Update app.py

app.py

@@ -1156,80 +1156,46 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
 
-# =================== OBJECTIVE 5: Operator Fatigue Risk Gradient Dashboard =====================
-st.markdown("""
-<h2 class="objective-header">OBJECTIVE 5: See Your Team’s Fatigue Hazard Profile!</h2>
-""", unsafe_allow_html=True)
+st.subheader("OBJECTIVE 5:See your team’s fatigue Fatigue Hazard Profile!")
 
-# ✅ CUSTOM CSS — RESPONSIVE & SESUAI PREFERENSI
+# Custom CSS — tetap seperti sebelumnya (sudah sesuai preferensi)
 st.markdown("""
 <style>
-    /* Responsiveness: base font & container */
-    @media (max-width: 768px) {
-        .objective-header { font-size: 22px; padding: 12px; }
-        .big-title { font-size: 20px; padding: 10px; }
-        .legend-container { flex-direction: column; gap: 15px; }
-        .legend-box { min-width: 100% !important; max-width: none; }
-        .ai-insight-box, .recommendation-box { padding: 14px; }
-    }
-    @media (max-width: 480px) {
-        .objective-header { font-size: 20px; }
-        .legend-item span { font-size: 12px; }
-        .legend-note { font-size: 11px; }
-    }
-
-    /* === OBJECTIVE HEADER === */
-    .objective-header {
-        font-size: 26px;
+    .big-title {
+        font-size: 28px;
         font-weight: bold;
-        color: #2c3e50;
+        color: #ffffff;
         text-align: center;
-        margin: 10px 0 25px 0;
-        background: white;
-        padding: 14px;
+        margin-bottom: 10px;
+        background: linear-gradient(135deg, #2c3e50, #1a252c);
+        padding: 15px;
         border-radius: 10px;
-        box-shadow: 0 3px 12px rgba(0,0,0,0.1);
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.3);
     }
-
-    /* === BIG TITLE === */
-    .big-title {
-        font-size: 22px;
-        font-weight: bold;
-        color: #2c3e50;
+    .subnote {
+        font-size: 16px;
+        color: #7f8c8d;
         text-align: center;
-        margin: 25px 0 15px 0;
-        background: white;
-        padding: 12px;
-        border-radius: 8px;
-        box-shadow: 0 2px 8px rgba(0,0,0,0.08);
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        margin-bottom: 20px;
     }
-
     .section-divider {
         height: 2px;
         background: linear-gradient(to right, #3498db, #2ecc71, #f1c40f, #e74c3c);
-        margin: 25px 0;
+        margin: 20px 0;
     }
-
-    /* === LEGEND — RESPONSIVE FLEXBOX === */
     .legend-container {
         display: flex;
-        gap: 20px;
-        flex-wrap: wrap;
-        justify-content: center;
-        margin: 20px 0;
+        gap: 15px;
+        margin: 15px 0;
     }
     .legend-box {
-        background: #f9f9f9;
+        background: white;
         border: 1px solid #ddd;
-        border-radius: 10px;
-        padding: 16px;
-        min-width: 280px;
+        border-radius: 8px;
+        padding: 15px;
         flex: 1;
-        max-width: 340px;
-        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        min-width: 300px;
+        box-shadow: 0 2px 10px rgba(0,0,0,0.05);
     }
     .legend-title {
         font-weight: bold;
@@ -1243,34 +1209,24 @@ st.markdown("""
         display: flex;
         align-items: center;
         margin: 5px 0;
-        font-size: 13px;
+        font-size: 12px;
     }
     .legend-color {
         width: 18px;
         height: 18px;
         border-radius: 3px;
-        margin-right: 10px;
+        margin-right: 8px;
         border: 1px solid #ccc;
-        flex-shrink: 0;
-    }
-    .legend-note {
-        font-size: 12px;
-        color: #666;
-        margin-top: 12px;
-        font-style: italic;
-        line-height: 1.4;
     }
-
-    /* === AI INSIGHTS === */
     .ai-insight-box {
         background: #f8f9fa;
         border: 1px solid #dee2e6;
         border-radius: 8px;
-        padding: 16px;
-        margin: 12px 0;
+        padding: 15px;
+        margin: 10px 0;
         color: #2c3e50;
         font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        box-shadow: 0 2px 10px rgba(0,0,0,0.05);
+        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
     }
     .ai-insight-title {
         font-weight: bold;
@@ -1282,14 +1238,20 @@ st.markdown("""
         border-radius: 5px;
         border-left: 4px solid #495057;
     }
-
-    /* === RECOMMENDATIONS === */
+    .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: 16px;
-        margin: 12px 0;
+        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);
@@ -1305,49 +1267,18 @@ st.markdown("""
         border-left: 4px solid white;
     }
     .recommendation-reason {
-        font-size: 13px;
+        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);
     }
-
-    /* === TRENDS === */
-    .trend-up { color: #e74c3c; font-weight: bold; }
-    .trend-down { color: #27ae60; font-weight: bold; }
-
-    /* Plotly responsive fix */
-    .js-plotly-plot .plotly > div { max-width: 100% !important; }
 </style>
 """, unsafe_allow_html=True)
 
 # ===============================================================
-# CONTOH DATA (GANTI DENGAN DATA ANDA)
-# ===============================================================
-# Simulasi data — ganti dengan df Anda
-@st.cache_data
-def generate_sample_data():
-    np.random.seed(42)
-    operators = [f"OP{i:03d}" for i in range(1, 51)]
-    fleets = ["OB HAULLER"] * 25 + ["HAULING COAL"] * 25
-    dates = pd.date_range("2025-01-01", "2025-03-31", freq="D")
-    
-    data = []
-    for op, fleet in zip(operators, fleets):
-        n_events = np.random.randint(5, 50)
-        for _ in range(n_events):
-            start = np.random.choice(dates)
-            data.append({"Operator": op, "Fleet_Type": fleet, "start": start})
-    return pd.DataFrame(data)
-
-# Ganti ini dengan df Anda
-df = generate_sample_data()
-col_operator = "Operator"
-col_fleet_type = "Fleet_Type"
-
-# ===============================================================
-# LOGIC UTAMA — RESPONSIVE & SESUAI PREFERENSI
+# LOGIC UTAMA
 # ===============================================================
 if df.empty:
     st.info("No data available after applying filters.")
@@ -1358,21 +1289,15 @@ else:
             st.warning("Required columns (operator, fleet_type, start) are missing.")
             st.stop()
 
-        # ✅ Shorten operator names: "John Doe" → "John"
         df_op = df[[col_operator, col_fleet_type, "start"]].dropna()
-        if col_operator in df_op.columns:
-            df_op[col_operator] = (
-                df_op[col_operator]
-                .astype(str)
-                .str.strip()
-                .str.split()
-                .str[0]
-            )
-
         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
@@ -1384,7 +1309,8 @@ else:
         coal_data = df_op[df_op["is_coal"]]
 
         def get_top10_with_slope(data):
-            if data.empty: return pd.DataFrame()
+            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()
@@ -1392,20 +1318,22 @@ else:
             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
+                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)
-                slope = 0.0
                 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,
@@ -1413,129 +1341,156 @@ else:
                     "total_events": total_events,
                     "n_weeks": n_weeks
                 })
-            return pd.DataFrame(metrics).nlargest(10, "weekly_avg") if metrics else pd.DataFrame()
+            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 — RESPONSIVE & SESUAI PREFERENSI
+        # LEGEND — UPDATED: Stable → One Time Event, Gray → Yellow
         # ===============================================================
-        st.markdown('<h3 class="big-title">Hazard Gradient Legend</h3>', unsafe_allow_html=True)
+        st.subheader("Hazard Gradient Legend")
         st.markdown("""
-<div class="legend-container">
-    <!-- Worsening Trends -->
-    <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 Risk (≥ 1.5)</span>
-        </div>
-        <div class="legend-item">
-            <div class="legend-color" style="background-color: #e57373;"></div>
-            <span>High Risk (1.0 – 1.5)</span>
-        </div>
-        <div class="legend-item">
-            <div class="legend-color" style="background-color: #ef9a9a;"></div>
-            <span>Moderate Risk (0.5 – 1.0)</span>
-        </div>
-        <div class="legend-item">
-            <div class="legend-color" style="background-color: #ffcdd2;"></div>
-            <span>Slight Risk (0 – 0.5)</span>
-        </div>
-        <p class="legend-note">
-            <i>Note: Positive slope indicates increasing fatigue events over time — escalating operational risk.</i>
-        </p>
-    </div>
-
-    <!-- Improving Trends -->
-    <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>
-        <p class="legend-note">
-            <i>Note: Negative slope reflects decreasing fatigue events — effective mitigation or behavioral adaptation.</i>
-        </p>
-    </div>
-
-    <!-- One-Time Events -->
-    <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 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>
+            </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>
+            </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>
+                <br>
+                <i>Note: Applies when an operator has data in only one week — slope is set to 0 by definition.</i>
+            </div>
         </div>
-        <p class="legend-note">
-            <i>Note: Slope = 0 by definition when data exists for only one week — trend assessment not applicable.</i>
-        </p>
-    </div>
-</div>
-""", unsafe_allow_html=True)
+        """, unsafe_allow_html=True)
 
         # ===============================================================
-        # PLOT FUNCTION — RESPONSIVE
+        # PLOT FUNCTION — UPDATED: color for slope=0 is now #FFD700
         # ===============================================================
         def plot_chart(data, title):
             if data.empty:
                 fig = go.Figure()
                 fig.add_annotation(
-                    text="No Data", x=0.5, y=0.5,
+                    text="No Data",
+                    x=0.5, y=0.5,
                     showarrow=False,
-                    font_size=16,
-                    font_color="#888"
-                )
-                fig.update_layout(
-                    height=350,
-                    title=dict(text=title, x=0.5, font=dict(size=18, family="Segoe UI")),
-                    plot_bgcolor="rgba(0,0,0,0)",
-                    paper_bgcolor="rgba(0,0,0,0)",
-                    margin=dict(l=40, r=20, t=60, b=100),
+                    font_size=16
                 )
+                fig.update_layout(height=350, title=dict(text=title, x=0.5))
                 return fig
 
             data_sorted = data.sort_values('weekly_avg', ascending=False)
 
             def get_color(slope):
                 if slope == 0:
-                    return "#FFD700"
+                    return "#FFD700"  # ✅ Kuning untuk One Time Event
                 elif slope > 0:
-                    if slope >= 1.5: return "#d32f2f"
-                    elif slope >= 1.0: return "#e57373"
-                    elif slope >= 0.5: return "#ef9a9a"
-                    else: return "#ffcdd2"
-                else:
-                    if slope <= -1.5: return "#388e3c"
-                    elif slope <= -1.0: return "#81c784"
-                    elif slope <= -0.5: return "#a5d6a7"
-                    else: return "#c8e6c9"
+                    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=1.2, color="rgba(0,0,0,0.2)")),
+                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",
-                textfont=dict(size=11, family="Segoe UI"),
                 hovertemplate=(
                     "<b>%{x}</b><br>" +
                     "Weekly Avg: %{y:.2f}<br>" +
-                    "Trend Slope: %{customdata[0]:+.2f}<br>" +
+                    "Trend Slope: %{customdata[0]:+.3f}<br>" +
                     "Total Events: %{customdata[1]}<br>" +
                     "Weeks Active: %{customdata[2]}<br>" +
                     "<extra></extra>"
@@ -1545,140 +1500,138 @@ else:
 
             fig = go.Figure(bar_trace)
             fig.update_layout(
-                title=dict(text=f"<b>{title}</b>", x=0.5, font=dict(size=18, color="#2c3e50")),
+                title=dict(text=f"<b>{title}</b>", x=0.5),
                 height=450,
                 margin=dict(l=50, r=20, t=60, b=120),
-                xaxis_title=dict(text="<b>Operator</b>", font=dict(family="Segoe UI")),
-                yaxis_title=dict(text="<b>Weekly Avg Events</b>", font=dict(family="Segoe UI")),
+                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,
-                    tickfont=dict(family="Segoe UI", size=11),
-                    automargin=True
-                ),
-                yaxis=dict(gridcolor="#eee")
+                xaxis=dict(tickangle=45)
             )
-            # Responsif: nonaktifkan zoom & pan di mobile
-            fig.update_yaxes(fixedrange=True)
             return fig
 
         # ===============================================================
-        # CHARTS — RESPONSIVE COLUMN
+        # CHARTS
         # ===============================================================
-        if st.session_state.get("is_mobile", False) or st._get_query_params().get("mobile"):
-            # Force single column on mobile
+        col1, col2 = st.columns(2)
+        with col1:
             st.plotly_chart(plot_chart(top_ob, "OB HAULER Operators (Hazard Gradient)"), use_container_width=True)
+        with col2:
             st.plotly_chart(plot_chart(top_coal, "HAULING COAL Operators (Hazard Gradient)"), use_container_width=True)
-        else:
-            col1, col2 = st.columns(2)
-            with col1:
-                st.plotly_chart(plot_chart(top_ob, "OB HAULER Operators (Hazard Gradient)"), use_container_width=True)
-            with col2:
-                st.plotly_chart(plot_chart(top_coal, "HAULING COAL Operators (Hazard Gradient)"), use_container_width=True)
 
         # ===============================================================
-        # AI INSIGHTS — RESPONSIVE
+        # AI INSIGHTS — tetap dalam bahasa Inggris, tanpa emoticon
         # ===============================================================
-        if st.session_state.get("is_mobile", False) or st._get_query_params().get("mobile"):
-            # Single column
+        col_insight1, col_insight2 = st.columns(2)
+
+        with col_insight1:
             if not top_ob.empty:
-                st.markdown('<h3 class="big-title">OB HAULER Analysis</h3>', unsafe_allow_html=True)
-                # ... (same logic as below)
-            if not top_coal.empty:
-                st.markdown('<h3 class="big-title">HAULING COAL Analysis</h3>', unsafe_allow_html=True)
-                # ... 
-        else:
-            col_insight1, col_insight2 = st.columns(2)
-            with col_insight1:
-                if not top_ob.empty:
-                    st.markdown('<h3 class="big-title">OB HAULER Analysis</h3>', unsafe_allow_html=True)
-                    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()
-
-                    insights = []
-                    if ob_worsening > ob_improving:
-                        insights.append(f"{ob_worsening} out of 10 top-risk operators show <span class='trend-up'>worsening</span> trends.")
-                    else:
-                        insights.append(f"{ob_improving} out of 10 top-risk operators show <span class='trend-down'>improvement</span>.")
-                    if ob_one_time > 0:
-                        insights.append(f"{ob_one_time} operator(s) classified as <b>One Time Event</b>.")
-                    insights.append(f"Average risk: {ob_avg_risk:.2f} events/week (max: {ob_max_risk:.2f}).")
-
-                    for txt in insights:
-                        st.markdown(f"""
-                        <div class="ai-insight-box">
-                            <div class="ai-insight-title">Risk Summary</div>
-                            <p>{txt}</p>
-                        </div>
-                        """, unsafe_allow_html=True)
+                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:
-                    st.info("No OB HAULER data for analysis.")
-
-            with col_insight2:
-                if not top_coal.empty:
-                    st.markdown('<h3 class="big-title">HAULING COAL Analysis</h3>', unsafe_allow_html=True)
-                    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()
-
-                    insights = []
-                    if coal_worsening > coal_improving:
-                        insights.append(f"{coal_worsening} out of 10 top-risk operators show <span class='trend-up'>worsening</span> trends.")
-                    else:
-                        insights.append(f"{coal_improving} out of 10 top-risk operators show <span class='trend-down'>improvement</span>.")
-                    if coal_one_time > 0:
-                        insights.append(f"{coal_one_time} operator(s) classified as <b>One Time Event</b>.")
-                    insights.append(f"Average risk: {coal_avg_risk:.2f} events/week (max: {coal_max_risk:.2f}).")
-
-                    for txt in insights:
-                        st.markdown(f"""
-                        <div class="ai-insight-box">
-                            <div class="ai-insight-title">Risk Summary</div>
-                            <p>{txt}</p>
-                        </div>
-                        """, unsafe_allow_html=True)
+                    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).")
+                ob_insights.append(f"Average risk: {ob_avg_risk:.2f} events/week (max: {ob_max_risk:.2f}).")
+
+                for insight in ob_insights:
+                    st.markdown(f"""
+                    <div class="ai-insight-box">
+                        <div class="ai-insight-title">Risk Summary</div>
+                        <p>{insight}</p>
+                    </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:
-                    st.info("No HAULING COAL data for analysis.")
+                    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).")
+                coal_insights.append(f"Average risk: {coal_avg_risk:.2f} events/week (max: {coal_max_risk:.2f}).")
+
+                for insight in coal_insights:
+                    st.markdown(f"""
+                    <div class="ai-insight-box">
+                        <div class="ai-insight-title">Risk Summary</div>
+                        <p>{insight}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+            else:
+                st.info("No HAULING COAL data for analysis.")
 
         # ===============================================================
-        # RECOMMENDATIONS — RESPONSIVE
+        # RECOMMENDATIONS
         # ===============================================================
+        col_rec1, col_rec2 = st.columns(2)
+
         def generate_recommendations(top_ob, top_coal):
             rec = {}
-            for label, data in [("ob", top_ob), ("coal", top_coal)]:
-                if not data.empty:
-                    w = len(data[data['slope'] > 0])
-                    ot = len(data[data['slope'] == 0])
-                    avg = data['weekly_avg'].mean()
-                    if w > 5:
-                        r = "Prioritize fatigue intervention for operators with worsening trends."
-                        reason = "High proportion of deteriorating operators signals emerging fatigue risks."
-                    elif ot > 4:
-                        r = "Validate data completeness — high One Time Event count may indicate reporting gaps."
-                        reason = "Operators with single-week data cannot yield reliable trend analysis."
-                    elif avg > 8:
-                        r = "Review scheduling and rest protocols to reduce event frequency."
-                        reason = "Elevated average event rate increases cumulative fatigue exposure."
-                    else:
-                        r = "Maintain current protocols with targeted monitoring."
-                        reason = "Risk profile is stable; focus on sustaining safe practices."
-                    rec[label] = r
-                    rec[f"{label}_reason"] = reason
+            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()
+                if w > 5:
+                    r = "Prioritize fatigue intervention for operators with worsening trends."
+                    reason = "High proportion of deteriorating operators signals emerging fatigue risks."
+                elif ot > 4:
+                    r = "Validate data completeness — high One Time Event count may indicate reporting gaps."
+                    reason = "Operators with single-week data cannot yield reliable trend analysis."
+                elif avg > 8:
+                    r = "Review scheduling and rest protocols to reduce event frequency."
+                    reason = "Elevated average event rate increases cumulative fatigue exposure."
+                else:
+                    r = "Maintain current protocols with targeted monitoring."
+                    reason = "Risk profile is stable; focus on sustaining safe practices."
+                rec['ob'] = r
+                rec['ob_reason'] = reason
+
+            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()
+                if w > 5:
+                    r = "Prioritize fatigue intervention for operators with worsening trends."
+                    reason = "High proportion of deteriorating operators signals emerging fatigue risks."
+                elif ot > 4:
+                    r = "Validate data completeness — high One Time Event count may indicate reporting gaps."
+                    reason = "Operators with single-week data cannot yield reliable trend analysis."
+                elif avg > 8:
+                    r = "Review scheduling and rest protocols to reduce event frequency."
+                    reason = "Elevated average event rate increases cumulative fatigue exposure."
+                else:
+                    r = "Maintain current protocols with targeted monitoring."
+                    reason = "Risk profile is stable; focus on sustaining safe practices."
+                rec['coal'] = r
+                rec['coal_reason'] = reason
             return rec
 
         ai_rec = generate_recommendations(top_ob, top_coal)
 
-        if st.session_state.get("is_mobile", False) or st._get_query_params().get("mobile"):
+        with col_rec1:
             if 'ob' in ai_rec:
-                st.markdown('<h3 class="big-title">OB HAULER Recommendations</h3>', unsafe_allow_html=True)
+                st.markdown("### OB HAULER Recommendations")
                 st.markdown(f"""
                 <div class="recommendation-box">
                     <div class="recommendation-title">Action Plan</div>
@@ -1686,8 +1639,12 @@ else:
                     <div class="recommendation-reason">AI Reasoning: {ai_rec['ob_reason']}</div>
                 </div>
                 """, unsafe_allow_html=True)
+            else:
+                st.info("No OB HAULER recommendations.")
+
+        with col_rec2:
             if 'coal' in ai_rec:
-                st.markdown('<h3 class="big-title">HAULING COAL Recommendations</h3>', unsafe_allow_html=True)
+                st.markdown("### HAULING COAL Recommendations")
                 st.markdown(f"""
                 <div class="recommendation-box">
                     <div class="recommendation-title">Action Plan</div>
@@ -1695,51 +1652,14 @@ else:
                     <div class="recommendation-reason">AI Reasoning: {ai_rec['coal_reason']}</div>
                 </div>
                 """, unsafe_allow_html=True)
-        else:
-            col_rec1, col_rec2 = st.columns(2)
-            with col_rec1:
-                if 'ob' in ai_rec:
-                    st.markdown('<h3 class="big-title">OB HAULER Recommendations</h3>', unsafe_allow_html=True)
-                    st.markdown(f"""
-                    <div class="recommendation-box">
-                        <div class="recommendation-title">Action Plan</div>
-                        <div>{ai_rec['ob']}</div>
-                        <div class="recommendation-reason">AI Reasoning: {ai_rec['ob_reason']}</div>
-                    </div>
-                    """, unsafe_allow_html=True)
-                else:
-                    st.info("No OB HAULER recommendations.")
-            with col_rec2:
-                if 'coal' in ai_rec:
-                    st.markdown('<h3 class="big-title">HAULING COAL Recommendations</h3>', unsafe_allow_html=True)
-                    st.markdown(f"""
-                    <div class="recommendation-box">
-                        <div class="recommendation-title">Action Plan</div>
-                        <div>{ai_rec['coal']}</div>
-                        <div class="recommendation-reason">AI Reasoning: {ai_rec['coal_reason']}</div>
-                    </div>
-                    """, unsafe_allow_html=True)
-                else:
-                    st.info("No HAULING COAL recommendations.")
+            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)  # Uncomment for debugging
+        st.exception(e)  # optionally show full traceback during dev
+
 
-# ✅ Auto-detect mobile (opsional)
-def detect_mobile():
-    try:
-        from streamlit.runtime.scriptrunner import get_script_run_ctx
-        ctx = get_script_run_ctx()
-        if ctx and ctx.session_id:
-            user_agent = st.context.headers.get("User-Agent", "").lower()
-            return any(x in user_agent for x in ["mobile", "android", "iphone", "ipad"])
-    except:
-        pass
-    return False
-
-if "is_mobile" not in st.session_state:
-    st.session_state.is_mobile = detect_mobile()
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")