStepUp"] * {
background-color: var(--colors-surface-bone) !important;
color: var(--text-ink) !important;
border-color: rgba(32,32,32,0.12) !important;
}
.stNumberInput button:hover {
background-color: var(--colors-surface-card) !important;
color: var(--colors-primary) !important;
}
/* Custom outline and solid buttons */
div.stButton > button {
background-color: var(--colors-surface-card) !important;
color: var(--text-ink) !important;
border: 1px solid var(--colors-hairline-strong) !important;
border-radius: 9999px !important; /* rounded.full */
font-family: 'Inter', sans-serif !important;
font-weight: 600 !important;
padding: 10px 28px !important;
font-size: 15px !important;
height: 44px !important;
transition: all 0.2s ease-in-out !important;
box-shadow: none !important;
}
div.stButton > button:hover {
background-color: var(--colors-surface-bone) !important;
border-color: var(--colors-hairline-strong) !important;
}
/* Primary Accent Button Style (Replicate Stamp Orange) */
div.stButton > button[kind="primary"] {
background-color: var(--colors-primary) !important;
color: #ffffff !important;
border: 1px solid var(--colors-primary) !important;
}
div.stButton > button[kind="primary"]:hover {
background-color: var(--colors-primary-deep) !important;
border-color: var(--colors-primary-deep) !important;
}
/* File Uploader styling - White drop area with hairline strong border */
div[data-testid="stFileUploader"] > section {
background-color: var(--colors-surface-card) !important;
border: 1px dashed var(--colors-hairline-strong) !important;
border-radius: 10px !important;
padding: 24px !important;
}
/* File Uploader inner buttons color fixing (preventing dark mode overlay) */
[data-testid="stFileUploader"] button,
[data-testid="stFileUploader"] [role="button"] {
background-color: var(--colors-surface-bone) !important;
color: var(--text-ink) !important;
border: 1px solid var(--colors-hairline-strong) !important;
border-radius: 9999px !important;
padding: 8px 20px !important;
box-shadow: none !important;
}
[data-testid="stFileUploader"] button *,
[data-testid="stFileUploader"] [role="button"] * {
background-color: transparent !important;
color: var(--text-ink) !important;
}
[data-testid="stFileUploader"] button:hover,
[data-testid="stFileUploader"] [role="button"]:hover {
background-color: var(--colors-surface-card) !important;
border-color: var(--colors-primary) !important;
}
/* Footer styling */
.rep-footer {
border-top: 1px solid var(--colors-hairline);
padding-top: 24px;
margin-top: 48px;
text-align: center;
font-family: 'Inter', sans-serif;
font-size: 12px;
color: var(--text-mute);
}
.rep-footer a {
color: var(--colors-primary);
text-decoration: none;
font-weight: 500;
}
.rep-footer a:hover {
text-decoration: underline;
}
/* Highlight badge label */
.rep-badge {
display: inline-block;
background-color: var(--colors-surface-bone);
color: var(--text-charcoal);
font-family: 'JetBrains Mono', monospace;
font-size: 11px;
font-weight: 500;
padding: 2px 8px;
border-radius: 4px;
border: 1px solid rgba(32,32,32,0.06);
}
/* Force global light theme text contrast to override Streamlit dark mode fallbacks */
div[data-testid="stWidgetLabel"] p,
label[data-testid="stWidgetLabel"],
label[data-testid="stWidgetLabel"] p,
div[data-testid="stMetricLabel"],
div[data-testid="stMetricLabel"] p,
div[data-testid="stMetricValue"],
div[data-testid="stMetricValue"] div,
div[data-testid="stMetricDelta"] div,
div[data-testid="stFileUploaderText"],
div[data-testid="stFileUploaderText"] small,
div[data-testid="stFileUploaderText"] span,
div[data-testid="stFileUploaderText"] p,
[data-testid="stFileUploader"] label,
.stMarkdown p,
.stMarkdown li,
.stMarkdown span,
.stMarkdown h1,
.stMarkdown h2,
.stMarkdown h3,
.stMarkdown h4,
.stMarkdown h5,
.stMarkdown h6,
.stExpander summary,
.stExpander summary p,
.stExpander p,
div[data-testid="stExpander"] p,
div[data-testid="stExpander"] summary,
div[data-testid="stExpander"] summary p,
.stDataFrame td,
.stDataFrame th,
.stDataFrame div,
div[data-testid="stTable"] td,
div[data-testid="stTable"] th,
div[data-testid="stNotification"] p,
div[data-testid="stNotification"] span,
div[data-testid="stHelpText"] p,
div[data-testid="stForm"] label,
.stForm p,
.stForm span,
.stTabs p,
.stTabs span {
color: #202020 !important;
}
/* Keep explicit dark backgrounds legible and prevent active/hover states from darkening text */
div.stButton > button[kind="primary"],
div.stButton > button[kind="primary"]:hover,
div.stButton > button[kind="primary"]:active,
div.stButton > button[kind="primary"]:focus,
div.stButton > button[kind="primary"] p,
div.stButton > button[kind="primary"] span,
div.stButton > button[kind="primary"] div,
div.stButton > button[kind="primary"] * {
color: #ffffff !important;
}
/* Force default outline button to always have dark legible text */
div.stButton > button,
div.stButton > button:hover,
div.stButton > button:active,
div.stButton > button:focus,
div.stButton > button p,
div.stButton > button span,
div.stButton > button div,
div.stButton > button * {
color: #202020 !important;
}
.rep-status-pill.rep-status-safe,
.rep-status-pill.rep-status-safe span {
color: var(--colors-badge-success) !important;
}
.rep-status-pill.rep-status-warning,
.rep-status-pill.rep-status-warning span,
.rep-status-pill.rep-status-critical,
.rep-status-pill.rep-status-critical span {
color: var(--colors-primary) !important;
}
</style>
""", unsafe_allow_html=True)
# --- KONEKSI DATABASE ---
def init_db():
conn = sqlite3.connect('furnace_data.db', check_same_thread=False)
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS cycles
(id INTEGER PRIMARY KEY, start_date TEXT, initial_thickness REAL, active INTEGER)''')
c.execute('''CREATE TABLE IF NOT EXISTS daily_logs
(id INTEGER PRIMARY KEY, cycle_id INTEGER, log_date TEXT, raw_temp REAL)''')
# Jalankan migrasi kolom untuk menambah kolom baru jika belum ada
c.execute("PRAGMA table_info(daily_logs)")
columns = [row[1] for row in c.fetchall()]
for col, col_type in [('cone_front', 'REAL'), ('cone_back', 'REAL'), ('thickness', 'REAL'), ('lot_number', 'TEXT')]:
if col not in columns:
c.execute(f"ALTER TABLE daily_logs ADD COLUMN {col} {col_type}")
conn.commit()
return conn
# --- LOAD MODEL ---
HF_TOKEN = os.environ.get("HF_TOKEN")
REPO_MODEL = "Rendhaputra/BTA_predictive"
FILE_MODEL = "xgboost_bta.json"
FILE_PROPHET_MODEL = "model_prophet_bta.json"
@st.cache_resource
def load_model():
try:
path = hf_hub_download(repo_id=REPO_MODEL, filename=FILE_MODEL, token=HF_TOKEN)
m = XGBRegressor()
m.load_model(path)
return m
except Exception as e:
st.error(f"Gagal muat model: {e}")
return None
@st.cache_resource
def load_prophet_model():
try:
import json
from prophet.serialize import model_from_json
path = hf_hub_download(repo_id=REPO_MODEL, filename=FILE_PROPHET_MODEL, token=HF_TOKEN)
with open(path, "r") as f:
model_json = json.load(f)
return model_from_json(model_json)
except Exception as e:
st.warning(f"Gagal memuat model Prophet dari Hugging Face: {e}")
return None
model = load_model()
prophet_model = load_prophet_model()
db = init_db()
# --- FUNGSI HELPER ---
def get_active_cycle():
c = db.cursor()
c.execute("SELECT id, start_date, initial_thickness FROM cycles WHERE active = 1 ORDER BY id DESC LIMIT 1")
return c.fetchone()
def get_status_html(pred_mm, model_name="Model 1", batas_kritis=115.0, batas_warning=130.0):
"""Return HTML for Replicate-style status pill based on predicted thickness and model name."""
if pred_mm < batas_kritis:
return f'<div class="rep-status-pill rep-status-critical"><span class="rep-pulse rep-pulse-orange"></span> CRITICAL ({model_name}) — Sisa Ketebalan {pred_mm:.1f} mm. Segera lakukan perbaikan!</div>'
elif pred_mm < batas_warning:
return f'<div class="rep-status-pill rep-status-warning"><span class="rep-pulse rep-pulse-orange"></span> WARNING ({model_name}) — Sisa Ketebalan {pred_mm:.1f} mm. Siapkan jadwal pemeliharaan.</div>'
else:
return f'<div class="rep-status-pill rep-status-safe"><span class="rep-pulse rep-pulse-green"></span> AMAN ({model_name}) — Sisa Ketebalan {pred_mm:.1f} mm. Furnace dalam kondisi prima.</div>'
# ============================================================
# HERO HEADER (Estetika Editorial Replicate)
# ============================================================
st.markdown("""
<div class="hero-container">
<div class="orange-stamp">PREDICTIVE ANALYTICS ENGINE</div>
<h1 class="display-title">BTA Smart Monitor.</h1>
</div>
""", unsafe_allow_html=True)
# --- MODEL SELECTION ---
selected_model_type = st.selectbox(
"Pilih Model Analisis",
options=["Model 1", "Model 2"],
index=0,
help="Pilih model analitis untuk menghitung keausan dan memproyeksikan sisa umur BTA."
)
is_xgboost_selected = (selected_model_type == "Model 1")
# --- CHECK SIKLUS AKTIF ---
active_cycle = get_active_cycle()
if not active_cycle:
# --- NO ACTIVE CYCLE WELCOME SCREEN (Premium Color Block) ---
with st.container(border=True):
st.markdown('<div class="tab-section-header">Mulai Siklus Pengukuran BTA Baru</div>', unsafe_allow_html=True)
st.markdown('<p class="tab-section-desc">Sistem mendeteksi belum ada siklus pemantauan yang aktif. Daftarkan tanggal pemasangan BTA baru beserta ketebalan awal untuk memulai.</p>', unsafe_allow_html=True)
col1, col2 = st.columns(2)
with col1:
new_date = st.date_input("Tanggal Pemasangan BTA Baru", datetime.date.today())
with col2:
new_thick = st.number_input("Ketebalan Awal (mm)", min_value=100.0, max_value=300.0, value=230.0)
if st.button("Mulai Siklus Baru", type="primary"):
db.execute("INSERT INTO cycles (start_date, initial_thickness, active) VALUES (?, ?, ?)",
(new_date.isoformat(), new_thick, 1))
db.commit()
st.success("Siklus pemantauan BTA baru berhasil dimulai!")
st.rerun()
else:
cycle_id, start_date_str, initial_thickness = active_cycle
try:
start_date = pd.to_datetime(start_date_str).date()
except Exception:
start_date = datetime.date.fromisoformat(start_date_str)
# --- QUERY DATA HISTORIS ---
df_hist = pd.read_sql_query(
f"SELECT log_date, raw_temp, cone_front, cone_back, thickness, lot_number FROM daily_logs WHERE cycle_id={cycle_id}", db)
# Pre-computation jika data tidak kosong
has_data = not df_hist.empty
if has_data:
df_hist['log_date'] = pd.to_datetime(df_hist['log_date'], format="mixed", dayfirst=False)
df_hist = df_hist.sort_values('log_date').reset_index(drop=True)
latest_row = df_hist.iloc[-1]
latest_date = latest_row['log_date'].date()
df_ukur = df_hist.dropna(subset=['thickness']).copy()
if not df_ukur.empty:
last_ukur_date = df_ukur.iloc[-1]['log_date'].date()
last_ukur_thickness = float(df_ukur.iloc[-1]['thickness'])
else:
last_ukur_date = start_date
last_ukur_thickness = float(initial_thickness)
hari_ops = (latest_date - start_date).days
# Prediksi XGBoost Anchor-Based Wear Rate
laju_recent = -0.1359
tail_14 = df_hist['raw_temp'].tail(14)
suhu_asumsi = tail_14.mean() if not tail_14.empty else 350.0
if model is not None:
laju_proj_pred = float(model.predict(pd.DataFrame([[suhu_asumsi]], columns=['suhu_avg_periode']))[0])
else:
laju_proj_pred = -0.1359
laju_proj_efektif = 0.4 * laju_proj_pred + 0.6 * laju_recent
hari_sejak_ukur = (latest_date - last_ukur_date).days
pred_mm = last_ukur_thickness + (laju_proj_efektif * hari_sejak_ukur)
pred_mm = max(pred_mm, 100.0)
BATAS_KRITIS = 115.0
if pred_mm > BATAS_KRITIS and laju_proj_efektif < 0:
sisa_hari = int((pred_mm - BATAS_KRITIS) / abs(laju_proj_efektif))
else:
sisa_hari = 0
# --- PREDIKSI MODEL PROPHET ---
prophet_pred_mm = None
prophet_sisa_hari = None
df_forecast_prophet = None
if prophet_model is not None:
try:
# Menghitung periods dari tanggal pengukuran terakhir ke latest_date + 90 hari
forecast_periods = (latest_date - last_ukur_date).days + 90
future_df = prophet_model.make_future_dataframe(periods=forecast_periods, include_history=True)
df_forecast_prophet = prophet_model.predict(future_df)
# Filter untuk tanggal hari ini/terakhir
pred_today_row = df_forecast_prophet[df_forecast_prophet['ds'].dt.date == latest_date]
if not pred_today_row.empty:
prophet_pred_mm = max(float(pred_today_row['yhat'].values[0]), 100.0)
else:
prophet_pred_mm = 100.0
# Hitung sisa hari dari hari ini ke batas kritis
future_predictions = df_forecast_prophet[df_forecast_prophet['ds'].dt.date >= latest_date]
critical_prophet = future_predictions[future_predictions['yhat'] <= BATAS_KRITIS]
if not critical_prophet.empty:
earliest_crit = pd.to_datetime(critical_prophet['ds'].min()).date()
prophet_sisa_hari = max(0, (earliest_crit - latest_date).days)
else:
prophet_sisa_hari = 90
except Exception as pe:
st.warning(f"Gagal memproses prediksi Prophet: {pe}")
# Tentukan nilai prediksi aktif untuk dashboard
if not is_xgboost_selected and prophet_pred_mm is not None:
active_pred_mm = prophet_pred_mm
active_sisa_hari = prophet_sisa_hari
active_model_name = "Model 2"
else:
active_pred_mm = pred_mm
active_sisa_hari = sisa_hari
active_model_name = "Model 1"
lot_display = str(latest_row['lot_number']) if pd.notna(latest_row['lot_number']) else "-"
front_display = f"{latest_row['cone_front']:.0f}" if pd.notna(latest_row['cone_front']) else "-"
mid_display = f"{latest_row['raw_temp']:.0f}" if pd.notna(latest_row['raw_temp']) else "-"
back_display = f"{latest_row['cone_back']:.0f}" if pd.notna(latest_row['cone_back']) else "-"
# ============================================================
# SUB-NAV PILLS (Menggunakan st.tabs bawaan dengan CSS Custom)
# ============================================================
tab_dashboard, tab_cycles = st.tabs([
"Dashboard & Input Terpadu",
"Manajemen Siklus BTA"
])
# ============================================================
# TAB 1: DASHBOARD & INPUT TERPADU (Satu Halaman Flow)
# ============================================================
with tab_dashboard:
if not has_data:
st.info("Selamat datang di siklus baru! Belum ada data pemantauan harian. Silakan gunakan panel input di bawah untuk mengisi data perdana.")
# Form Input Perdana Langsung Tampil di bawah info selamat datang
col_manual, col_bulk = st.columns(2)
with col_manual:
with st.form("input_form_initial", clear_on_submit=False):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 16px; font-family: \'Bricolage Grotesque\'; color: #202020;">Entri Log Harian Perdana</div>', unsafe_allow_html=True)
tgl_skrg = st.date_input("Tanggal Log", datetime.date.today(), key="init_tgl")
lot_num = st.text_input("Lot Number", "RE 009", key="init_lot")
st.markdown('<div style="height: 8px;"></div>', unsafe_allow_html=True)
col_sub3, col_sub4, col_sub5 = st.columns(3)
with col_sub3:
temp_front = st.number_input("Suhu Cone Depan (°C)", 100.0, 600.0, 350.0, key="init_front")
with col_sub4:
temp_raw = st.number_input("Suhu Body Tengah (°C)", 100.0, 600.0, 350.0, key="init_mid")
with col_sub5:
temp_back = st.number_input("Suhu Cone Belakang (°C)", 100.0, 600.0, 350.0, key="init_back")
st.markdown('<div style="height: 8px;"></div>', unsafe_allow_html=True)
thick_val = st.number_input("Ketebalan BTA Aktual (mm) — Opsional", 0.0, 300.0, 0.0, key="init_thick")
submit_manual = st.form_submit_button("Simpan Data Perdana", type="primary")
if submit_manual:
thickness_actual = float(thick_val) if thick_val > 0 else None
db.execute("""
INSERT INTO daily_logs
(cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", (cycle_id, tgl_skrg.isoformat(), temp_raw, temp_front, temp_back, thickness_actual, lot_num))
if thickness_actual is not None:
db.execute("UPDATE cycles SET initial_thickness = ?, start_date = ? WHERE id = ?",
(thickness_actual, tgl_skrg.isoformat(), cycle_id))
db.commit()
st.success("Log perdana berhasil disimpan!")
st.rerun()
with col_bulk:
with st.container(border=True):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 8px; font-family: \'Bricolage Grotesque\'; color: #202020;">Bulk Import Data via CSV</div>', unsafe_allow_html=True)
st.markdown('<p style="font-size: 13px; color: var(--text-charcoal); margin-bottom: 16px;">Unggah file CSV dengan kolom utama: <b>Tanggal</b> dan <b>Bodi Tengah (°C)</b> untuk mengimpor seluruh data riwayat operasional secara massal.</p>', unsafe_allow_html=True)
uploaded_file = st.file_uploader("Pilih file CSV dari komputer Anda", type="csv", key="init_uploader")
if uploaded_file is not None:
try:
try:
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', skiprows=1)
except UnicodeDecodeError:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='latin1', skiprows=1)
if len(data_import.columns) == 1:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', sep=';', skiprows=1)
data_import.columns = data_import.columns.str.strip()
normalized_cols = {c.lower(): c for c in data_import.columns}
col_tanggal = normalized_cols.get('tanggal')
col_suhu = normalized_cols.get('bodi tengah (°c)') or normalized_cols.get('suhu')
col_front = normalized_cols.get('cone depan (°c)')
col_back = normalized_cols.get('cone belakang (°c)')
col_tebal = normalized_cols.get('ketebalan bta (mm)')
col_lot = normalized_cols.get('lot number')
if not col_tanggal or not col_suhu:
try:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig')
data_import.columns = data_import.columns.str.strip()
normalized_cols = {c.lower(): c for c in data_import.columns}
col_tanggal = normalized_cols.get('tanggal')
col_suhu = normalized_cols.get('bodi tengah (°c)') or normalized_cols.get('suhu')
col_front = normalized_cols.get('cone depan (°c)')
col_back = normalized_cols.get('cone belakang (°c)')
col_tebal = normalized_cols.get('ketebalan bta (mm)')
col_lot = normalized_cols.get('lot number')
except Exception:
pass
if not col_tanggal or not col_suhu:
st.error("Kolom 'Tanggal' and 'Bodi Tengah (°C)' wajib ada di dalam file CSV.")
else:
st.info("Pratinjau Data (Semua Kolom):")
st.dataframe(data_import.head(3), use_container_width=True)
if st.button("Konfirmasi & Simpan CSV", type="primary"):
count = 0
for index, row in data_import.dropna(subset=[col_tanggal, col_suhu]).iterrows():
log_date = str(row[col_tanggal]).strip()
try:
raw_temp = float(row[col_suhu])
except ValueError:
continue
cone_front = float(row[col_front]) if col_front and pd.notna(row[col_front]) else None
cone_back = float(row[col_back]) if col_back and pd.notna(row[col_back]) else None
thickness = float(row[col_tebal]) if col_tebal and pd.notna(row[col_tebal]) else None
lot_number = str(row[col_lot]).strip() if col_lot and pd.notna(row[col_lot]) else None
db.execute("""
INSERT INTO daily_logs
(cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", (cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number))
count += 1
db.commit()
st.success(f"Berhasil mengimpor {count} baris data historis secara bulk!")
st.rerun()
except Exception as e:
st.error(f"Gagal memproses file CSV: {e}")
else:
# Replicate Status Pill di bagian paling atas dashboard
st.markdown('<div class="rep-status-container">', unsafe_allow_html=True)
st.markdown(get_status_html(active_pred_mm, model_name=active_model_name), unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
# KPI Grid ala Replicate Model Cards
st.markdown(f"""
<div class="rep-kpi-container">
<div class="rep-kpi-card">
<div class="rep-kpi-label">Lot Number</div>
<div class="rep-kpi-value">{lot_display}</div>
</div>
<div class="rep-kpi-card">
<div class="rep-kpi-label">Suhu Cone Depan</div>
<div class="rep-kpi-value-mono">{front_display}<span class="rep-kpi-unit"> °C</span></div>
</div>
<div class="rep-kpi-card">
<div class="rep-kpi-label">Suhu Body Tengah</div>
<div class="rep-kpi-value-mono">{mid_display}<span class="rep-kpi-unit"> °C</span></div>
</div>
<div class="rep-kpi-card">
<div class="rep-kpi-label">Suhu Cone Belakang</div>
<div class="rep-kpi-value-mono">{back_display}<span class="rep-kpi-unit"> °C</span></div>
</div>
<div class="rep-kpi-card">
<div class="rep-kpi-label">Pengukuran Terakhir</div>
<div class="rep-kpi-value-mono">{last_ukur_thickness:.1f}<span class="rep-kpi-unit"> mm</span></div>
</div>
</div>
""", unsafe_allow_html=True)
# Ringkasan Parameter Prediktif & Status Operasional
c1, c2, c3 = st.columns(3)
c1.metric("Data Log Terakhir", latest_row['log_date'].strftime('%d %b %Y'))
c2.metric("Estimasi Ketebalan", f"{active_pred_mm:.1f} mm")
c3.metric("Estimasi Sisa Umur", f"{active_sisa_hari} Hari")
# --- PLOTLY CHART (Estetika Minimalis & Editorial Replicate) ---
st.markdown('<div class="rep-chart-well">', unsafe_allow_html=True)
fig = go.Figure()
# Stepwise Ketebalan Aktual
df_hist['thickness_step'] = df_hist['thickness'].ffill()
if pd.isna(df_hist.loc[0, 'thickness_step']):
df_hist.loc[0, 'thickness_step'] = float(initial_thickness)
df_hist['thickness_step'] = df_hist['thickness_step'].ffill()
fig.add_trace(go.Scatter(
x=df_hist['log_date'], y=df_hist['thickness_step'],
mode='lines', line=dict(color='#202020', width=3.5, shape='hv'),
name='Ketebalan Aktual (Langkah)',
hovertemplate='%{x|%d %b %Y}<br><b>Aktual: %{y:.1f} mm</b><extra></extra>'
))
# Titik Pengukuran Manual (dengan Tag Melayang & Panah Penunjuk Pointer)
if not df_ukur.empty:
# 1. Tambahkan titik bulat marker Pengukuran Manual (bersih tanpa teks bawaan)
fig.add_trace(go.Scatter(
x=df_ukur['log_date'], y=df_ukur['thickness'],
mode='markers',
marker=dict(color='#202020', size=9, line=dict(color='#f9f7f3', width=2), symbol='circle'),
name='Pengukuran Manual', showlegend=True,
hovertemplate='Pengukuran: %{x|%d %b %Y}<br><b>%{y:.1f} mm</b><extra></extra>'
))
# 2. Tambahkan Lencana Tag Melayang dengan Panah Pointer bergaya Zig-Zag Vertikal (Bebas Tabrakan)
step_interval = max(1, len(df_ukur) // 10)
for idx, row in df_ukur.reset_index(drop=True).iterrows():
# Tampilkan tag secara berkala, dan pastikan titik terakhir (terbaru) selalu tampil!
if idx % step_interval == 0 or idx == len(df_ukur) - 1:
val = float(row['thickness'])
date_val = row['log_date']
# Pola zig-zag vertikal & horizontal bergantian untuk menghindari tabrakan antar teks yang berdekatan
if idx % 2 == 0:
offset_y = -35 # Tinggi sedang
offset_x = -12 # Geser kiri sedikit
else:
offset_y = -70 # Sangat tinggi
offset_x = 12 # Geser kanan sedikit
fig.add_annotation(
x=date_val,
y=val,
text=f"<b>{val:.1f} mm</b>",
showarrow=True,
arrowhead=2,
arrowsize=1,
arrowwidth=1.2,
arrowcolor="rgba(32, 32, 32, 0.5)",
ax=offset_x,
ay=offset_y,
font=dict(size=9, color="#202020", family="Inter"),
bordercolor="rgba(32, 32, 32, 0.15)",
borderwidth=1,
borderpad=4,
bgcolor="#ffffff",
opacity=0.96
)
# Prediksi XGBoost Berkelanjutan
pred_values = []
current_anchor = float(initial_thickness)
anchor_date = start_date
for index, row in df_hist.iterrows():
row_date = row['log_date'].date()
if pd.notna(row['thickness']):
current_anchor = float(row['thickness'])
anchor_date = row_date
sub_df = df_hist.iloc[max(0, index-29):index+1]
avg_temp = sub_df['raw_temp'].mean() if not sub_df.empty else float(row['raw_temp'])
df_inp = pd.DataFrame([[avg_temp]], columns=['suhu_avg_periode'])
if model is not None:
l_pred = float(model.predict(df_inp)[0])
else:
l_pred = -0.1359
l_efektif = 0.4 * l_pred + 0.6 * laju_recent
h_sejak = (row_date - anchor_date).days
t_pred = current_anchor + (l_efektif * h_sejak)
pred_values.append(max(t_pred, 100.0))
df_hist['thickness_pred'] = pred_values
# Definisikan rentang waktu proyeksi masa depan (dipakai oleh grafik & label zona)
proj_hari = 90
proj_dates = [latest_date + datetime.timedelta(days=i) for i in range(0, proj_hari+1)]
if is_xgboost_selected:
# Hitung proyeksi masa depan XGBoost
proj_thickness = []
for i in range(0, proj_hari+1):
t_proj = last_ukur_thickness + (laju_proj_efektif * (hari_sejak_ukur + i))
proj_thickness.append(max(t_proj, 100.0))
# Plot model Wear-rate (XGBoost)
fig.add_trace(go.Scatter(
x=df_hist['log_date'], y=df_hist['thickness_pred'],
mode='lines', line=dict(color='#4A90D9', width=2),
name='Model Wear-rate (Model 1)',
hovertemplate='%{x|%d %b %Y}<br>Prediksi Model 1: %{y:.1f} mm<extra></extra>'
))
# Proyeksi Progresif Masa Depan XGBoost
fig.add_trace(go.Scatter(
x=proj_dates, y=proj_thickness,
mode='lines', line=dict(color='#2b9a66', width=2.5),
name=f'Proyeksi Operasi Model 1 ({laju_proj_efektif:.4f} mm/hari)',
hovertemplate='%{x|%d %b %Y}<br>Proyeksi Model 1: %{y:.1f} mm<extra></extra>',
fill='tozeroy',
fillcolor='rgba(43, 154, 102, 0.03)'
))
else:
# Plot model Prophet
if df_forecast_prophet is not None:
# Ambil data prophet bertepatan dengan rentang waktu
min_date = df_hist['log_date'].min().date()
mask_prophet = (df_forecast_prophet['ds'].dt.date >= min_date) & \
(df_forecast_prophet['ds'].dt.date <= latest_date + datetime.timedelta(days=90))
df_prophet_plot = df_forecast_prophet[mask_prophet]
# Riwayat Prophet (sampai Hari Ini)
df_prophet_past = df_prophet_plot[df_prophet_plot['ds'].dt.date <= latest_date]
fig.add_trace(go.Scatter(
x=df_prophet_past['ds'], y=df_prophet_past['yhat'],
mode='lines', line=dict(color='#ea2804', width=2),
name='Model 2 (History)',
hovertemplate='%{x|%d %b %Y}<br>Prediksi Model 2: %{y:.1f} mm<extra></extra>'
))
# Proyeksi Prophet (dari Hari Ini ke Depan)
df_prophet_future = df_prophet_plot[df_prophet_plot['ds'].dt.date >= latest_date]
fig.add_trace(go.Scatter(
x=df_prophet_future['ds'], y=df_prophet_future['yhat'],
mode='lines', line=dict(color='#2b9a66', width=2.5, dash='dash'),
name='Proyeksi Operasi Model 2',
hovertemplate='%{x|%d %b %Y}<br>Proyeksi Model 2: %{y:.1f} mm<extra></extra>',
fill='tozeroy',
fillcolor='rgba(43, 154, 102, 0.03)'
))
# Shaded uncertainty band
fig.add_trace(go.Scatter(
x=df_prophet_plot['ds'], y=df_prophet_plot['yhat_upper'],
mode='lines', line=dict(width=0), showlegend=False,
hoverinfo='skip'
))
fig.add_trace(go.Scatter(
x=df_prophet_plot['ds'], y=df_prophet_plot['yhat_lower'],
mode='lines', line=dict(width=0), showlegend=False,
fill='tonexty', fillcolor='rgba(234, 40, 4, 0.07)',
hoverinfo='skip'
))
# Penanda Garis Vertical "Hari Ini"
fig.add_vline(x=latest_date.strftime("%Y-%m-%d"), line_width=1.5, line_color="#202020", line_dash="dash")
fig.add_annotation(x=latest_date.strftime("%Y-%m-%d"), y=238,
text="<b>Hari Ini</b>", showarrow=False,
font=dict(color="#ffffff", size=10, family="Inter"),
bgcolor="#ea2804", bordercolor="#ea2804",
borderwidth=1, borderpad=5)
# Zone Labels
mid_past = df_hist['log_date'].iloc[len(df_hist)//2].strftime("%Y-%m-%d")
fig.add_annotation(x=mid_past, y=215, text="<b>ACTUAL</b>", showarrow=False,
font=dict(color="rgba(32,32,32,0.25)", size=20, family="Bricolage Grotesque"))
mid_future = proj_dates[proj_hari//2].strftime("%Y-%m-%d")
fig.add_annotation(x=mid_future, y=170, text="<b>PREDICTION</b>", showarrow=False,
font=dict(color="rgba(43, 154, 102, 0.18)", size=20, family="Bricolage Grotesque"))
# Threshold Limit Lines
fig.add_hline(y=115.0, line_width=1.5, line_color="#ea2804", line_dash="dot",
annotation_text="Kritis (115 mm)", annotation_position="bottom left",
annotation_font=dict(color="#ea2804", size=10))
fig.add_hline(y=130.0, line_width=1.2, line_color="#202020", line_dash="dash",
annotation_text="Peringatan (130 mm)", annotation_position="bottom left",
annotation_font=dict(color="#202020", size=10))
# Styling Chart Layout Modern
fig.update_layout(
xaxis=dict(
title=None,
gridcolor='rgba(32,32,32,0.04)',
zeroline=False,
showline=False,
tickfont=dict(color='#575757', size=11, family="Inter")
),
yaxis=dict(
title='Ketebalan Furnace Lining (mm)',
gridcolor='rgba(32,32,32,0.04)',
zeroline=False,
showline=False,
range=[90, 245],
tickfont=dict(color='#575757', size=11, family="Inter"),
title_font=dict(color='#575757', size=12, family="Inter")
),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1,
font=dict(size=11, color='#575757', family="Inter"),
bgcolor='rgba(0,0,0,0)'
),
hovermode="x unified",
hoverlabel=dict(
bgcolor="#202020",
bordercolor="rgba(255,255,255,0.1)",
font=dict(family="Inter", size=12, color="#ffffff")
),
margin=dict(l=50, r=20, t=20, b=40),
height=480
)
st.plotly_chart(fig, use_container_width=True)
st.markdown('</div>', unsafe_allow_html=True)
# Metadata Info / Stats
metode_badge = "Model 2" if not is_xgboost_selected else "Model 1"
st.markdown(f"""
<div style="display: flex; justify-content: space-between; font-size: 13px; color: var(--text-charcoal); margin-top: -12px; margin-bottom: 24px; padding: 0 12px;">
<span>Total data historis tercatat: <span class="rep-badge">{len(df_hist)} log harian</span></span>
<span>Metode Wear-rate: <span class="rep-badge">{metode_badge}</span></span>
</div>
""", unsafe_allow_html=True)
# --- EXCEL DATA VIEW PANEL (Membentang Penuh) ---
st.markdown('<div style="height: 8px;"></div>', unsafe_allow_html=True)
with st.container(border=True):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 6px; font-family: \'Bricolage Grotesque\'; color: #202020;">Tabel Data Terpasang (Excel View)</div>', unsafe_allow_html=True)
st.markdown('<p style="font-size: 13px; color: var(--text-charcoal); margin-bottom: 16px;">Tabel interaktif di bawah menampilkan seluruh baris data operasional aktif. Anda dapat melakukan pencarian, pengurutan, penyalinan data, atau mengunduhnya mirip dengan spreadsheet Excel.</p>', unsafe_allow_html=True)
# Kita siapkan DataFrame yang akan ditampilkan dengan penamaan kolom yang rapi
df_excel = df_hist.copy()
# Urutkan berdasarkan tanggal terbaru di atas agar user langsung melihat input teranyar!
df_excel = df_excel.sort_values('log_date', ascending=False).reset_index(drop=True)
# Format tanggal agar nyaman dibaca
df_excel['Tanggal'] = df_excel['log_date'].dt.strftime('%d %b %Y')
# Pilih dan susun kolom yang relevan
cols_to_show = []
rename_dict = {}
if 'Tanggal' in df_excel.columns:
cols_to_show.append('Tanggal')
rename_dict['Tanggal'] = 'Tanggal'
if 'lot_number' in df_excel.columns:
cols_to_show.append('lot_number')
rename_dict['lot_number'] = 'Lot Number'
if 'cone_front' in df_excel.columns:
cols_to_show.append('cone_front')
rename_dict['cone_front'] = 'Suhu Cone Depan (°C)'
if 'raw_temp' in df_excel.columns:
cols_to_show.append('raw_temp')
rename_dict['raw_temp'] = 'Suhu Body Tengah (°C)'
if 'cone_back' in df_excel.columns:
cols_to_show.append('cone_back')
rename_dict['cone_back'] = 'Suhu Cone Belakang (°C)'
if 'thickness' in df_excel.columns:
cols_to_show.append('thickness')
rename_dict['thickness'] = 'Tebal Aktual (mm)'
if is_xgboost_selected:
if 'thickness_pred' in df_excel.columns:
cols_to_show.append('thickness_pred')
rename_dict['thickness_pred'] = 'Estimasi Tebal (mm)'
else:
# Gabungkan prediksi Prophet ke Excel View jika ada
if df_forecast_prophet is not None:
df_forecast_prophet['log_date_dt'] = pd.to_datetime(df_forecast_prophet['ds']).dt.date
df_excel['log_date_dt'] = df_excel['log_date'].dt.date
df_excel = pd.merge(
df_excel,
df_forecast_prophet[['log_date_dt', 'yhat']].rename(columns={'yhat': 'thickness_prophet'}),
on='log_date_dt',
how='left'
).drop(columns=['log_date_dt'])
if 'thickness_prophet' in df_excel.columns:
cols_to_show.append('thickness_prophet')
rename_dict['thickness_prophet'] = 'Estimasi Tebal (mm)'
df_excel_subset = df_excel[cols_to_show].rename(columns=rename_dict)
# Gunakan st.dataframe dengan format numerik yang rapi agar seperti Excel
st.dataframe(
df_excel_subset,
use_container_width=True,
height=280,
column_config={
'Tebal Aktual (mm)': st.column_config.NumberColumn(format="%.1f mm"),
'Estimasi Tebal (mm)': st.column_config.NumberColumn(format="%.1f mm"),
'Suhu Cone Depan (°C)': st.column_config.NumberColumn(format="%.0f °C"),
'Suhu Body Tengah (°C)': st.column_config.NumberColumn(format="%.0f °C"),
'Suhu Cone Belakang (°C)': st.column_config.NumberColumn(format="%.0f °C"),
}
)
st.markdown('<div style="height: 24px;"></div>', unsafe_allow_html=True)
# ============================================================
# SUB-SECTION: INPUT DATA & FILE IMPORT (Terintegrasi Langsung!)
# ============================================================
st.markdown('<div class="tab-section-header">Pengumpulan Data Pemantauan</div>', unsafe_allow_html=True)
st.markdown('<p class="tab-section-desc">Masukkan parameter harian secara langsung untuk pembaruan instan, atau impor file CSV sekaligus untuk merekam data riwayat operasional secara massal.</p>', unsafe_allow_html=True)
# Grid Columns
col_manual, col_bulk = st.columns(2)
# Column 1: Input Manual
with col_manual:
with st.form("input_form_integrated", clear_on_submit=False):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 16px; font-family: \'Bricolage Grotesque\'; color: #202020;">Entri Log Harian Manual</div>', unsafe_allow_html=True)
col_sub1, col_sub2 = st.columns(2)
with col_sub1:
tgl_skrg = st.date_input("Tanggal Log", datetime.date.today(), key="int_tgl")
with col_sub2:
lot_num = st.text_input("Lot Number", "RE 009", key="int_lot")
st.markdown('<div style="height: 8px;"></div>', unsafe_allow_html=True)
col_sub3, col_sub4, col_sub5 = st.columns(3)
with col_sub3:
temp_front = st.number_input("Suhu Cone Depan (°C)", 100.0, 600.0, 350.0, key="int_front")
with col_sub4:
temp_raw = st.number_input("Suhu Body Tengah (°C)", 100.0, 600.0, 350.0, key="int_mid")
with col_sub5:
temp_back = st.number_input("Suhu Cone Belakang (°C)", 100.0, 600.0, 350.0, key="int_back")
st.markdown('<div style="height: 8px;"></div>', unsafe_allow_html=True)
thick_val = st.number_input("Ketebalan BTA Aktual (mm) — Opsional", 0.0, 300.0, 0.0, key="int_thick",
help="Hanya isi jika dilakukan pengukuran fisik manual pada tanggal bersangkutan.")
st.markdown('<div style="font-size: 12px; color: var(--text-mute); margin-bottom: 18px;">*Jika diisi, model akan menyetel titik ini sebagai dasar (anchor) baru untuk proyeksi selanjutnya.</div>', unsafe_allow_html=True)
submit_manual = st.form_submit_button("Simpan & Perbarui Grafik", type="primary")
if submit_manual:
thickness_actual = float(thick_val) if thick_val > 0 else None
db.execute("""
INSERT INTO daily_logs
(cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", (cycle_id, tgl_skrg.isoformat(), temp_raw, temp_front, temp_back, thickness_actual, lot_num))
if thickness_actual is not None:
db.execute("UPDATE cycles SET initial_thickness = ?, start_date = ? WHERE id = ?",
(thickness_actual, tgl_skrg.isoformat(), cycle_id))
db.commit()
st.success("Log harian berhasil disimpan!")
st.rerun()
# Column 2: Bulk Import CSV
with col_bulk:
with st.container(border=True):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 8px; font-family: \'Bricolage Grotesque\'; color: #202020;">Bulk Import Data via CSV</div>', unsafe_allow_html=True)
st.markdown('<p style="font-size: 13px; color: var(--text-charcoal); margin-bottom: 16px;">Unggah file CSV dengan kolom utama: <b>Tanggal</b> dan <b>Bodi Tengah (°C)</b>. Kolom pendukung seperti <i>Cone Depan (°C)</i>, <i>Cone Belakang (°C)</i>, <i>Ketebalan BTA (mm)</i>, dan <i>Lot Number</i> akan terdeteksi secara otomatis.</p>', unsafe_allow_html=True)
uploaded_file = st.file_uploader("Pilih file CSV dari komputer Anda", type="csv", key="int_uploader")
if uploaded_file is not None:
try:
try:
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', skiprows=1)
except UnicodeDecodeError:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='latin1', skiprows=1)
if len(data_import.columns) == 1:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', sep=';', skiprows=1)
data_import.columns = data_import.columns.str.strip()
normalized_cols = {c.lower(): c for c in data_import.columns}
col_tanggal = normalized_cols.get('tanggal')
col_suhu = normalized_cols.get('bodi tengah (°c)') or normalized_cols.get('suhu')
col_front = normalized_cols.get('cone depan (°c)')
col_back = normalized_cols.get('cone belakang (°c)')
col_tebal = normalized_cols.get('ketebalan bta (mm)')
col_lot = normalized_cols.get('lot number')
if not col_tanggal or not col_suhu:
try:
uploaded_file.seek(0)
data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig')
data_import.columns = data_import.columns.str.strip()
normalized_cols = {c.lower(): c for c in data_import.columns}
col_tanggal = normalized_cols.get('tanggal')
col_suhu = normalized_cols.get('bodi tengah (°c)') or normalized_cols.get('suhu')
col_front = normalized_cols.get('cone depan (°c)')
col_back = normalized_cols.get('cone belakang (°c)')
col_tebal = normalized_cols.get('ketebalan bta (mm)')
col_lot = normalized_cols.get('lot number')
except Exception:
pass
if not col_tanggal or not col_suhu:
st.error("Kolom 'Tanggal' dan 'Bodi Tengah (°C)' wajib ada di dalam file CSV.")
else:
st.info("Pratinjau Data (Semua Kolom):")
st.dataframe(data_import.head(3), use_container_width=True)
if st.button("Konfirmasi & Simpan CSV", type="primary", key="int_confirm_btn"):
count = 0
for index, row in data_import.dropna(subset=[col_tanggal, col_suhu]).iterrows():
log_date = str(row[col_tanggal]).strip()
try:
raw_temp = float(row[col_suhu])
except ValueError:
continue
cone_front = float(row[col_front]) if col_front and pd.notna(row[col_front]) else None
cone_back = float(row[col_back]) if col_back and pd.notna(row[col_back]) else None
thickness = float(row[col_tebal]) if col_tebal and pd.notna(row[col_tebal]) else None
lot_number = str(row[col_lot]).strip() if col_lot and pd.notna(row[col_lot]) else None
db.execute("""
INSERT INTO daily_logs
(cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", (cycle_id, log_date, raw_temp, cone_front, cone_back, thickness, lot_number))
count += 1
db.commit()
st.success(f"Berhasil mengimpor {count} baris data historis secara bulk!")
st.rerun()
except Exception as e:
st.error(f"Gagal memproses file CSV: {e}")
# ============================================================
# TAB 2: SIKLUS BTA & MANAJEMEN DATA (Administratif)
# ============================================================
with tab_cycles:
st.markdown('<div class="tab-section-header">Siklus BTA & Manajemen Data</div>', unsafe_allow_html=True)
st.markdown('<p class="tab-section-desc">Gunakan menu ini untuk menutup siklus operasi saat ini dan meluncurkan BTA furnace lining baru, atau melihat daftar siklus historis dan log data mentah.</p>', unsafe_allow_html=True)
col_c1, col_c2 = st.columns(2)
# Column 1: Reset / Pasang BTA baru
with col_c1:
with st.container(border=True):
st.markdown('<div style="font-size: 18px; font-weight: 700; color: var(--colors-primary); margin-bottom: 12px; font-family: \'Bricolage Grotesque\';">Penutupan & Siklus Baru</div>', unsafe_allow_html=True)
st.markdown(f'<p style="font-size: 13px; color: var(--text-charcoal); margin-bottom: 18px;">Saat ini Anda berada di dalam siklus aktif yang dimulai pada <b>{start_date_str}</b> dengan ketebalan awal BTA sebesar <b>{initial_thickness:.1f} mm</b>.</p>', unsafe_allow_html=True)
st.markdown('<p style="font-size: 13px; color: var(--text-charcoal); margin-bottom: 24px;">Menutup siklus aktif akan mengarsipkan seluruh riwayat log harian saat ini, dan membuka form registrasi untuk mendata lining furnace BTA baru.</p>', unsafe_allow_html=True)
if st.button("Akhiri Siklus & Pasang BTA Baru"):
db.execute("UPDATE cycles SET active = 0 WHERE active = 1")
db.commit()
st.success("Siklus aktif berhasil diarsipkan.")
st.rerun()
# Column 2: Log Tabel Mentah Database
with col_c2:
with st.container(border=True):
st.markdown('<div style="font-size: 18px; font-weight: 700; margin-bottom: 12px; font-family: \'Bricolage Grotesque\';">Riwayat Siklus & Log Mentah</div>', unsafe_allow_html=True)
# Tampilkan list siklus
df_cycles = pd.read_sql_query("SELECT id, start_date, initial_thickness, active FROM cycles ORDER BY id DESC", db)
if not df_cycles.empty:
st.markdown('<div style="font-size: 13px; font-weight: 600; margin-bottom: 6px;">Daftar Siklus Tersimpan:</div>', unsafe_allow_html=True)
df_cycles_styled = df_cycles.copy()
df_cycles_styled['active'] = df_cycles_styled['active'].apply(lambda x: "AKTIF" if x == 1 else "ARSIP")
st.dataframe(df_cycles_styled, use_container_width=True, height=140)
# View data log mentah
if has_data:
st.markdown('<div style="height: 12px;"></div>', unsafe_allow_html=True)
with st.expander("Lihat Log Tabel Mentah Harian (10 Baris Terakhir)"):
st.dataframe(df_hist.tail(10), use_container_width=True)
# ============================================================
# FOOTER (Minimalist Editorial ala Replicate)
# ============================================================
st.markdown("""
<div class="rep-footer">
BTA Smart Monitor © 2026
</div>
""", unsafe_allow_html=True) |