app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
import requests
import json
import numpy as np
import math
import base64

# =================== CONFIG =====================
st.set_page_config(
    page_title="Advanced Fatigue Anfalytics",
    page_icon="🛡️",  # Safety icon
    layout="wide",
    initial_sidebar_state="expanded"
)

# =================== LOGO =====================
logo_path = "btech.png"  # File logo
def get_base64(file_path):
    with open(file_path, "rb") as f:
        data = f.read()
    return base64.b64encode(data).decode()

try:
    logo_base64 = get_base64(logo_path)
    logo_html = f'<img src="data:image/png;base64,{logo_base64}" style="max-height: 80px; max-width: 120px;">'
except FileNotFoundError:
    st.warning(f"Logo file '{logo_path}' not found. Using placeholder text.")
    logo_html = '<div style="font-size: 18px; font-weight: bold; color: #2c3e50;">BTECH</div>'

# # =================== GLOBAL CSS =====================
# st.markdown("""
# <style>
# body {
#     background-color: #f6f8fa;
# }

# /* ===== HEADER WRAPPER ===== */
# .header-container {
#     display: flex;
#     justify-content: space-between;
#     align-items: center;
#     padding: 25px 35px;
#     background: white; /* Latar belakang utama diubah menjadi putih */
#     border-radius: 0 0 14px 14px; /* Rounded bottom only */
#     box-shadow: 0 5px 18px rgba(0,0,0,0.15); /* Bayangan lebih lembut */
#     border: 1px solid #e0e0e0; /* Border tipis untuk definisi */
#     margin-bottom: 25px;
#     position: relative;
#     overflow: hidden; /* Ensure rounded corners clip content */
# }

# /* Optional: Subtle pattern or texture overlay (optional, can be removed) */
# /* .header-container::before {
#     content: "";
#     position: absolute;
#     top: 0;
#     left: 0;
#     right: 0;
#     bottom: 0;
#     background: linear-gradient(45deg, rgba(255,255,255,0.03) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.03) 50%, rgba(255,255,255,0.03) 75%, transparent 75%, transparent);
#     background-size: 20px 20px;
#     pointer-events: none;
# } */

# /* ===== HEADER TEXT ===== */
# .header-title {
#     color: #2c3e50; /* Teks header diubah agar kontras dengan latar putih */
#     font-family: 'Segoe UI', sans-serif;
#     flex-grow: 1; /* Allow text to take up available space */
#     margin-right: 20px; /* Space between text and logo */
#     text-align: left;
# }

# .header-title h1 {
#     font-size: 2.7em;
#     font-weight: 650;
#     margin: 0;
#     text-shadow: 1px 1px 2px rgba(0,0,0,0.1); /* Bayangan teks lebih lembut */
# }

# .header-title p {
#     font-size: 1.25em;
#     opacity: 0.85; /* Sedikit transparan untuk subjudul */
#     margin-top: 6px;
#     font-style: italic;
#     color: #34495e; /* Warna subjudul disesuaikan */
# }

# /* ===== LOGO WRAPPER ===== */
# .header-logo {
#     display: flex;
#     align-items: center;
#     justify-content: flex-end; /* Align logo to the right within its container */
#     flex-shrink: 0; /* Prevent logo container from shrinking */
# }

# /* ===== LOGO STYLE ===== */
# .header-logo img {
#     border-radius: 10px;
#     border: 2px solid rgba(44, 62, 80, 0.15); /* Border logo disesuaikan */
#     box-shadow: 0 3px 10px rgba(0,0,0,0.1); /* Bayangan logo lebih lembut */
#     max-height: 80px; /* Set max height */
#     max-width: 120px; /* Set max width */
# }

# /* ===== METRIC CARDS ===== */
# .metric-card {
#     background: #ffffff;
#     padding: 18px 22px;
#     border-radius: 12px;
#     border-left: 6px solid #1e3c72;
#     box-shadow: 0 3px 8px rgba(0,0,0,0.10);
#     transition: 0.25s ease-in-out;
# }
# .metric-card:hover {
#     transform: translateY(-4px);
#     box-shadow: 0 6px 15px rgba(0,0,0,0.18);
# }

# /* ===== INSIGHT BOX ===== */
# .insight-box {
#     background: #fafafa;
#     padding: 18px;
#     border-radius: 12px;
#     border-left: 6px solid #ff6b6b;
#     margin: 15px 0;
#     box-shadow: 0 2px 6px rgba(0,0,0,0.08);
# }

# /* ===== RISK MATRIX ===== */
# .risk-matrix {
#     border-collapse: collapse;
#     width: 100%;
#     margin: 20px 0;
# }
# .risk-matrix th, .risk-matrix td {
#     border: 1px solid #ddd;
#     padding: 12px;
#     text-align: center;
# }
# .risk-matrix th {
#     background-color: #f2f2f2;
# }
# .critical { background-color: #ffcccc; font-weight: bold; }
# .high { background-color: #ffebcc; }
# .medium { background-color: #ffffcc; }
# .low { background-color: #e6ffe6; }

# /* ===== CHAT UI ===== */
# .chat-container {
#     background: white;
#     padding: 20px;
#     border-radius: 12px;
#     height: 400px;
#     overflow-y: auto;
#     border: 1px solid #ccc;
# }
# .user-message {
#     background: #e3f2fd;
#     color: black;
#     padding: 12px;
#     border-radius: 12px;
#     margin: 10px 0;
#     text-align: right;
#     border: 1px solid #bbdefb;
# }
# .ai-message {
#     background: #f5f5f5;
#     color: black;
#     padding: 12px;
#     border-radius: 12px;
#     margin: 10px 0;
#     text-align: left;
#     border: 1px solid #e0e0e0;
# }

# /* ===== INPUT BOX ===== */
# .chat-box, .user-question, .ai-answer {
#     background: white;
#     border: 1px solid #ccc;
#     border-radius: 10px;
#     padding: 12px;
#     margin-bottom: 12px;
# }

# /* ===== FOOTER ===== */
# .footer {
#     text-align: center;
#     padding: 20px;
#     color: gray;
#     font-size: 0.9em;
# }

# /* ===== HOVER EFFECTS ===== */
# .metric-card:hover, .insight-box:hover {
#     box-shadow: 0 6px 15px rgba(0,0,0,0.2);
#     transition: all 0.3s ease-in-out;
# }

# </style>
# """, unsafe_allow_html=True)

# # =================== HEADER =====================
# st.markdown(f"""
# <div class="header-container">
#     <div class="header-title">
#         <h1>Advanced Fatigue Analysis</h1>
#         <p>Proactive Safety Intelligence for Mining Operations</p>
#     </div>
#     <div class="header-logo">
#         {logo_html}
#     </div>
# </div>
# """, unsafe_allow_html=True)

# =================== GLOBAL CSS =====================
st.markdown("""
<style>
body {
    background-color: #f6f8fa;
}

/* ===== HEADER WRAPPER ===== */
.header-container {
    display: flex;
    justify-content: space-between; /* Logo di kanan, teks di tengah */
    align-items: center;
    padding: 25px 35px;
    background: white;
    border-radius: 0 0 14px 14px;
    box-shadow: 0 5px 18px rgba(0,0,0,0.15);
    border: 1px solid #e0e0e0;
    margin-bottom: 25px;
    position: relative;
}

/* ===== HEADER TEXT ===== */
.header-title {
    flex: 1; /* Mengambil ruang sebanyak mungkin */
    display: flex;
    flex-direction: column;
    align-items: center; /* Center horizontal */
    justify-content: center; /* Center vertical */
    text-align: center;
}

.header-title h1 {
    font-size: 2.7em;
    font-weight: 650;
    margin: 0;
    text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
    color: #2c3e50;
}

.header-title p {
    font-size: 1.25em;
    opacity: 0.85;
    margin-top: 6px;
    font-style: italic;
    color: #34495e;
}

/* ===== LOGO WRAPPER ===== */
.header-logo {
    display: flex;
    align-items: center;
    justify-content: flex-end;
    flex-shrink: 0;
}

.header-logo img {
    border-radius: 10px;
    border: 2px solid rgba(44, 62, 80, 0.15);
    box-shadow: 0 3px 10px rgba(0,0,0,0.1);
    max-height: 80px;
    max-width: 120px;
}

/* Metric cards, insight box, dll. tetap sama... */
</style>
""", unsafe_allow_html=True)

# =================== HEADER =====================
st.markdown(f"""
<div class="header-container">
    <div class="header-title">
        <h1>Advanced Fatigue Analysis</h1>
        <p>Proactive Safety Intelligence for Mining Operations</p>
    </div>
    <div class="header-logo">
        {logo_html}  <!-- Logo tetap di kanan -->
    </div>
</div>
""", unsafe_allow_html=True)

# # ... (Kode selanjutnya disalin dari bagian bawah file Anda, misalnya LOAD DATA ke bawah)
# # =================== LOAD DATA ======================
@st.cache_data
def load_data():
    try:
        # ==================================
        # 1. LOAD CSV & NORMALIZE COLUMNS
        # ==================================
        df = pd.read_csv("data.csv")
        original_columns = df.columns.tolist()
        # Normalize: lower, strip, underscore
        df.columns = (
            df.columns.astype(str)
            .str.strip()
            .str.lower()
            .str.replace(r"\s+", "_", regex=True)
        )

        # ==================================
        # 2. AUTO-DETECT COLUMNS (case-insensitive)
        # ==================================
        col_operator = next((c for c in df.columns if "operator" in c or "driver" in c), None)
        col_shift = next((c for c in df.columns if "shift" in c), None)
        # ✅ FIX: Search for normalized "parent_fleet", NOT original "Parent Fleet"
        col_fleet_type = next((c for c in df.columns if "parent_fleet" in c), None)
        col_fleet_no = next((c for c in df.columns if "fleet_number" in c), None)

        # ==================================
        # 3. DERIVE COLUMNS
        # ==================================
        # Unit Number
        if col_fleet_no:
            df["unit_no"] = df[col_fleet_no].astype(str).str.split("-", n=1).str[-1].str.strip()
        else:
            df["unit_no"] = "UNKNOWN"

        # Speed
        col_speed = None
        for orig in original_columns:
            norm = orig.lower().replace(" ", "_")
            if "(in_km/hour).1" in norm or "speed" in norm:
                if norm in df.columns:
                    col_speed = norm
                    break
        if not col_speed:
            col_speed = next((c for c in df.columns if "speed" in c), None)

        # Time
        time_cols = [c for c in df.columns if "gmt" in c and "wita" in c]
        if len(time_cols) >= 2:
            df["start"] = pd.to_datetime(df[time_cols[0]], errors="coerce")
            df["end"] = pd.to_datetime(df[time_cols[1]], errors="coerce")
        elif len(time_cols) == 1:
            df["start"] = pd.to_datetime(df[time_cols[0]], errors="coerce")
            df["end"] = df["start"] + pd.Timedelta(minutes=1)
        else:
            df["start"] = pd.NaT
            df["end"] = pd.NaT

        # Time features
        if not df["start"].isna().all():
            df["hour"] = df["start"].dt.hour
            df["date"] = df["start"].dt.date
            df["day_of_week"] = df["start"].dt.day_name()
            # df["week"], df["month"], df["year"] — optional, not used in filters
        else:
            df["hour"] = 0
            df["date"] = None

        # Shift as int
        if col_shift:
            df[col_shift] = pd.to_numeric(df[col_shift], errors="coerce").astype("Int64")

        # ✅ FIX: CREATE site & group_model HERE (not in sidebar!)
        if col_fleet_type:
            # Split ONCE on first '-', keep FULL left part (e.g., "Amsterdam - CAT789" → "AMSTERDAM")
            split = df[col_fleet_type].astype(str).str.split("-", n=1, expand=True)
            df["site"] = split[0].str.strip().str.upper()
            df["group_model"] = split[1].str.strip().fillna("UNKNOWN").replace("", "UNKNOWN")
        else:
            df["site"] = "UNKNOWN"
            df["group_model"] = "UNKNOWN"

        return df, col_operator, col_shift, col_fleet_type, col_speed, col_fleet_no

    except Exception as e:
        st.error(f"Error loading data: {e}")
        return pd.DataFrame(), None, None, None, None, None

# ==================================
# CALL load_data()
# ==================================
df, col_operator, col_shift, col_fleet_type, col_speed, col_fleet_no = load_data()
df_original_full = df.copy()
if df.empty:
    st.stop()
st.success("Data Loaded Successfully")
df_full_report = df.copy()

# =================== FILTERS (Sidebar) =====================
filter_dict = {}

st.sidebar.markdown(
    """
    <div style="
        font-family: 'Segoe UI', sans-serif;
        font-size: 1.35em;
        font-weight: 600;
        color: #2c3e50;
        padding: 10px 0 14px 0;
        text-align: center;
        border-bottom: 2px solid #3498db;
        margin-bottom: 16px;
    ">
        Filter if Need Specific Conditions
    </div>
    """,
    unsafe_allow_html=True
)


with st.sidebar.form("filters_form"):
    # ---------------- Date Range ----------------
    if 'date' in df.columns and not df['date'].isna().all():
        min_date = pd.to_datetime(df['date']).min().date()
        max_date = pd.to_datetime(df['date']).max().date()
        date_range = st.date_input("Select Date Range", (min_date, max_date))
        filter_dict['date_range'] = date_range
    else:
        filter_dict['date_range'] = (None, None)

    # ✅ FIXED: Use df['site'] & df['group_model'] (already created in load_data)
    # ---------------- Site Filter ----------------
    all_sites = sorted(df['site'].dropna().unique())
    selected_site = st.selectbox(
        "Filter Site",
        options=[None] + all_sites,
        format_func=lambda x: "All" if x is None else x
    )
    filter_dict['site'] = selected_site
    # ---------------- Group Model Filter ✅ NOW WORKING ----------------
    all_models = sorted(df['group_model'].dropna().unique())

    # Define display names for specific values
    display_map = {
        "OB HAULLER": "OB HAULER",
        "HAULING COAL": "COAL HAULING"
    }

    # Create display options
    display_options = [display_map.get(model, model) for model in all_models]

    # Create reverse map to get original value back
    reverse_map = {v: k for k, v in display_map.items()}

    # Create selectbox with display names
    selected_display = st.selectbox(
        "Filter Group Model",
        options=[None] + display_options,
        format_func=lambda x: "All" if x is None else x
    )

    # Map back to original value for filtering
    selected_model = reverse_map.get(selected_display, selected_display) if selected_display else None
    filter_dict['group_model'] = selected_model

    # ---------------- Shift ----------------
    if col_shift:
        shifts = sorted(df[col_shift].dropna().unique())
        selected_shift = st.selectbox(
            f"Select {col_shift.replace('_', ' ').title()}",
            options=[None] + shifts,
            format_func=lambda x: "All" if x is None else f"Shift {x}"
        )
        filter_dict['shift'] = selected_shift
    else:
        filter_dict['shift'] = None

    # ---------------- Operator ----------------
    if col_operator:
        ops = sorted(df[col_operator].dropna().unique())
        selected_op = st.selectbox(
            f"Select {col_operator.replace('_', ' ').title()}",
            options=[None] + ops,
            format_func=lambda x: "All" if x is None else x
        )
        filter_dict['operator'] = selected_op
    else:
        filter_dict['operator'] = None

    # ---------------- Hour ----------------
    if 'hour' in df.columns and not df['hour'].isna().all():
        hours = sorted(df['hour'].dropna().unique())
        hour_range = st.slider("Select Hour Range", int(min(hours)), int(max(hours)), (int(min(hours)), int(max(hours))))
        filter_dict['hour_range'] = hour_range
    else:
        filter_dict['hour_range'] = (0, 23)

    # ---------------- Unit No ----------------
    if 'unit_no' in df.columns:
        units = sorted(df['unit_no'].dropna().unique())
        selected_unit = st.selectbox("Select Unit Number", [None] + units, format_func=lambda x: "All" if x is None else x)
        filter_dict['unit_no'] = selected_unit
    else:
        filter_dict['unit_no'] = None

    # ---------------- Submit ----------------
    apply_filters = st.form_submit_button("Apply Filters")
# =================== APPLY FILTERS =====================
if apply_filters:
    # Filter Date Range
    if filter_dict.get('date_range'):
        start_date, end_date = filter_dict['date_range']
        df = df[(df['date'] >= start_date) & (df['date'] <= end_date)]

    # Filter Site
    if filter_dict.get('site') is not None:
        df = df[df['site'] == filter_dict['site']]

    # Filter Group Model
    if filter_dict.get('group_model') is not None:
        df = df[df['group_model'] == filter_dict['group_model']]
    
    # UI display mapping (for rendering only — data remains unchanged)
    group_model_display = {
        'OB HAULLER': 'OB HAULER',
        'HAULING COAL': 'COAL HAULING'
    }

    # Filter Shift
    if filter_dict.get('shift') is not None:
        df = df[df[col_shift] == filter_dict['shift']]

    # Filter Operator
    if filter_dict.get('operator') is not None:
        df = df[df[col_operator] == filter_dict['operator']]

    # Filter Hour Range
    if filter_dict.get('hour_range'):
        hr_start, hr_end = filter_dict['hour_range']
        df = df[(df['hour'] >= hr_start) & (df['hour'] <= hr_end)]

    # Filter Unit No
    if filter_dict.get('unit_no') is not None:
        df = df[df[col_fleet_no] == filter_dict['unit_no']]


# Sisanya dari kode Anda (Visualisasi, dll.) tetap sama
# Objective 1
# ===================== GLOBAL FUNCTION: Hour Category Labels =====================
def hour_range_label_full(hour):
    if not (0 <= hour < 24):
        return 'Unknown'
    if 6 <= hour < 9:
        return 'Shift 1 Morning Early (6-9)'
    elif 9 <= hour < 12:
        return 'Shift 1 Morning Late (9-12)'
    elif 12 <= hour < 15:
        return 'Shift 1 Afternoon Early (12-15)'
    elif 15 <= hour < 18:
        return 'Shift 1 Afternoon Late (15-18)'
    elif 18 <= hour < 21:
        return 'Shift 2 Evening Early (18-21)'
    elif 21 <= hour < 24:
        return 'Shift 2 Evening Late (21-24)'
    elif 0 <= hour < 3:
        return 'Shift 2 Dawn Early (0-3)'
    elif 3 <= hour < 6:
        return 'Shift 2 Dawn Late (3-6)'
    return 'Unknown'

# ===================== MAIN VISUALIZATION =====================
st.subheader("OBJECTIVE 1: Want to see fatigue patterns across different shifts?")
if 'start' in df.columns and not df.empty:
    try:
        # --- Data Preparation ---
        df_local = df.copy()
        if not pd.api.types.is_datetime64_any_dtype(df_local['start']):
            df_local['start'] = pd.to_datetime(df_local['start'], errors='coerce')
        df_local = df_local.dropna(subset=['start'])
        df_local['hour'] = df_local['start'].dt.hour
        # --- COLOR MAP: KUNING-ORANGE (Shift 1), BIRU (Shift 2) ---
        color_map_full = {
            'Shift 1 Morning Early (6-9)':      '#FFEB3B',  # Yellow 300
            'Shift 1 Morning Late (9-12)':      '#FFC107',  # Amber 300
            'Shift 1 Afternoon Early (12-15)':  '#FF9800',  # Orange 300
            'Shift 1 Afternoon Late (15-18)':   '#F57C00',  # Deep Orange 300
            'Shift 2 Evening Early (18-21)':    '#42A5F5',  # Light Blue 300
            'Shift 2 Evening Late (21-24)':     '#1976D2',  # Blue 300
            'Shift 2 Dawn Early (0-3)':         '#0288D1',  # Cyan 300
            'Shift 2 Dawn Late (3-6)':          '#01579B',  # Blue 800
        }
        # --- Define intervals in analog-clock order (12→3→6→9) ---
        intervals_shift1 = [(12, 15), (15, 18), (6, 9), (9, 12)]
        labels_shift1 = [
            'Shift 1 Afternoon Early (12-15)',
            'Shift 1 Afternoon Late (15-18)',
            'Shift 1 Morning Early (6-9)',
            'Shift 1 Morning Late (9-12)',
        ]
        intervals_shift2 = [(0, 3), (3, 6), (18, 21), (21, 24)]
        labels_shift2 = [
            'Shift 2 Dawn Early (0-3)',
            'Shift 2 Dawn Late (3-6)',
            'Shift 2 Evening Early (18-21)',
            'Shift 2 Evening Late (21-24)',
        ]
        # --- Compute frequencies ---
        def compute_counts(intervals):
            counts = []
            for start_h, end_h in intervals:
                cnt = df_local[(df_local['hour'] >= start_h) & (df_local['hour'] < end_h)].shape[0]
                counts.append(cnt)
            return counts
        freq_shift1 = compute_counts(intervals_shift1)
        freq_shift2 = compute_counts(intervals_shift2)
        # --- Polar geometry ---
        theta_midpoints = [45, 135, 225, 315]  # centers of 90° segments
        bar_width = [90] * 4
        angular_tick_vals = [0, 90, 180, 270]  # fixed angle positions
        # ✅ CUSTOM TICK LABELS PER SHIFT (sesuai permintaan Anda)
        angular_tick_text_shift1 = ["12", "15", "6/18", "9"]   # 0°, 90°, 180°, 270°
        angular_tick_text_shift2 = ["24", "3", "18/6", "21"]   # 0°=24, 90°=3, 180°=6, 270°=21
        # --- Independent radial scales ---
        max_r1 = max(freq_shift1) if freq_shift1 and max(freq_shift1) > 0 else 1
        max_r2 = max(freq_shift2) if freq_shift2 and max(freq_shift2) > 0 else 1
        # ============== FIGURE: SHIFT 1 (KUNING-ORANGE) ==============
        fig1 = go.Figure()
        fig1.add_trace(go.Barpolar(
            r=freq_shift1,
            theta=theta_midpoints,
            width=bar_width,
            marker_color=[color_map_full.get(lbl, '#FFEB3B') for lbl in labels_shift1],
            marker_line_color="black",
            marker_line_width=1.5,
            opacity=0.93,
            hovertemplate="<b>%{text}</b><br>Fatigue Incidents: %{r}<extra></extra>",
            text=labels_shift1,
        ))
        fig1.update_layout(
            title=dict(text="Shift 1 (06:00–18:00)", font=dict(size=18, color="#FF9800", family="Segoe UI")),
            polar=dict(
                bgcolor="rgba(255,248,225,0.7)",
                angularaxis=dict(
                    rotation=90,  # 12 at top
                    direction="clockwise",
                    tickmode='array',
                    tickvals=angular_tick_vals,
                    ticktext=angular_tick_text_shift1,  # ✅ 12, 15, 6, 9
                    tickfont=dict(size=14, color="#5D4037", weight="bold"),
                    showline=True,
                    linewidth=1.2,
                    linecolor="#FFD54F",
                ),
                radialaxis=dict(
                    visible=True,
                    showticklabels=True,
                    tickfont=dict(size=11),
                    angle=45,
                    gridcolor="#FFE082",
                    gridwidth=0.8,
                    range=[0, max_r1 * 1.15],
                )
            ),
            showlegend=False,
            height=550,
            width=550,
            margin=dict(t=65, b=40, l=40, r=40),
            font=dict(family="Segoe UI, -apple-system, sans-serif"),
        )
        # ============== FIGURE: SHIFT 2 (BIRU) ==============
        fig2 = go.Figure()
        fig2.add_trace(go.Barpolar(
            r=freq_shift2,
            theta=theta_midpoints,
            width=bar_width,
            marker_color=[color_map_full.get(lbl, '#42A5F5') for lbl in labels_shift2],
            marker_line_color="black",
            marker_line_width=1.5,
            opacity=0.93,
            hovertemplate="<b>%{text}</b><br>Fatigue Incidents: %{r}<extra></extra>",
            text=labels_shift2,
        ))
        fig2.update_layout(
            title=dict(text="Shift 2 (18:00–06:00)", font=dict(size=18, color="#1976D2", family="Segoe UI")),
            polar=dict(
                bgcolor="rgba(230,245,255,0.7)",
                angularaxis=dict(
                    rotation=90,
                    direction="clockwise",
                    tickmode='array',
                    tickvals=angular_tick_vals,
                    ticktext=angular_tick_text_shift2,  # ✅ 24, 3, 6, 21
                    tickfont=dict(size=14, color="#0D47A1", weight="bold"),
                    showline=True,
                    linewidth=1.2,
                    linecolor="#64B5F6",
                ),
                radialaxis=dict(
                    visible=True,
                    showticklabels=True,
                    tickfont=dict(size=11),
                    angle=45,
                    gridcolor="#BBDEFB",
                    gridwidth=0.8,
                    range=[0, max_r2 * 1.15],  # ✅ SKALA INDEPENDEN
                )
            ),
            showlegend=False,
            height=550,
            width=550,
            margin=dict(t=65, b=40, l=40, r=40),
            font=dict(family="Segoe UI, -apple-system, sans-serif"),
        )
        # ============== EXPLANATION — URUTAN KRONOLOGIS, REMARK TETAP ==============
        st.markdown("""
        <div style="
            background: linear-gradient(135deg, #FFFDE7 0%, #E3F2FD 100%);
            padding: 20px;
            border-radius: 12px;
            border-left: 5px solid #FF9800;
            margin: 22px 0;
            box-shadow: 0 3px 10px rgba(0,0,0,0.06);
        ">
        <h4 style="color:#1976D2; margin:0 0 14px 0; display:flex; align-items:center;">
            <span style="background:#FF9800; color:white; width:26px; height:26px; border-radius:50%; 
                        display:inline-flex; align-items:center; justify-content:center; margin-right:10px; font-weight:bold;">!</span>
            ⚠️ Clockwise Time Mapping (Analog Layout)
        </h4>
        <table style="width:100%; font-size:14px; border-collapse:collapse; color:#424242;">
          <tr style="background-color:#FFF8E1;">
            <th style="padding:8px; text-align:left; width:25%;">Time Block</th>
            <th style="padding:8px; text-align:left;">Shift 1 (Day)</th>
            <th style="padding:8px; text-align:left;">Shift 2 (Night)</th>
          </tr>
          <tr>
            <td style="padding:8px; font-weight:bold;">1st Block</td>
            <td><b>06 → 09</b></td>
            <td><b>18 → 21</b></td>
          </tr>
          <tr style="background-color:#F5F9FF;">
            <td style="padding:8px; font-weight:bold;">2nd Block</td>
            <td><b>09 → 12</b></td>
            <td><b>21 → 24</b> (Alertness Decline)</td>
          </tr>
          <tr>
            <td style="padding:8px; font-weight:bold;">3rd Block</td>
            <td><b>12 → 15</b></td>
            <td><b>24 → 03</b> (Circadian Nadir)</td>
          </tr>
          <tr style="background-color:#F5F9FF;">
            <td style="padding:8px; font-weight:bold;">4th Block</td>
            <td><b>15 → 18</b></td>
            <td><b>03 → 06</b></td>
          </tr>
        </table>
        <p style="margin-top:12px; font-size:13px; color:#546E7A;">
            <b>Scale is independent per shift</b> — bar length shows relative risk <i>within</i> the shift.
        </p>
        </div>
        """, unsafe_allow_html=True)
        # ============== RENDER CHARTS HORIZONTALLY (NO OVERLAP) ==============
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(fig1, use_container_width=True, config={'displayModeBar': False})
        with col2:
            st.plotly_chart(fig2, use_container_width=True, config={'displayModeBar': False})
        # ============== FOOTNOTE (SEMINAR-READY) ==============
        st.caption(
            " *Safety Insight*: Highest fatigue risk occurs during **24→06** (Shift 2) — aligns with circadian trough (Czeisler, 1999). "
        )
    except Exception as e:
        st.error(f"⚠️ Rendering error: {e}")
        st.code(f"{type(e).__name__}: {str(e)}", language="python")
else:
    st.info("⏳ Awaiting data... Ensure column `'start'` contains valid timestamps (e.g., '2025-06-15 14:30:00').")

#Objective 
#
#Objective 1
st.subheader("OBJECTIVE 2: How does operator energy fluctuate from start to finish of each shift?")
if 'start' in df.columns and not df.empty:
    try:
        df_local = df.copy()
        df_local['hour'] = df_local['start'].dt.hour
        df_local['date'] = df_local['start'].dt.normalize()
        # Kategorisasi jam menggunakan fungsi global
        df_local['hour_category'] = df_local['hour'].apply(hour_range_label_full)
        color_map = {
            'Shift 1 Morning Early (6-9)':  '#FFEB3B',
            'Shift 1 Morning Late (9-12)':   '#FFC107',
            'Shift 1 Afternoon Early (12-15)':'#FF9800',
            'Shift 1 Afternoon Late (15-18)': '#F57C00',
            'Shift 2 Evening Early (18-21)':  '#42A5F5',
            'Shift 2 Evening Late (21-24)':   '#1976D2',
            'Shift 2 Dawn Early (0-3)':     '#0288D1',
            'Shift 2 Dawn Late (3-6)':      '#01579B',
            'Unknown': '#E0E0E0'
        }
        # Hitung jumlah fatigue per hari dan kategori jam
        daily_by_cat = df_local.groupby(['date', 'hour_category']).size().reset_index(name='fatigue_count')
        # --- TAMBAHAN: Ambil dominant hour_category per hari untuk Objective 3 ---
        # Kita gunakan data df_local yang sudah memiliki hour_category
        daily_dominant_cat = df_local.groupby('date')['hour_category'].agg(
            lambda x: x.value_counts().idxmax()
        ).reset_index()
        daily_dominant_cat.rename(columns={'hour_category': 'dominant_hour_category'}, inplace=True)
        # --- END TAMBAHAN ---
        all_dates = pd.date_range(start=daily_by_cat['date'].min(), end=daily_by_cat['date'].max(), freq='D')
        all_cats = list(color_map.keys())
        full_index = pd.MultiIndex.from_product([all_dates, all_cats], names=['date', 'hour_category'])
        daily_by_cat = daily_by_cat.set_index(['date', 'hour_category']).reindex(full_index, fill_value=0).reset_index()
        daily_by_cat['day_of_week_num'] = daily_by_cat['date'].dt.dayofweek
        daily_by_cat['week_start'] = daily_by_cat['date'] - pd.to_timedelta(daily_by_cat['day_of_week_num'], unit='d')
        daily_by_cat['week_label'] = daily_by_cat['week_start'].dt.strftime('Week %U')
        fig = px.bar(
            daily_by_cat,
            x='date',
            y='fatigue_count',
            color='hour_category',
            title="Daily Fatigue Alerts by Detailed Hour Category",
            color_discrete_map=color_map,
            labels={'fatigue_count': 'Fatigue Alerts', 'date': 'Date'},
            hover_data={'fatigue_count': True, 'week_label': True}
        )
        fig.update_layout(
            barmode='stack',
            xaxis_title="Date",
            yaxis_title="Fatigue Alerts",
            height=400,
            legend_title="Hour Category"
        )
        unique_weeks = daily_by_cat['week_start'].unique()
        shapes = []
        week_labels = []
        bg_colors = ['#f0e6ff', '#e6f0ff', '#e6fff0', '#fff0e6', '#ffe6e6', '#f0ffe6', '#e6e6ff']
        for i, week in enumerate(sorted(unique_weeks)):
            week_days = daily_by_cat[daily_by_cat['week_start'] == week]['date']
            if len(week_days) > 0:
                start_date = week_days.min()
                end_date = week_days.max()
                shapes.append(dict(
                    type="rect",
                    xref="x",
                    yref="paper",
                    x0=start_date,
                    x1=end_date,
                    y0=0,
                    y1=1,
                    fillcolor=bg_colors[i % len(bg_colors)],
                    opacity=0.2,
                    layer="below",
                    line_width=0,
                ))
                week_labels.append(
                    dict(
                        xref='x',
                        yref='paper',
                        x=start_date + (end_date - start_date) / 2,
                        y=1.02,
                        text=f"Week {week.strftime('%U')}",
                        showarrow=False,
                        font=dict(size=10),
                        xanchor='center',
                        yanchor='bottom'
                    )
                )
        fig.update_layout(shapes=shapes, annotations=week_labels)
        st.plotly_chart(fig, use_container_width=True)
    except Exception as e:
        st.error(f"⚠️ Error in Daily Fatigue by Detailed Hour Category: {e}")
else:
    st.info("ℹ️ Insufficient time data to display this visualization.")

# =================== OBJECTIVE 3: Daily Roster Insight per Week (Scatter Plot) =====================
# =================== OBJECTIVE 3: Daily Roster Insight per Week (Scatter Plot) =====================
st.subheader("OBJECTIVE 3: Looking for patterns in your team’s weekly roster?")
if not df.empty and col_operator in df.columns and col_shift and col_shift in df.columns:
    try:
        df['date'] = pd.to_datetime(df['date'])
        # Hitung total event per hari
        daily_totals = df.groupby('date').size().reset_index(name='total_count')
        # Ambil dominant shift per hari
        dominant_shift = df.groupby('date')[col_shift].agg(lambda x: x.value_counts().idxmax()).reset_index()
        dominant_shift.rename(columns={col_shift: 'dominant_shift'}, inplace=True)
        daily_analysis = daily_totals.merge(dominant_shift, on='date', how='left')
        daily_analysis['week_start'] = daily_analysis['date'] - pd.to_timedelta(daily_analysis['date'].dt.weekday, unit='d')
        summary = []
        weekly_groups = daily_analysis.groupby('week_start')
        for week_start, week_data in weekly_groups:
            # Urutkan data berdasarkan tanggal dalam minggu ini
            week_data_sorted = week_data.sort_values('date').reset_index(drop=True)
            for idx, row in week_data_sorted.iterrows():
                current_date = row['date']
                current_shift = row['dominant_shift']
                current_count = row['total_count']
                # --- CARI DATA DI HARI SEBELUM DAN SESUDAH (BERDASARKAN TANGGAL, BUKAN INDEKS) ---
                prev_date = current_date - pd.Timedelta(days=1)
                next_date = current_date + pd.Timedelta(days=1)
                # Cari shift di hari sebelumnya
                prev_row = week_data_sorted[week_data_sorted['date'] == prev_date]
                prev_shift = prev_row['dominant_shift'].iloc[0] if not prev_row.empty else None
                # Cari shift di hari berikutnya
                next_row = week_data_sorted[week_data_sorted['date'] == next_date]
                next_shift = next_row['dominant_shift'].iloc[0] if not next_row.empty else None
                # ---- LOGIKA REMARK BERDASARKAN PERUBAHAN SHIFT DALAM MINGGU YANG SAMA----
                # Awal Roster: Ada data di hari sebelumnya (prev_date) dalam minggu, dan shift-nya berbeda
                # Akhir Roster: Ada data di hari berikutnya (next_date) dalam minggu, dan shift-nya berbeda
                # Bukan Awal/Akhir: Ada data di hari sebelumnya ATAU berikutnya, dan shift-nya sama
                # Unknown: Tidak ada data di hari sebelumnya (prev_date) DAN tidak ada data di hari berikutnya (next_date) dalam minggu yang sama
                if pd.isna(current_shift):
                    remark = "Unknown"
                elif prev_shift is not None and prev_shift != current_shift:
                    remark = "Start of Roster"
                elif next_shift is not None and next_shift != current_shift:
                    remark = "End of Roster"
                elif (prev_shift is not None and prev_shift == current_shift) or (next_shift is not None and next_shift == current_shift):
                    remark = "Neither Start nor End of Roster"
                elif prev_shift is None and next_shift is None:
                    remark = "Unknown"
                else:
                    remark = "Unknown"
                # --- Operator dari data df (YANG SUDAH DIFILTER) ---
                df_orig_for_date = df[df['date']==current_date] # Gunakan df yang difilter
                if not df_orig_for_date.empty:
                    peak_nik_counts = df_orig_for_date[col_operator].value_counts()
                    peak_nik = peak_nik_counts.index[0] if not peak_nik_counts.empty else "N/A"
                else:
                    peak_nik = "N/A"
                summary.append({
                    'week_start': week_start,
                    'date': current_date,
                    'day_name': current_date.strftime('%A'),
                    'total_count': current_count,
                    'shift_category': current_shift,
                    'remark': remark,
                    'operator': peak_nik
                })
        summary_df = pd.DataFrame(summary)
        if not summary_df.empty:
            # Buat color map untuk remark (sesuai permintaan Anda)
            color_map_remark = {
                'Start of Roster': '#ffcccc',      # Merah muda
                'End of Roster': '#cce5ff',     # Biru muda
                'Neither Start nor End of Roster': '#fff2cc', # Kuning muda
                'Unknown': '#c0c0c0'           # Abu-abu muda
            }
            # ===== SCATTER PLOT (WARNA BERDASARKAN remark) =====
            fig = px.scatter(
                summary_df,
                x='date',
                y='remark',
                color='remark', # Warna berdasarkan remark (satu-satunya kolom di sumbu Y)
                color_discrete_map=color_map_remark, # Gunakan color_map_remark
                size='total_count',
                hover_data=['shift_category', 'operator', 'total_count'],
                title="Daily Roster Status by Date and Trend",
                category_orders={'remark': ['Start of Roster', 'End of Roster', 'Neither Start nor End of Roster', 'Unknown']}
            )
            fig.update_layout(height=450, xaxis_title="Date", yaxis_title="Roster Status")
            st.plotly_chart(fig, use_container_width=True)
            # ===== TABEL =====
            table_df = summary_df.rename(columns={
                'week_start':'Week Start',
                'day_name':'Day',
                'date':'Date',
                'total_count':'Event Count',
                'shift_category':'Dominant Shift',
                'remark':'Roster Status',
                'operator':'Operator'
            })
            def highlight_remark(row):
                colors = {
                    'Start of Roster':'background-color: #ffcccc',
                    'End of Roster':'background-color: #cce5ff',
                    'Neither Start nor End of Roster':'background-color: #fff2cc',
                    'Unknown':'background-color: #c0c0c0'
                }
                return [colors.get(row['Roster Status'], '') for _ in row]
            st.dataframe(table_df.style.apply(highlight_remark, axis=1), use_container_width=True)
        else:
            st.info("ℹ️ No daily data to analyze.")
    except Exception as e:
        st.error(f"Error in Daily Roster Insight: {e}")
else:
    if col_shift is None:
        st.info("ℹ️ Shift column not found, cannot display Daily Roster Insight.")
    elif col_shift not in df.columns:
        st.info(f"ℹ️ Column '{col_shift}' not found in the filtered data, cannot display Daily Roster Insight.")
    else:
        st.info("ℹ️ Insufficient data (date, operator, or shift column not found) to display daily roster insight.")
# import plotly.express as px
# from datetime import datetime
st.subheader("OBJECTIVE 4: How is the Fatigue Event Risk Map per Operator?")
import math
import plotly.express as px
try:
    # ============================
    # 1. PREPROCESS & COPY DF
    # ============================
    df_local = df.copy()
    df_local['date_only'] = df_local['start'].dt.normalize()
    df_local['week_number'] = df_local['date_only'].dt.isocalendar().week
    df_local['week_label'] = "Week " + df_local['week_number'].astype(str)
    # Unit cleanup
    df_local['unit_no'] = (
        df_local[col_fleet_no]
        .astype(str)
        .str.split("-", n=1).str[-1].str.strip()
    )
    if 'id' not in df_local.columns:
        st.error("❌ Column 'id' not found!")
        st.stop()
    # ============================
    # 2. FILTER 8 MINGGU TERAKHIR
    # ============================
    df_local['week_num_int'] = df_local['week_number'].astype(int)
    unique_weeks = sorted(df_local['week_num_int'].unique())
    selected_last8 = unique_weeks[-8:] if len(unique_weeks) >= 8 else unique_weeks
    df_8w = df_local[df_local['week_num_int'].isin(selected_last8)].copy()
    # =====================================
    # 3. FREQUENCY PER OPERATOR PER MINGGU
    # =====================================
    weekly_freq = (
        df_8w.groupby([col_operator, 'week_label'])['id']
        .nunique()
        .reset_index(name='weekly_frequency')
    )
    # ============================================
    # 4. SUMMARY FREQUENCY & CEIL AVERAGE FREQ
    # ============================================
    freq_summary = (
        weekly_freq
        .groupby(col_operator)['weekly_frequency']
        .agg(['sum', 'mean', 'count'])
        .reset_index()
        .rename(columns={
            'sum': 'frequency_by_shift',
            'mean': 'avg_frequency',
            'count': 'frequency_by_weeks'
        })
    )
    freq_summary['avg_frequency'] = freq_summary['avg_frequency'].apply(lambda x: math.ceil(x))
    # ================================
    # 5. RATA-RATA SPEED PER OPERATOR
    # ================================
    speed_summary = (
        df_8w.groupby(col_operator)[col_speed]
        .mean()
        .reset_index(name='avg_speed')
    )
    # =====================
    # 6. GABUNGKAN DATA
    # =====================
    risk_matrix = freq_summary.merge(speed_summary, on=col_operator, how='left')
    risk_matrix = risk_matrix.rename(columns={col_operator: "Operator Name"})
    # ================================
    # 7. Tentukan Quadrant untuk Count
    # ================================
    def assign_quadrant(row):
        if row['avg_frequency'] >= 2.5 and row['avg_speed'] >= 20:
            return "Quadrant I – Prevent at Source"
        elif row['avg_frequency'] < 2.5 and row['avg_speed'] >= 20:
            return "Quadrant II – Detect & Monitor"
        elif row['avg_frequency'] >= 2.5 and row['avg_speed'] < 20:
            return "Quadrant III – Monitor"
        else:
            return "Quadrant IV – Low Control"
    risk_matrix['quadrant'] = risk_matrix.apply(assign_quadrant, axis=1)
    quadrant_count = risk_matrix['quadrant'].value_counts().reindex([
        "Quadrant I – Prevent at Source",
        "Quadrant II – Detect & Monitor",
        "Quadrant III – Monitor",
        "Quadrant IV – Low Control"
    ], fill_value=0)
    # ================================
    # 8. VISUAL SCATTER PLOT
    # ================================
    fig = px.scatter(
        risk_matrix,
        x='avg_frequency',
        y='avg_speed',
        hover_name="Operator Name",
        title="Operator Risk Matrix: Frequency vs Speed",
        size=[12] * len(risk_matrix),
        size_max=15
    )
    max_x = risk_matrix['avg_frequency'].max() + 1
    max_y = risk_matrix['avg_speed'].max() + 1
    # ================================
    # 9. Quadrant Coloring
    # ================================
    fig.add_shape(type="rect", x0=2.5, x1=max_x, y0=20, y1=max_y,
                  fillcolor="rgba(255,0,0,0.25)", line_width=0)   # I
    fig.add_shape(type="rect", x0=0, x1=2.5, y0=20, y1=max_y,
                  fillcolor="rgba(255,150,50,0.25)", line_width=0) # II
    fig.add_shape(type="rect", x0=2.5, x1=max_x, y0=0, y1=20,
                  fillcolor="rgba(255,200,200,0.25)", line_width=0) # III
    fig.add_shape(type="rect", x0=0, x1=2.5, y0=0, y1=20,
                  fillcolor="rgba(0,120,255,0.15)", line_width=0) # IV
    # Garis batas
    fig.add_vline(x=2.5, line_dash="dash", line_color="black")
    fig.add_hline(y=20, line_dash="dash", line_color="black")
    # ================================
    # 10. Tampilkan Count di Quadrant
    # ================================
    fig.add_annotation(
        x=2.5 + (max_x-2.5)/2, y=20 + (max_y-20)/2,
        text=f"<b>{quadrant_count['Quadrant I – Prevent at Source']}</b>",
        showarrow=False, font=dict(size=20, color="red")
    )
    fig.add_annotation(
        x=2.5/2, y=20 + (max_y-20)/2,
        text=f"<b>{quadrant_count['Quadrant II – Detect & Monitor']}</b>",
        showarrow=False, font=dict(size=20, color="orange")
    )
    fig.add_annotation(
        x=2.5 + (max_x-2.5)/2, y=0 + (20-0)/2,
        text=f"<b>{quadrant_count['Quadrant III – Monitor']}</b>",
        showarrow=False, font=dict(size=20, color="darkred")
    )
    fig.add_annotation(
        x=2.5/2, y=0 + (20-0)/2,
        text=f"<b>{quadrant_count['Quadrant IV – Low Control']}</b>",
        showarrow=False, font=dict(size=20, color="blue")
    )
    # ================================
    # 11. Label Quadrant
    # ================================
    fig.add_annotation(x=4, y=max_y-2, text="Quadrant I<br>Prevent at Source",
                       showarrow=False, font=dict(size=12))
    fig.add_annotation(x=1.25, y=max_y-2, text="Quadrant II<br>Detect & Monitor",
                       showarrow=False, font=dict(size=12))
    fig.add_annotation(x=4, y=5, text="Quadrant III<br>Monitor",
                       showarrow=False, font=dict(size=12))
    fig.add_annotation(x=1.25, y=5, text="Quadrant IV<br>Low Control",
                       showarrow=False, font=dict(size=12))
    fig.update_xaxes(dtick=1)
    fig.update_layout(
        xaxis_title="Average Frequency (Ceil)",
        yaxis_title="Average Speed (km/h)",
        height=650
    )
    st.plotly_chart(fig, use_container_width=True)
    # ================================
    # 12. DISPLAY TABLE
    # ================================
    st.subheader("Operator Hazard Summary Table (8 Weeks Observed)")
    table_display = (
        risk_matrix[[
            "Operator Name",
            "frequency_by_shift",
            "avg_frequency",
            "frequency_by_weeks",
            "avg_speed",
            "quadrant"
        ]]
        .rename(columns={
            "frequency_by_shift": "Frequency by Shift",
            "avg_frequency": "Avg Frequency",
            "frequency_by_weeks": "Frequency by Weeks",
            "avg_speed": "Avg Speed",
            "quadrant":"Quadrant"
        })
    )
    st.dataframe(
        table_display.sort_values("Avg Frequency", ascending=False),
        use_container_width=True
    )
except Exception as e:
    st.error(f"⚠️ Error Risk Map Objective 4: {e}")
    st.exception(e)


        # st.exception(e)  # Uncomment during development


import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
st.subheader("OBJECTIVE 5: See your team’s Fatigue Hazard Profile!")

# Custom CSS — tetap seperti sebelumnya (sudah sesuai preferensi)
st.markdown("""
<style>
    .big-title {
        font-size: 28px;
        font-weight: bold;
        color: #ffffff;
        text-align: center;
        margin-bottom: 10px;
        background: linear-gradient(135deg, #2c3e50, #1a252c);
        padding: 15px;
        border-radius: 10px;
        box-shadow: 0 4px 15px rgba(0,0,0,0.3);
    }
    .subnote {
        font-size: 16px;
        color: #7f8c8d;
        text-align: center;
        margin-bottom: 20px;
    }
    .section-divider {
        height: 2px;
        background: linear-gradient(to right, #3498db, #2ecc71, #f1c40f, #e74c3c);
        margin: 20px 0;
    }
    .legend-container {
        display: flex;
        gap: 15px;
        margin: 15px 0;
    }
    .legend-box {
        background: white;
        border: 1px solid #ddd;
        border-radius: 8px;
        padding: 15px;
        flex: 1;
        min-width: 300px;
        box-shadow: 0 2px 10px rgba(0,0,0,0.05);
    }
    .legend-title {
        font-weight: bold;
        color: #2c3e50;
        margin-bottom: 10px;
        font-size: 14px;
        border-bottom: 1px solid #eee;
        padding-bottom: 5px;
    }
    .legend-item {
        display: flex;
        align-items: center;
        margin: 5px 0;
        font-size: 12px;
    }
    .legend-color {
        width: 18px;
        height: 18px;
        border-radius: 3px;
        margin-right: 8px;
        border: 1px solid #ccc;
    }
    .ai-insight-box {
        background: #f8f9fa;
        border: 1px solid #dee2e6;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
        color: #2c3e50;
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
    }
    .ai-insight-title {
        font-weight: bold;
        color: #2c3e50;
        margin-bottom: 8px;
        font-size: 14px;
        background: #e9ecef;
        padding: 8px;
        border-radius: 5px;
        border-left: 4px solid #495057;
    }
    .trend-up {
        color: #e74c3c;
        font-weight: bold;
    }
    .trend-down {
        color: #27ae60;
        font-weight: bold;
    }
    .recommendation-box {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        border: 1px solid #4a5568;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
        color: white;
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
        box-shadow: 0 4px 15px rgba(0,0,0,0.1);
    }
    .recommendation-title {
        font-weight: bold;
        color: white;
        margin-bottom: 8px;
        font-size: 14px;
        background: rgba(255,255,255,0.2);
        padding: 8px;
        border-radius: 5px;
        border-left: 4px solid white;
    }
    .recommendation-reason {
        font-size: 12px;
        margin-top: 10px;
        padding: 8px;
        background: rgba(255,255,255,0.1);
        border-radius: 5px;
        border-left: 3px solid rgba(255,255,255,0.3);
    }
</style>
""", unsafe_allow_html=True)

# ===============================================================
# LOGIC UTAMA
# ===============================================================
if df.empty:
    st.info("No data available after applying filters.")
else:
    try:
        required = [col_operator, col_fleet_type, "start"]
        if not all(c in df.columns for c in required if c is not None):
            st.warning("Required columns (operator, fleet_type, start) are missing.")
            st.stop()

        df_op = df[[col_operator, col_fleet_type, "start"]].dropna()
        if df_op.empty:
            st.info("No operator data after filtering.")
            st.stop()

        if col_operator is None:
            st.error("Operator column could not be auto-detected. Please check your data.")
            st.stop()

        df_op["year_week"] = df_op["start"].dt.strftime("%Y-W%U")

        # Fuzzy match fleet names
        fleet_clean = df_op[col_fleet_type].str.strip().str.upper()
        df_op["is_ob"] = fleet_clean.str.contains(r"OB HAULLER", na=False)
        df_op["is_coal"] = fleet_clean.str.contains(r"HAULING COAL", na=False)

        ob_data = df_op[df_op["is_ob"]]
        coal_data = df_op[df_op["is_coal"]]

        def get_top10_with_slope(data):
            if data.empty:
                return pd.DataFrame()
            if col_operator not in data.columns:
                st.error(f"Operator column '{col_operator}' not found in data subset.")
                return pd.DataFrame()

            weekly = data.groupby([col_operator, "year_week"]).size().reset_index(name="weekly_sum")
            metrics = []
            for nik, grp in weekly.groupby(col_operator):
                if pd.isna(nik):
                    continue
                grp = grp.sort_values("year_week")
                counts = grp["weekly_sum"].values
                weeks = np.arange(len(counts))
                weekly_avg = counts.mean()
                total_events = counts.sum()
                n_weeks = len(counts)
                if n_weeks >= 2:
                    x_mean = weeks.mean()
                    y_mean = counts.mean()
                    numerator = np.sum((weeks - x_mean) * (counts - y_mean))
                    denominator = np.sum((weeks - x_mean) ** 2)
                    slope = numerator / denominator if denominator != 0 else 0.0
                else:
                    slope = 0.0  # One Time Event
                metrics.append({
                    col_operator: nik,
                    "weekly_avg": weekly_avg,
                    "slope": slope,
                    "total_events": total_events,
                    "n_weeks": n_weeks
                })
            if not metrics:
                return pd.DataFrame()
            return pd.DataFrame(metrics).nlargest(10, "weekly_avg")

        top_ob = get_top10_with_slope(ob_data)
        top_coal = get_top10_with_slope(coal_data)

        def get_all_operators_with_slope(data):
            if data.empty:
                return pd.DataFrame()
            if col_operator not in data.columns:
                return pd.DataFrame()

            weekly = data.groupby([col_operator, "year_week"]).size().reset_index(name="weekly_sum")
            metrics = []
            for nik, grp in weekly.groupby(col_operator):
                if pd.isna(nik):
                    continue
                grp = grp.sort_values("year_week")
                counts = grp["weekly_sum"].values
                weeks = np.arange(len(counts))
                weekly_avg = counts.mean()
                total_events = counts.sum()
                n_weeks = len(counts)
                if n_weeks >= 2:
                    slope = np.cov(weeks, counts)[0, 1] / np.var(weeks) if np.var(weeks) != 0 else 0.0
                else:
                    slope = 0.0
                metrics.append({
                    col_operator: nik,
                    "weekly_avg": weekly_avg,
                    "slope": slope,
                    "total_events": total_events,
                    "n_weeks": n_weeks
                })
            return pd.DataFrame(metrics) if metrics else pd.DataFrame()

        all_ob = get_all_operators_with_slope(ob_data)
        all_coal = get_all_operators_with_slope(coal_data)

        # ===============================================================
        # LEGEND — UPDATED: Stable → One Time Event, Gray → Yellow
        # ===============================================================
        st.subheader("Legend of Frequency Trends")

        st.markdown("""
        <div class="legend-container">
            <div class="legend-box">
                <div class="legend-title">Worsening Trends (Positive Slope):</div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #d32f2f;"></div>
                    <span>Very High Worsening (≥1.5)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #e57373;"></div>
                    <span>High Worsening (1.0–1.5)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #ef9a9a;"></div>
                    <span>Moderate Worsening (0.5–1.0)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #ffcdd2;"></div>
                    <span>Slight Worsening (0–0.5)</span>
                </div>
                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
                    Note: Positive slope indicates increasing fatigue event frequency over weeks.
                </i>
            </div>
            <div class="legend-box">
                <div class="legend-title">Improving Trends (Negative Slope):</div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #388e3c;"></div>
                    <span>Excellent Improvement (≤−1.5)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #81c784;"></div>
                    <span>Great Improvement (−1.5 to −1.0)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #a5d6a7;"></div>
                    <span>Good Improvement (−1.0 to −0.5)</span>
                </div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #c8e6c9;"></div>
                    <span>Slight Improvement (−0.5 to 0)</span>
                </div>
                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
                    Note: Negative slope reflects a consistent decline in fatigue events.
                </i>
            </div>
            <div class="legend-box">
                <div class="legend-title">One-Time Events (Zero Slope):</div>
                <div class="legend-item">
                    <div class="legend-color" style="background-color: #FFD700;"></div>
                    <span>One Time Event (0)</span>
                </div>
                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
                    Note: Slope = 0 by definition when data exists for only one week — trend assessment is not applicable.
                </i>
            </div>
        </div>
        """, unsafe_allow_html=True)
        # ✅ Function definition at correct indentation level
        def plot_chart(data, title):
            if data.empty:
                fig = go.Figure()
                fig.add_annotation(
                    text="No Data",
                    x=0.5, y=0.5,
                    showarrow=False,
                    font_size=16
                )
                fig.update_layout(
                    height=350,
                    title=dict(text=title, x=0.5, xanchor='center')  # ← Ini wajib
                )
                return fig

            data_sorted = data.sort_values('weekly_avg', ascending=False)

            def get_color(slope):
                if slope == 0:
                    return "#FFD700"  # ✅ Yellow for One Time Event
                elif slope > 0:
                    if slope < 0.5:
                        return "#ffcdd2"
                    elif slope < 1.0:
                        return "#ef9a9a"
                    elif slope < 1.5:
                        return "#e57373"
                    else:
                        return "#d32f2f"
                else:  # slope < 0
                    if slope > -0.5:
                        return "#c8e6c9"
                    elif slope > -1.0:
                        return "#a5d6a7"
                    elif slope > -1.5:
                        return "#81c784"
                    else:
                        return "#388e3c"

            colors = [get_color(s) for s in data_sorted["slope"]]

            bar_trace = go.Bar(
                x=data_sorted[col_operator].astype(str),
                y=data_sorted["weekly_avg"],
                marker=dict(color=colors, line=dict(width=2, color="rgba(0,0,0,0.2)")),
                text=[f"{v:.1f}" for v in data_sorted["weekly_avg"]],
                textposition="outside",
                hovertemplate=(
                    "<b>%{x}</b><br>" +
                    "Weekly Avg: %{y:.2f}<br>" +
                    "Trend Slope: %{customdata[0]:+.3f}<br>" +
                    "Total Events: %{customdata[1]}<br>" +
                    "Weeks Active: %{customdata[2]}<br>" +
                    "<extra></extra>"
                ),
                customdata=np.stack([data_sorted["slope"], data_sorted["total_events"], data_sorted["n_weeks"]], axis=-1)
            )

            fig = go.Figure(bar_trace)
            fig.update_layout(
                title=dict(text=f"<b>{title}</b>", x=0.5, xanchor='center'),  # ← Ini wajib
                height=450,
                margin=dict(l=50, r=20, t=60, b=120),
                xaxis_title="<b>Operator Name</b>",
                yaxis_title="<b>Weekly Avg Events</b>",
                font=dict(family="Segoe UI", size=12),
                bargap=0.3,
                plot_bgcolor="rgba(0,0,0,0)",
                paper_bgcolor="rgba(0,0,0,0)",
                xaxis=dict(tickangle=45)
            )
            return fig

        # ===============================================================
        # CHARTS
        # ===============================================================
        # ===============================================================
        # CHARTS
        # ===============================================================
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(
                plot_chart(top_ob, "OB HAULER Operator Hazard Profile"),
                use_container_width=True,  # ← Ini penting!
                config={'displayModeBar': False}
            )
        with col2:
            st.plotly_chart(
                plot_chart(top_coal, "COAL HAULING Operator Hazard Profile"),
                use_container_width=True,  # ← Ini penting!
                config={'displayModeBar': False}
            )
        # ===============================================================
        # AI INSIGHTS — DIPERBAIKI: Risk Summary jadi 1 box + 3 list
        # ===============================================================
        col_insight1, col_insight2 = st.columns(2)

        with col_insight1:
            if not top_ob.empty:
                st.markdown("### OB HAULER Analysis")
                ob_worsening = len(top_ob[top_ob['slope'] > 0])
                ob_improving = len(top_ob[top_ob['slope'] < 0])
                ob_one_time = len(top_ob[top_ob['slope'] == 0])
                ob_avg_risk = top_ob['weekly_avg'].mean()
                ob_max_risk = top_ob['weekly_avg'].max()

                ob_insights = []
                if ob_worsening > ob_improving:
                    ob_insights.append(f"{ob_worsening} out of 10 top risk operators are showing <span class='trend-up'>worsening</span> trends.")
                else:
                    ob_insights.append(f"{ob_improving} out of 10 top risk operators are showing <span class='trend-down'>improvement</span>.")
                if ob_one_time > 0:
                    ob_insights.append(f"{ob_one_time} operators are classified as <b>One Time Event</b> (single-week activity).")
                else:
                    ob_insights.append("No operators classified as <b>One Time Event</b>.")
                ob_insights.append(f"Average risk: {ob_avg_risk:.2f} events/week (max: {ob_max_risk:.2f}).")

                st.markdown(f"""
                <div class="ai-insight-box">
                    <div class="ai-insight-title">Hazard Summary</div>
                    <ul style="padding-left: 20px; margin: 8px 0; line-height: 1.5;">
                        <li>{ob_insights[0]}</li>
                        <li>{ob_insights[1]}</li>
                        <li>{ob_insights[2]}</li>
                    </ul>
                </div>
                """, unsafe_allow_html=True)
            else:
                st.info("No OB HAULER data for analysis.")

        with col_insight2:
            if not top_coal.empty:
                st.markdown("### HAULING COAL Analysis")
                coal_worsening = len(top_coal[top_coal['slope'] > 0])
                coal_improving = len(top_coal[top_coal['slope'] < 0])
                coal_one_time = len(top_coal[top_coal['slope'] == 0])
                coal_avg_risk = top_coal['weekly_avg'].mean()
                coal_max_risk = top_coal['weekly_avg'].max()

                coal_insights = []
                if coal_worsening > coal_improving:
                    coal_insights.append(f"{coal_worsening} out of 10 top risk operators are showing <span class='trend-up'>worsening</span> trends.")
                else:
                    coal_insights.append(f"{coal_improving} out of 10 top risk operators are showing <span class='trend-down'>improvement</span>.")
                if coal_one_time > 0:
                    coal_insights.append(f"{coal_one_time} operators are classified as <b>One Time Event</b> (single-week activity).")
                else:
                    coal_insights.append("No operators classified as <b>One Time Event</b>.")
                coal_insights.append(f"Average risk: {coal_avg_risk:.2f} events/week (max: {coal_max_risk:.2f}).")

                st.markdown(f"""
                <div class="ai-insight-box">
                    <div class="ai-insight-title">Hazard Summary</div>
                    <ul style="padding-left: 20px; margin: 8px 0; line-height: 1.5;">
                        <li>{coal_insights[0]}</li>
                        <li>{coal_insights[1]}</li>
                        <li>{coal_insights[2]}</li>
                    </ul>
                </div>
                """, unsafe_allow_html=True)
            else:
                st.info("No HAULING COAL data for analysis.")

        # ===============================================================
        # RECOMMENDATIONS — DIPERBARUI: 3 list per fleet, sesuai 3 poin Risk Summary
        # ===============================================================
        col_rec1, col_rec2 = st.columns(2)

        with col_rec1:
            if not top_ob.empty:
                w = len(top_ob[top_ob['slope'] > 0])
                ot = len(top_ob[top_ob['slope'] == 0])
                avg = top_ob['weekly_avg'].mean()
                max_risk = top_ob['weekly_avg'].max()

                # 3 rekomendasi, paralel dengan 3 poin Risk Summary
                rec_list = []

                # 1. Trend-driven action
                if w > 5:
                    rec_list.append("Conduct targeted fatigue risk assessments for operators with worsening trends (slope > 0).")
                elif w > 0 and w <= 5:
                    rec_list.append("Monitor worsening-trend operators weekly and schedule supervisor check-ins.")
                elif w == 0 and len(top_ob[top_ob['slope'] < 0]) > 0:
                    rec_list.append("Recognize improving operators — consider sharing best practices internally.")
                else:
                    rec_list.append("Maintain current monitoring for stable trend profile.")

                # 2. One-Time Event follow-up
                if ot > 0:
                    rec_list.append(f"Re-engage {ot} <b>One Time Event</b> operators to verify data completeness and activity status.")
                else:
                    rec_list.append("Trend analysis is reliable — all operators have multi-week activity.")

                # 3. Benchmark & sustain
                if avg > 8:
                    rec_list.append("Initiate immediate review of shift scheduling and rest-break compliance.")
                elif avg > 5:
                    rec_list.append("Conduct monthly fatigue KPI review using cohort average as baseline.")
                else:
                    rec_list.append("Sustain current protocols — risk level is within acceptable range.")

                # Ensure exactly 3 items
                while len(rec_list) < 3:
                    rec_list.append("—")

                st.markdown("### OB HAULER Recommendations")
                st.markdown(f"""
                <div class="recommendation-box">
                    <div class="recommendation-title">Action Plan</div>
                    <ul style="padding-left: 20px; margin: 8px 0; line-height: 1.5;">
                        <li>{rec_list[0]}</li>
                        <li>{rec_list[1]}</li>
                        <li>{rec_list[2]}</li>
                    </ul>
                </div>
                """, unsafe_allow_html=True)
            else:
                st.info("No OB HAULER recommendations.")

        with col_rec2:
            if not top_coal.empty:
                w = len(top_coal[top_coal['slope'] > 0])
                ot = len(top_coal[top_coal['slope'] == 0])
                avg = top_coal['weekly_avg'].mean()
                max_risk = top_coal['weekly_avg'].max()

                rec_list = []

                # 1. Trend-driven action
                if w > 5:
                    rec_list.append("Conduct targeted fatigue risk assessments for operators with worsening trends (slope > 0).")
                elif w > 0 and w <= 5:
                    rec_list.append("Monitor worsening-trend operators weekly and schedule supervisor check-ins.")
                elif w == 0 and len(top_coal[top_coal['slope'] < 0]) > 0:
                    rec_list.append("Recognize improving operators — consider sharing best practices internally.")
                else:
                    rec_list.append("Maintain current monitoring for stable trend profile.")

                # 2. One-Time Event follow-up
                if ot > 0:
                    rec_list.append(f"Re-engage {ot} <b>One Time Event</b> operators to verify data completeness and activity status.")
                else:
                    rec_list.append("Trend analysis is reliable — all operators have multi-week activity.")

                # 3. Benchmark & sustain
                if avg > 8:
                    rec_list.append("Initiate immediate review of shift scheduling and rest-break compliance.")
                elif avg > 5:
                    rec_list.append("Conduct monthly fatigue KPI review using cohort average as baseline.")
                else:
                    rec_list.append("Sustain current protocols — risk level is within acceptable range.")

                while len(rec_list) < 3:
                    rec_list.append("—")

                st.markdown("### HAULING COAL Recommendations")
                st.markdown(f"""
                <div class="recommendation-box">
                    <div class="recommendation-title">Action Plan</div>
                    <ul style="padding-left: 20px; margin: 8px 0; line-height: 1.5;">
                        <li>{rec_list[0]}</li>
                        <li>{rec_list[1]}</li>
                        <li>{rec_list[2]}</li>
                    </ul>
                </div>
                """, unsafe_allow_html=True)
            else:
                st.info("No HAULING COAL recommendations.")

    except Exception as e:
        st.error(f"Error in Top 10 Operator analysis: {str(e)}")
        st.exception(e)



# # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
# st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")

# # Membagi tampilan menjadi dua kolom
# col_insights, col_recs = st.columns(2)

# # =====================================================================
# # 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS
# # =====================================================================
# with col_insights:
#     st.subheader("Insights by Advanced Analytics")

#     # ===================== 1. Critical Hour Analysis =====================
#     critical_hours = [2, 3, 4, 5]
#     critical_alerts = df[df['hour'].isin(critical_hours)]
#     critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0

#     st.markdown(f"**Critical Hour Risk (3-6 AM)**")
#     bg_color = (
#         "#ffcccc" if critical_pct > 50 else
#         "#ffebcc" if critical_pct > 25 else
#         "#ffffcc" if critical_pct > 10 else
#         "#e6ffe6"
#     )
#     st.markdown(
#         f'<div style="background-color: {bg_color}; padding: 10px; border-radius: 5px;">'
#         f'Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)</div>',
#         unsafe_allow_html=True
#     )

#     if critical_pct > 10:
#         st.warning(
#             f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM). "
#             f"This is a known circadian dip period."
#         )
#     else:
#         st.info(
#             f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range."
#         )

#     # ===================== 2. High-Speed Fatigue Analysis =====================
#     if col_speed and col_speed in df.columns:
#         high_speed_threshold = 20
#         high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
#         high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

#         st.markdown(f"**High-Speed Fatigue Risk (Speed > {high_speed_threshold} km/h)**")
#         st.markdown(
#             f"""
#             <div style="font-size: 24px; font-weight: bold;">{len(high_speed_fatigue)}</div>
#             <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {high_speed_pct:.1f}% of total alerts</div>
#             """,
#             unsafe_allow_html=True
#         )

#         if high_speed_pct > 20:
#             st.warning(
#                 f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. "
#                 f"This increases accident severity potential."
#             )
#         else:
#             st.info(
#                 f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range."
#             )
#     else:
#         st.info("Speed data not available for High-Speed Fatigue Analysis.")

#     # ===================== 3. Shift Pattern Analysis =====================
#     if col_shift and col_shift in df.columns:
#         shift_counts = df[col_shift].value_counts()
#         st.markdown(f"**Shift Pattern Risk**")

#         for shift_val in shift_counts.index:
#             shift_pct = (shift_counts[shift_val] / len(df)) * 100

#             st.markdown(
#                 f"""
#                 <div style="font-size: 24px; font-weight: bold;">{shift_counts[shift_val]}</div>
#                 <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {shift_pct:.1f}% of total alerts</div>
#                 """,
#                 unsafe_allow_html=True
#             )

#             if shift_pct > 50:
#                 st.warning(
#                     f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). "
#                     f"Review shift scheduling and workload."
#                 )
#             else:
#                 st.info(
#                     f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%)."
#                 )
#     else:
#         st.info("Shift data not available for Shift Pattern Analysis.")

#     # ===================== 4. Operator Risk Profiling =====================
#     if col_operator and col_operator in df.columns:
#         operator_alerts = df[col_operator].value_counts()
#         top_risk_operators = operator_alerts.head(5)

#         st.markdown("**High-Risk Operator Identification**")
#         colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]

#         for idx, (op_name, count) in enumerate(top_risk_operators.items()):
#             op_pct = (count / len(df)) * 100
#             color = colors[idx] if idx < len(colors) else colors[-1]

#             st.markdown(
#                 f"**Operator:** {op_name}  \n**Alerts:** {count}"
#             )
#             st.markdown(
#                 f"<span style='font-weight:600'>Share:</span> "
#                 f"<span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
#                 unsafe_allow_html=True
#             )

#             if op_pct > 5:
#                 st.warning(
#                     f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%). "
#                     f"Consider coaching or rest plan."
#                 )
#             else:
#                 st.info(
#                     f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%)."
#                 )
#     else:
#         st.info("Operator data not available for Operator Risk Profiling.")

# # =====================================================================
# # 🔹 KOLOM KANAN — AI RECOMMENDATIONS (PER INSIGHT + PER OPERATOR)
# # =====================================================================
# with col_recs:
#     st.subheader("Recommendations")

#     ai_recommendations = []

#     # 1. Critical Hour Insight → AI Rec
#     if "hour" in df.columns and not df.empty:
#         peak_hour = df["hour"].value_counts().idxmax()
#         critical_hours = [2, 3, 4, 5]

#         if peak_hour in critical_hours:
#             ai_recommendations.append({
#                 "action": "Deploy enhanced fatigue monitoring systems during 3-6 AM.",
#                 "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}%)",
#                 "reasoning": "High percentage of alerts during circadian low period."
#             })
#         else:
#             ai_recommendations.append({
#                 "action": "Monitor fatigue patterns around peak hour (Hour {peak_hour}).",
#                 "data_point": f"Peak Hour: {peak_hour}:00 — {df['hour'].value_counts()[peak_hour]} alerts",
#                 "reasoning": "This hour shows highest fatigue occurrence."
#             })

#     # 2. High-Speed Insight → AI Rec
#     if col_speed and col_speed in df.columns and not df.empty:
#         high_speed_threshold = 20
#         high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
#         high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

#         if high_speed_pct > 20:
#             ai_recommendations.append({
#                 "action": "Implement speed-reduction protocols during fatigue-prone hours.",
#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
#                 "reasoning": "High-speed alerts increase accident severity potential."
#             })
#         else:
#             ai_recommendations.append({
#                 "action": "Maintain current speed monitoring — risk level is acceptable.",
#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
#                 "reasoning": "Current high-speed fatigue rate is within acceptable range."
#             })

#     # 3. Shift Pattern Insight → AI Rec
#     if col_shift and col_shift in df.columns and not df.empty:
#         worst_shift = df[col_shift].value_counts().idxmax()
#         shift_pct = (df[col_shift].value_counts()[worst_shift] / len(df)) * 100

#         if shift_pct > 50:
#             ai_recommendations.append({
#                 "action": "Review shift rotation schedules for Shift {worst_shift}.",
#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
#                 "reasoning": "Disproportionately high fatigue alerts indicate scheduling imbalance."
#             })
#         else:
#             ai_recommendations.append({
#                 "action": "Continue monitoring all shifts — no dominant risk identified.",
#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
#                 "reasoning": "Shift distribution is balanced."
#             })

#     # 4. Operator Risk Profiling → AI Rec for EACH of Top 5 Operators
#     if col_operator and col_operator in df.columns and not df.empty:
#         top_operators = df[col_operator].value_counts().head(5)
#         for op_name, count in top_operators.items():
#             op_pct = (count / len(df)) * 100

#             if op_pct > 5:
#                 ai_recommendations.append({
#                     "action": f"Coaching or mandatory rest for Operator {op_name}.",
#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)",
#                     "reasoning": f"Operator has high fatigue alerts — requires individual intervention."
#                 })
#             else:
#                 ai_recommendations.append({
#                     "action": f"Continue general monitoring for Operator {op_name}.",
#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)",
#                     "reasoning": f"Risk is within acceptable range — no urgent action needed."
#                 })

#     # Render each recommendation as a card
#     for rec in ai_recommendations:
#         # Highlight percentages in red
#         data_point_colored = rec['data_point'].replace(
#             f"({rec['data_point'].split('(')[-1]}",
#             f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
#         ).replace(")", "</span>)")

#         reasoning_colored = rec['reasoning'].replace(
#             f"({rec['reasoning'].split('(')[-1]}",
#             f"(<span style='color: red;'>{rec['reasoning'].split('(')[-1]}"
#         ).replace(")", "</span>)")

#         st.markdown(
#             f"""
#             <div style="
#                 background: #f8f9fa;
#                 border: 1px solid #dee2e6;
#                 border-radius: 8px;
#                 padding: 15px;
#                 margin: 10px 0;
#                 box-shadow: 0 2px 8px rgba(0,0,0,0.05);
#             ">
#                 <div style="
#                     font-weight: bold;
#                     background: #e9ecef;
#                     padding: 8px;
#                     border-radius: 5px;
#                     margin-bottom: 8px;
#                     border-left: 4px solid #495057;
#                 ">
#                     AI Recommendation
#                 </div>
#                 <div style="padding: 8px 0;">
#                     <strong>Action:</strong> {rec['action']}
#                 </div>
#                 <div style="
#                     padding: 8px;
#                     background: #f1f1f1;
#                     border-radius: 5px;
#                     margin: 8px 0;
#                 ">
#                     <strong>Data Point:</strong> {data_point_colored}
#                 </div>
#                 <div style="
#                     padding: 8px;
#                     background: #f1f1f1;
#                     border-radius: 5px;
#                 ">
#                     <strong>AI Reasoning:</strong> {reasoning_colored}
#                 </div>
#             </div>
#             """,
#             unsafe_allow_html=True
#         )

#     if not ai_recommendations:
#         st.info(
#             "No specific data points available for AI recommendations. "
#             "Ensure relevant columns are present (hour, shift, operator, duration, speed)."
#         )

# # ================= FOOTER ===========================
# st.markdown("---")
# st.markdown(
#     '<div class="footer">FatigueAnalyzer - Transforming Mining Safety with Intelligent Analytics | Contact: info@bukittechnology.com</div>',
#     unsafe_allow_html=True


# )

# # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
# st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")

# # Membagi tampilan menjadi dua kolom
# col_insights, col_recs = st.columns(2)

# # =====================================================================
# # 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS
# # =====================================================================
# with col_insights:
#     st.subheader("Insights by Advanced Analytics")

#     # ===================== 1. Critical Hour Analysis =====================
#     critical_hours = [2, 3, 4, 5]
#     critical_alerts = df[df['hour'].isin(critical_hours)]
#     critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0

#     st.markdown(f"**Critical Hour Risk (3-6 AM)**")
#     bg_color = (
#         "#ffcccc" if critical_pct > 50 else
#         "#ffebcc" if critical_pct > 25 else
#         "#ffffcc" if critical_pct > 10 else
#         "#e6ffe6"
#     )
#     st.markdown(
#         f'<div style="background-color: {bg_color}; padding: 10px; border-radius: 5px;">'
#         f'Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)</div>',
#         unsafe_allow_html=True
#     )

#     if critical_pct > 10:
#         st.warning(
#             f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM). "
#             f"This is a known circadian dip period."
#         )
#     else:
#         st.info(
#             f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range."
#         )

#     # ===================== 2. High-Speed Fatigue Analysis =====================
#     if col_speed and col_speed in df.columns:
#         high_speed_threshold = 20
#         high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
#         high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

#         st.markdown(f"**High-Speed Fatigue Risk (Speed > {high_speed_threshold} km/h)**")
#         st.markdown(
#             f"""
#             <div style="font-size: 24px; font-weight: bold;">{len(high_speed_fatigue)}</div>
#             <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {high_speed_pct:.1f}% of total alerts</div>
#             """,
#             unsafe_allow_html=True
#         )

#         if high_speed_pct > 20:
#             st.warning(
#                 f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. "
#                 f"This increases accident severity potential."
#             )
#         else:
#             st.info(
#                 f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range."
#             )
#     else:
#         st.info("Speed data not available for High-Speed Fatigue Analysis.")

#     # ===================== 3. Shift Pattern Analysis =====================
#     if col_shift and col_shift in df.columns:
#         shift_counts = df[col_shift].value_counts()
#         st.markdown(f"**Shift Pattern Risk**")

#         for shift_val in shift_counts.index:
#             shift_pct = (shift_counts[shift_val] / len(df)) * 100

#             st.markdown(
#                 f"""
#                 <div style="font-size: 24px; font-weight: bold;">{shift_counts[shift_val]}</div>
#                 <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {shift_pct:.1f}% of total alerts</div>
#                 """,
#                 unsafe_allow_html=True
#             )

#             if shift_pct > 50:
#                 st.warning(
#                     f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). "
#                     f"Review shift scheduling and workload."
#                 )
#             else:
#                 st.info(
#                     f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%)."
#                 )
#     else:
#         st.info("Shift data not available for Shift Pattern Analysis.")

#     # ===================== 4. Operator Risk Profiling =====================
#     if col_operator and col_operator in df.columns:
#         operator_alerts = df[col_operator].value_counts()
#         top_risk_operators = operator_alerts.head(5)

#         st.markdown("**High-Risk Operator Identification**")
#         colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]

#         for idx, (op_name, count) in enumerate(top_risk_operators.items()):
#             op_pct = (count / len(df)) * 100
#             color = colors[idx] if idx < len(colors) else colors[-1]

#             st.markdown(
#                 f"**Operator:** {op_name}  \n**Alerts:** {count}"
#             )
#             st.markdown(
#                 f"<span style='font-weight:600'>Share:</span> "
#                 f"<span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
#                 unsafe_allow_html=True
#             )

#             if op_pct > 5:
#                 st.warning(
#                     f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%). "
#                     f"Consider coaching or rest plan."
#                 )
#             else:
#                 st.info(
#                     f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%)."
#                 )
#     else:
#         st.info("Operator data not available for Operator Risk Profiling.")

# # =====================================================================
# # 🔹 KOLOM KANAN — AI RECOMMENDATIONS
# # =====================================================================
# with col_recs:
#     st.subheader("Recommendations")

#     ai_recommendations = []

#     # 1. Critical Hour Insight → AI Rec
#     if "hour" in df.columns and not df.empty:
#         peak_hour = df["hour"].value_counts().idxmax()
#         critical_hours = [2, 3, 4, 5]

#         if peak_hour in critical_hours:
#             ai_recommendations.append({
#                 "type": "critical_hour",
#                 "action": "Deploy enhanced fatigue monitoring systems during 3-6 AM.",
#                 "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}%)",
#                 "reasoning": "High percentage of alerts during circadian low period."
#             })
#         else:
#             ai_recommendations.append({
#                 "type": "critical_hour",
#                 "action": "Monitor fatigue patterns around peak hour (Hour {peak_hour}).",
#                 "data_point": f"Peak Hour: {peak_hour}:00 — {df['hour'].value_counts()[peak_hour]} alerts",
#                 "reasoning": "This hour shows highest fatigue occurrence."
#             })

#     # 2. High-Speed Insight → AI Rec
#     if col_speed and col_speed in df.columns and not df.empty:
#         high_speed_threshold = 20
#         high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
#         high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

#         if high_speed_pct > 20:
#             ai_recommendations.append({
#                 "type": "high_speed",
#                 "action": "Implement speed-reduction protocols during fatigue-prone hours.",
#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
#                 "reasoning": "High-speed alerts increase accident severity potential."
#             })
#         else:
#             ai_recommendations.append({
#                 "type": "high_speed",
#                 "action": "Maintain current speed monitoring — risk level is acceptable.",
#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
#                 "reasoning": "Current high-speed fatigue rate is within acceptable range."
#             })

#     # 3. Shift Pattern Insight → AI Rec
#     if col_shift and col_shift in df.columns and not df.empty:
#         worst_shift = df[col_shift].value_counts().idxmax()
#         shift_pct = (df[col_shift].value_counts()[worst_shift] / len(df)) * 100

#         if shift_pct > 50:
#             ai_recommendations.append({
#                 "type": "shift_pattern",
#                 "action": "Review shift rotation schedules for Shift {worst_shift}.",
#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
#                 "reasoning": "Disproportionately high fatigue alerts indicate scheduling imbalance."
#             })
#         else:
#             ai_recommendations.append({
#                 "type": "shift_pattern",
#                 "action": "Continue monitoring all shifts — no dominant risk identified.",
#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
#                 "reasoning": "Shift distribution is balanced."
#             })

#     # 4. Operator Risk Profiling → Simple Recommendations (No AI Reasoning, No Box)
#     if col_operator and col_operator in df.columns and not df.empty:
#         top_operators = df[col_operator].value_counts().head(5)
#         for op_name, count in top_operators.items():
#             op_pct = (count / len(df)) * 100

#             if op_pct > 5:
#                 ai_recommendations.append({
#                     "type": "operator",
#                     "action": f"Coaching or mandatory rest for Operator {op_name}.",
#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
#                 })
#             else:
#                 ai_recommendations.append({
#                     "type": "operator",
#                     "action": f"Continue general monitoring for Operator {op_name}.",
#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
#                 })

#     # Render each recommendation based on type
#     for rec in ai_recommendations:
#         if rec["type"] == "operator":
#             # Simple format: Action + Data Point only
#             data_point_colored = rec['data_point'].replace(
#                 f"({rec['data_point'].split('(')[-1]}",
#                 f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
#             ).replace(")", "</span>)")

#             st.markdown(
#                 f"""
#                 <div style="margin: 10px 0; padding: 10px; background: #f8f9fa; border-left: 4px solid #495057; border-radius: 5px;">
#                     <strong>Action:</strong> {rec['action']}<br>
#                     <strong>Data Point:</strong> {data_point_colored}
#                 </div>
#                 """,
#                 unsafe_allow_html=True
#             )
#         else:
#             # Standard format with AI Reasoning and box
#             data_point_colored = rec['data_point'].replace(
#                 f"({rec['data_point'].split('(')[-1]}",
#                 f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
#             ).replace(")", "</span>)")

#             reasoning_colored = rec['reasoning'].replace(
#                 f"({rec['reasoning'].split('(')[-1]}",
#                 f"(<span style='color: red;'>{rec['reasoning'].split('(')[-1]}"
#             ).replace(")", "</span>)")

#             st.markdown(
#                 f"""
#                 <div style="
#                     background: #f8f9fa;
#                     border: 1px solid #dee2e6;
#                     border-radius: 8px;
#                     padding: 15px;
#                     margin: 10px 0;
#                     box-shadow: 0 2px 8px rgba(0,0,0,0.05);
#                 ">
#                     <div style="
#                         font-weight: bold;
#                         background: #e9ecef;
#                         padding: 8px;
#                         border-radius: 5px;
#                         margin-bottom: 8px;
#                         border-left: 4px solid #495057;
#                     ">
#                         AI Recommendation
#                     </div>
#                     <div style="padding: 8px 0;">
#                         <strong>Action:</strong> {rec['action']}
#                     </div>
#                     <div style="
#                         padding: 8px;
#                         background: #f1f1f1;
#                         border-radius: 5px;
#                         margin: 8px 0;
#                     ">
#                         <strong>Data Point:</strong> {data_point_colored}
#                     </div>
#                     <div style="
#                         padding: 8px;
#                         background: #f1f1f1;
#                         border-radius: 5px;
#                     ">
#                         <strong>AI Reasoning:</strong> {reasoning_colored}
#                     </div>
#                 </div>
#                 """,
#                 unsafe_allow_html=True
#             )

#     if not ai_recommendations:
#         st.info(
#             "No specific data points available for AI recommendations. "
#             "Ensure relevant columns are present (hour, shift, operator, duration, speed)."
#         )

# # ================= FOOTER ===========================
# st.markdown("---")
# st.markdown(
#     '<div class="footer">FatigueAnalyzer - Transforming Mining Safety with Intelligent Analytics | Contact: info@bukittechnology.com</div>',
#     unsafe_allow_html=True
# )

# =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")

# Membagi tampilan menjadi dua kolom
col_insights, col_recs = st.columns(2)

# =====================================================================
# 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS (TANPA SEMUA KOTAK BIRU)
# =====================================================================
with col_insights:
    st.subheader("Insights by Advanced Analytics")

    # ===================== 1. Critical Hour Analysis =====================
    critical_hours = [2, 3, 4, 5]
    critical_alerts = df[df['hour'].isin(critical_hours)]
    critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0

    st.markdown(f"**Critical Hour Risk (3-6 AM)**")
    bg_color = (
        "#ffcccc" if critical_pct > 50 else
        "#ffebcc" if critical_pct > 25 else
        "#ffffcc" if critical_pct > 10 else
        "#e6ffe6"
    )
    st.markdown(
        f'<div style="background-color: {bg_color}; padding: 10px; border-radius: 5px;">'
        f'Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)</div>',
        unsafe_allow_html=True
    )

    if critical_pct > 10:
        st.warning(
            f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM). "
            f"This is a known circadian dip period."
        )
    else:
        st.info(
            f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range."
        )

    # ===================== 2. High-Speed Fatigue Analysis =====================
    if col_speed and col_speed in df.columns:
        high_speed_threshold = 20
        high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
        high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

        st.markdown(f"**High-Speed Fatigue Risk (Speed > {high_speed_threshold} km/h)**")
        st.markdown(
            f"""
            <div style="font-size: 24px; font-weight: bold;">{len(high_speed_fatigue)}</div>
            <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {high_speed_pct:.1f}% of total alerts</div>
            """,
            unsafe_allow_html=True
        )

        if high_speed_pct > 20:
            st.warning(
                f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. "
                f"This increases accident severity potential."
            )
        else:
            st.info(
                f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range."
            )
    else:
        st.info("Speed data not available for High-Speed Fatigue Analysis.")

    # ===================== 3. Shift Pattern Analysis =====================
    if col_shift and col_shift in df.columns:
        shift_counts = df[col_shift].value_counts()
        st.markdown(f"**Shift Pattern Risk**")

        for shift_val in shift_counts.index:
            shift_pct = (shift_counts[shift_val] / len(df)) * 100

            st.markdown(
                f"""
                <div style="font-size: 24px; font-weight: bold;">{shift_counts[shift_val]}</div>
                <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {shift_pct:.1f}% of total alerts</div>
                """,
                unsafe_allow_html=True
            )

            if shift_pct > 50:
                st.warning(
                    f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). "
                    f"Review shift scheduling and workload."
                )
            else:
                st.info(
                    f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%)."
                )
    else:
        st.info("Shift data not available for Shift Pattern Analysis.")

    # ===================== 4. Operator Risk Profiling =====================
    if col_operator and col_operator in df.columns:
        operator_alerts = df[col_operator].value_counts()
        top_risk_operators = operator_alerts.head(5)

        st.markdown("**High-Risk Operator Identification**")
        colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]

        for idx, (op_name, count) in enumerate(top_risk_operators.items()):
            op_pct = (count / len(df)) * 100
            color = colors[idx] if idx < len(colors) else colors[-1]

            st.markdown(
                f"**Operator:** {op_name}  \n**Alerts:** {count}"
            )
            st.markdown(
                f"<span style='font-weight:600'>Share:</span> "
                f"<span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
                unsafe_allow_html=True
            )

            if op_pct > 5:
                st.warning(
                    f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%). "
                    f"Consider coaching or rest plan."
                )
            else:
                # ❌ HILANGKAN TEKS "is within acceptable range" DAN KOTAK BIRU
                # Hanya tampilkan nama operator + alert count — tanpa tambahan teks
                st.markdown(
                    f"<span style='color: #2c3e50;'>Operator {op_name}: {count} alerts ({op_pct:.1f}%)</span>",
                    unsafe_allow_html=True
                )
    else:
        st.info("Operator data not available for Operator Risk Profiling.")

# =====================================================================
# 🔹 KOLOM KANAN — AI RECOMMENDATIONS (TIDAK BERUBAH)
# =====================================================================
with col_recs:
    st.subheader("Recommendations")

    ai_recommendations = []

    # 1. Critical Hour Insight → AI Rec
    if "hour" in df.columns and not df.empty:
        peak_hour = df["hour"].value_counts().idxmax()
        critical_hours = [2, 3, 4, 5]

        if peak_hour in critical_hours:
            ai_recommendations.append({
                "type": "critical_hour",
                "action": "Deploy enhanced fatigue monitoring systems during 3-6 AM.",
                "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}%)",
                "reasoning": "High percentage of alerts during circadian low period."
            })
        else:
            ai_recommendations.append({
                "type": "critical_hour",
                "action": "Monitor fatigue patterns around peak hour (Hour {peak_hour}).",
                "data_point": f"Peak Hour: {peak_hour}:00 — {df['hour'].value_counts()[peak_hour]} alerts",
                "reasoning": "This hour shows highest fatigue occurrence."
            })

    # 2. High-Speed Insight → AI Rec
    if col_speed and col_speed in df.columns and not df.empty:
        high_speed_threshold = 20
        high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
        high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0

        if high_speed_pct > 20:
            ai_recommendations.append({
                "type": "high_speed",
                "action": "Implement speed-reduction protocols during fatigue-prone hours.",
                "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
                "reasoning": "High-speed alerts increase accident severity potential."
            })
        else:
            ai_recommendations.append({
                "type": "high_speed",
                "action": "Maintain current speed monitoring — risk level is acceptable.",
                "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
                "reasoning": "Current high-speed fatigue rate is within acceptable range."
            })

    # 3. Shift Pattern Insight → AI Rec
    if col_shift and col_shift in df.columns and not df.empty:
        worst_shift = df[col_shift].value_counts().idxmax()
        shift_pct = (df[col_shift].value_counts()[worst_shift] / len(df)) * 100

        if shift_pct > 50:
            ai_recommendations.append({
                "type": "shift_pattern",
                "action": "Review shift rotation schedules for Shift {worst_shift}.",
                "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
                "reasoning": "Disproportionately high fatigue alerts indicate scheduling imbalance."
            })
        else:
            ai_recommendations.append({
                "type": "shift_pattern",
                "action": "Continue monitoring all shifts — no dominant risk identified.",
                "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
                "reasoning": "Shift distribution is balanced."
            })

    # 4. Operator Risk Profiling → Simple Recommendations (No AI Reasoning, No Box)
    if col_operator and col_operator in df.columns and not df.empty:
        top_operators = df[col_operator].value_counts().head(5)
        for op_name, count in top_operators.items():
            op_pct = (count / len(df)) * 100

            if op_pct > 5:
                ai_recommendations.append({
                    "type": "operator",
                    "action": f"Coaching or mandatory rest for Operator {op_name}.",
                    "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
                })
            else:
                ai_recommendations.append({
                    "type": "operator",
                    "action": f"Continue general monitoring for Operator {op_name}.",
                    "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
                })

    # Render each recommendation based on type
    for rec in ai_recommendations:
        if rec["type"] == "operator":
            # Simple format: Action + Data Point only
            data_point_colored = rec['data_point'].replace(
                f"({rec['data_point'].split('(')[-1]}",
                f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
            ).replace(")", "</span>)")

            st.markdown(
                f"""
                <div style="margin: 10px 0; padding: 10px; background: #f8f9fa; border-left: 4px solid #495057; border-radius: 5px;">
                    <strong>Action:</strong> {rec['action']}<br>
                    <strong>Data Point:</strong> {data_point_colored}
                </div>
                """,
                unsafe_allow_html=True
            )
        else:
            # Standard format with AI Reasoning and box
            data_point_colored = rec['data_point'].replace(
                f"({rec['data_point'].split('(')[-1]}",
                f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
            ).replace(")", "</span>)")

            reasoning_colored = rec['reasoning'].replace(
                f"({rec['reasoning'].split('(')[-1]}",
                f"(<span style='color: red;'>{rec['reasoning'].split('(')[-1]}"
            ).replace(")", "</span>)")

            st.markdown(
                f"""
                <div style="
                    background: #f8f9fa;
                    border: 1px solid #dee2e6;
                    border-radius: 8px;
                    padding: 15px;
                    margin: 10px 0;
                    box-shadow: 0 2px 8px rgba(0,0,0,0.05);
                ">
                    <div style="
                        font-weight: bold;
                        background: #e9ecef;
                        padding: 8px;
                        border-radius: 5px;
                        margin-bottom: 8px;
                        border-left: 4px solid #495057;
                    ">
                        AI Recommendation
                    </div>
                    <div style="padding: 8px 0;">
                        <strong>Action:</strong> {rec['action']}
                    </div>
                    <div style="
                        padding: 8px;
                        background: #f1f1f1;
                        border-radius: 5px;
                        margin: 8px 0;
                    ">
                        <strong>Data Point:</strong> {data_point_colored}
                    </div>
                    <div style="
                        padding: 8px;
                        background: #f1f1f1;
                        border-radius: 5px;
                    ">
                        <strong>AI Reasoning:</strong> {reasoning_colored}
                    </div>
                </div>
                """,
                unsafe_allow_html=True
            )

    if not ai_recommendations:
        st.info(
            "No specific data points available for AI recommendations. "
            "Ensure relevant columns are present (hour, shift, operator, duration, speed)."
        )

# ================= FOOTER ===========================
st.markdown("---")
st.markdown(
    '<div class="footer">FatigueAnalyzer - Transforming Mining Safety with Intelligent Analytics | Contact: info@bukittechnology.com</div>',
    unsafe_allow_html=True
)