Update app.py

app.py

@@ -1155,9 +1155,9 @@ except Exception as e:
 
 # ... (kode sebelumnya tetap sama) ...
 
-# =================== OBJECTIVE 5: Operator Fatigue Risk Gradient Dashboard =====================
 st.subheader("OBJECTIVE 5: See your team’s fatigue risk gradient at a glance!")
-# Custom CSS untuk tampilan ala market saham yang sangat fancy dan profesional
+
+# Custom CSS — tetap seperti sebelumnya (sudah sesuai preferensi)
 st.markdown("""
 <style>
     .big-title {
@@ -1283,7 +1283,6 @@ if df.empty:
     st.info("No data available after applying filters.")
 else:
     try:
-        # Validasi kolom
         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.")
@@ -1294,9 +1293,8 @@ else:
             st.info("No operator data after filtering.")
             st.stop()
 
-        # Pastikan col_operator bukan None sebelum digunakan
         if col_operator is None:
-            st.error(f"Operator column could not be auto-detected. Please check your data.")
+            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")
@@ -1309,107 +1307,81 @@ else:
         ob_data = df_op[df_op["is_ob"]]
         coal_data = df_op[df_op["is_coal"]]
 
-        # Fungsi hitung top 10 (untuk bar chart) - berdasarkan weekly avg events tertinggi
         def get_top10_with_slope(data):
             if data.empty:
-                st.warning("Data is empty in get_top10_with_slope.")
                 return pd.DataFrame()
-            # Pastikan col_operator tidak None dan ada di data
-            if col_operator is None or col_operator not in data.columns:
-                st.error(f"Operator column '{col_operator}' not found in data subset for get_top10.")
+            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 = []
-            try:
-                for nik, grp in weekly.groupby(col_operator):
-                    # Lewati jika nik adalah None
-                    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
-                    metrics.append({
-                        col_operator: nik,
-                        "weekly_avg": weekly_avg,
-                        "slope": slope,
-                        "total_events": total_events,
-                        "n_weeks": n_weeks
-                    })
-            except KeyError as e:
-                st.error(f"KeyError in get_top10_with_slope: {e}. This might happen if the operator column contains invalid data types or unexpected values.")
-                return pd.DataFrame()
-            # Ambil top 10 berdasarkan weekly_avg (descending order)
+            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:
-                st.warning("No valid operator data found for slope calculation in get_top10.")
                 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)
 
-        # Fungsi hitung semua operator (untuk summary)
         def get_all_operators_with_slope(data):
             if data.empty:
-                st.warning("Data is empty in get_all_operators_with_slope.")
                 return pd.DataFrame()
-            # Pastikan col_operator tidak None dan ada di data
-            if col_operator is None or col_operator not in data.columns:
-                st.error(f"Operator column '{col_operator}' not found in data subset for get_all.")
+            if col_operator not in data.columns:
                 return pd.DataFrame()
 
             weekly = data.groupby([col_operator, "year_week"]).size().reset_index(name="weekly_sum")
             metrics = []
-            try:
-                for nik, grp in weekly.groupby(col_operator):
-                    # Lewati jika nik adalah None
-                    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
-                    metrics.append({
-                        col_operator: nik,
-                        "weekly_avg": weekly_avg,
-                        "slope": slope,
-                        "total_events": total_events,
-                        "n_weeks": n_weeks
-                    })
-            except KeyError as e:
-                st.error(f"KeyError in get_all_operators_with_slope: {e}. This might happen if the operator column contains invalid data types or unexpected values.")
-                return pd.DataFrame()
-            if not metrics:
-                st.warning("No valid operator data found for slope calculation in get_all.")
-                return pd.DataFrame()
-            return pd.DataFrame(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 DI LUAR CHART - 3 KOTAK DENGAN UKURAN SAMA
+        # LEGEND — UPDATED: Stable → One Time Event, Gray → Yellow
         # ===============================================================
         st.subheader("Risk Gradient Legend")
         st.markdown("""
@@ -1422,50 +1394,50 @@ else:
                 </div>
                 <div class="legend-item">
                     <div class="legend-color" style="background-color: #e57373;"></div>
-                    <span>High Risk (1.0-1.5)</span>
+                    <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>
+                    <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>
+                    <span>Slight Risk (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>
+                    <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>
+                    <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>
+                    <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 (-0.5-0)</span>
+                    <span>Slight Improvement (−0.5 to 0)</span>
                 </div>
             </div>
             <div class="legend-box">
-                <div class="legend-title">Stable Trend (Zero Slope):</div>
+                <div class="legend-title">One-Time Events (Zero Slope):</div>
                 <div class="legend-item">
-                    <div class="legend-color" style="background-color: #95a5a6;"></div>
-                    <span>Stable (0)</span>
+                    <div class="legend-color" style="background-color: #FFD700;"></div>
+                    <span>One Time Event (0)</span>
                 </div>
                 <br>
-                <i>Note: Only appears when operator data shows consistent behavior within a single week observation period.</i>
+                <i>Note: Applies when an operator has data in only one week — slope is set to 0 by definition.</i>
             </div>
         </div>
         """, unsafe_allow_html=True)
 
         # ===============================================================
-        # PLOT FUNCTION (Bar Chart with Risk Gradient Colors) - PERBAIKAN DI SINI
+        # PLOT FUNCTION — UPDATED: color for slope=0 is now #FFD700
         # ===============================================================
         def plot_chart(data, title):
             if data.empty:
@@ -1476,41 +1448,35 @@ else:
                     showarrow=False,
                     font_size=16
                 )
-                # Gunakan update_layout untuk menetapkan judul
-                fig.update_layout(height=350, title=title)
+                fig.update_layout(height=350, title=dict(text=title, x=0.5))
                 return fig
 
-            # Urutkan data berdasarkan weekly_avg dari besar ke kecil
             data_sorted = data.sort_values('weekly_avg', ascending=False)
 
-            # Kategorisasi warna berdasarkan slope dengan gradasi yang berbeda
             def get_color(slope):
                 if slope == 0:
-                    return "#95a5a6"  # Abu-abu (Stabil)
+                    return "#FFD700"  # ✅ Kuning untuk One Time Event
                 elif slope > 0:
-                    # Gradasi merah untuk slope positif
                     if slope < 0.5:
-                        return "#ffcdd2"  # Merah sangat muda
+                        return "#ffcdd2"
                     elif slope < 1.0:
-                        return "#ef9a9a"  # Merah muda
+                        return "#ef9a9a"
                     elif slope < 1.5:
-                        return "#e57373"  # Merah sedang
+                        return "#e57373"
                     else:
-                        return "#d32f2f"  # Merah gelap
+                        return "#d32f2f"
                 else:  # slope < 0
-                    # Gradasi hijau untuk slope negatif
                     if slope > -0.5:
-                        return "#c8e6c9"  # Hijau sangat muda
+                        return "#c8e6c9"
                     elif slope > -1.0:
-                        return "#a5d6a7"  # Hijau muda
+                        return "#a5d6a7"
                     elif slope > -1.5:
-                        return "#81c784"  # Hijau sedang
+                        return "#81c784"
                     else:
-                        return "#388e3c"  # Hijau gelap
+                        return "#388e3c"
 
             colors = [get_color(s) for s in data_sorted["slope"]]
 
-            # Buat trace bar, TANPA argumen 'title'
             bar_trace = go.Bar(
                 x=data_sorted[col_operator].astype(str),
                 y=data_sorted["weekly_avg"],
@@ -1531,13 +1497,9 @@ else:
                 customdata=np.stack([data_sorted["slope"], data_sorted["total_events"], data_sorted["n_weeks"]], axis=-1)
             )
 
-            # Buat figure dan tambahkan trace
             fig = go.Figure(bar_trace)
-
-            # Gunakan update_layout untuk menetapkan judul dan layout lainnya
             fig.update_layout(
-                title=f"<b>{title}</b>",
-                title_x=0.5, # Pusatkan judul
+                title=dict(text=f"<b>{title}</b>", x=0.5),
                 height=450,
                 margin=dict(l=50, r=20, t=60, b=120),
                 xaxis_title="<b>Operator ID</b>",
@@ -1545,12 +1507,13 @@ else:
                 font=dict(family="Segoe UI", size=12),
                 bargap=0.3,
                 plot_bgcolor="rgba(0,0,0,0)",
-                paper_bgcolor="rgba(0,0,0,0)"
+                paper_bgcolor="rgba(0,0,0,0)",
+                xaxis=dict(tickangle=45)
             )
             return fig
 
         # ===============================================================
-        # TAMPILKAN BAR CHART
+        # CHARTS
         # ===============================================================
         col1, col2 = st.columns(2)
         with col1:
@@ -1559,58 +1522,58 @@ else:
             st.plotly_chart(plot_chart(top_coal, "HAULING COAL Operators (Risk Gradient)"), use_container_width=True)
 
         # ===============================================================
-        # AI INSIGHTS - DIBEDAKAN UNTUK OB HAULER DAN COAL HAULING - SEKARANG BERSEBELAHAN
+        # AI INSIGHTS — tetap dalam bahasa Inggris, tanpa emoticon
         # ===============================================================
-        # st.markdown("---")
-        # st.subheader("Data Insight Automation")
-
-        # Gunakan kolom untuk menampilkan analisis secara bersebelahan
         col_insight1, col_insight2 = st.columns(2)
 
-        # Insight untuk OB HAULER - Ditampilkan di kolom kiri
         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, indicating potential fatigue issues in this fleet type.")
+                    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>, suggesting effective fatigue management strategies.")
-                ob_insights.append(f"Average risk level among top 10 operators is {ob_avg_risk:.2f} events per week with maximum {ob_max_risk:.2f}.")
+                    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 Analysis</div>
+                        <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.")
 
-        # Insight untuk HAULING COAL - Ditampilkan di kolom kanan
         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, requiring immediate attention.")
+                    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>, indicating positive trends in safety management.")
-                coal_insights.append(f"Average risk level among top 10 operators is {coal_avg_risk:.2f} events per week with maximum {coal_max_risk:.2f}.")
+                    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 Analysis</div>
+                        <div class="ai-insight-title">Risk Summary</div>
                         <p>{insight}</p>
                     </div>
                     """, unsafe_allow_html=True)
@@ -1618,83 +1581,82 @@ else:
                 st.info("No HAULING COAL data for analysis.")
 
         # ===============================================================
-        # AI RECOMMENDATIONS - JUGA BERSEBELAHAN
+        # RECOMMENDATIONS
         # ===============================================================
-        # st.markdown("---")
-
-        
-        # st.subheader("Recommendations for Objective 5")
-
-        # Gunakan kolom untuk menampilkan rekomendasi secara bersebelahan
         col_rec1, col_rec2 = st.columns(2)
 
         def generate_recommendations(top_ob, top_coal):
-            recommendations = {}
+            rec = {}
             if not top_ob.empty:
-                ob_worsening = len(top_ob[top_ob['slope'] > 0])
-                ob_avg_risk = top_ob['weekly_avg'].mean()
-                if ob_worsening > 5:  # Lebih dari setengah
-                    recommendations['ob'] = "Implement immediate fatigue monitoring protocols for operators showing worsening trends."
-                    reason_ob = "High percentage of operators showing increasing risk trends indicates potential systemic fatigue issues requiring immediate intervention."
-                elif ob_avg_risk > 10:  # High average risk
-                    recommendations['ob'] = "Consider workload redistribution to reduce average risk levels."
-                    reason_ob = "High average risk levels suggest operational adjustments are needed to maintain optimal safety standards."
+                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:
-                    recommendations['ob'] = "Continue current safety protocols with enhanced monitoring for early detection."
-                    reason_ob = "Stable risk profile indicates current protocols are effective, but continuous monitoring ensures early detection of potential issues."
-                recommendations['ob_reason'] = reason_ob
+                    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:
-                coal_worsening = len(top_coal[top_coal['slope'] > 0])
-                coal_avg_risk = top_coal['weekly_avg'].mean()
-                if coal_worsening > 5:  # Lebih dari setengah
-                    recommendations['coal'] = "Implement immediate fatigue monitoring protocols for operators showing worsening trends."
-                    reason_coal = "High percentage of operators showing increasing risk trends indicates potential systemic fatigue issues requiring immediate intervention."
-                elif coal_avg_risk > 10:  # High average risk
-                    recommendations['coal'] = "Consider workload redistribution to reduce average risk levels."
-                    reason_coal = "High average risk levels suggest operational adjustments are needed to maintain optimal safety standards."
+                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:
-                    recommendations['coal'] = "Continue current safety protocols with enhanced monitoring for early detection."
-                    reason_coal = "Stable risk profile indicates current protocols are effective, but continuous monitoring ensures early detection of potential issues."
-                recommendations['coal_reason'] = reason_coal
+                    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
 
-            return recommendations
+        ai_rec = generate_recommendations(top_ob, top_coal)
 
-        ai_recommendations = generate_recommendations(top_ob, top_coal)
-
-        # Recommendation untuk OB HAULER - Ditampilkan di kolom kiri
         with col_rec1:
-            if 'ob' in ai_recommendations:
+            if 'ob' in ai_rec:
                 st.markdown("### OB HAULER Recommendations")
                 st.markdown(f"""
-                <div style="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); height: 180px; display: flex; flex-direction: column; justify-content: space-between;">
-                    <div style="font-weight: bold; font-size: 14px; background: rgba(255,255,255,0.2); padding: 8px; border-radius: 5px; border-left: 4px solid white;">Recommendation</div>
-                    <div style="padding-top: 8px; font-size: 14px;">{ai_recommendations['ob']}</div>
-                    <div style="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);">AI Reasoning: {ai_recommendations['ob_reason']}</div>
+                <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 generated.")
+                st.info("No OB HAULER recommendations.")
 
-        # Recommendation untuk HAULING COAL - Ditampilkan di kolom kanan
         with col_rec2:
-            if 'coal' in ai_recommendations:
+            if 'coal' in ai_rec:
                 st.markdown("### HAULING COAL Recommendations")
                 st.markdown(f"""
-                <div style="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); height: 180px; display: flex; flex-direction: column; justify-content: space-between;">
-                    <div style="font-weight: bold; font-size: 14px; background: rgba(255,255,255,0.2); padding: 8px; border-radius: 5px; border-left: 4px solid white;">Recommendation</div>
-                    <div style="padding-top: 8px; font-size: 14px;">{ai_recommendations['coal']}</div>
-                    <div style="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);">AI Reasoning: {ai_recommendations['coal_reason']}</div>
+                <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 generated.")
+                st.info("No HAULING COAL recommendations.")
 
     except Exception as e:
         st.error(f"Error in Top 10 Operator analysis: {str(e)}")
-        st.code(f"Error: {e}", language="python")
-
-
+        st.exception(e)  # optionally show full traceback during dev
 
 
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================