Update app.py

app.py

@@ -1155,18 +1155,129 @@ except Exception as e:
 
 # ... (kode sebelumnya tetap sama) ...
 
-# =================== OBJECTIVE 5: Operator Fatigue Risk Gradient Dashboard (DAILY-BASED) =====================
+# =================== OBJECTIVE 5: Operator Fatigue Risk Gradient Dashboard =====================
 st.subheader("OBJECTIVE 5: See your team’s fatigue risk gradient at a glance!")
-
-# Custom CSS (tetap sama — tidak berubah)
+# Custom CSS untuk tampilan ala market saham yang sangat fancy dan profesional
 st.markdown("""
 <style>
-    /* ... CSS tetap persis seperti sebelumnya ... */
+    .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 — DIPERBAIKI: DAILY-BASED SLOPE
+# LOGIC UTAMA
 # ===============================================================
 if df.empty:
     st.info("No data available after applying filters.")
@@ -1183,14 +1294,14 @@ else:
             st.info("No operator data after filtering.")
             st.stop()
 
+        # Pastikan col_operator bukan None sebelum digunakan
         if col_operator is None:
-            st.error("Operator column could not be auto-detected. Please check your data.")
+            st.error(f"Operator column could not be auto-detected. Please check your data.")
             st.stop()
 
-        # ✅ PERBAIKAN 1: Ganti dari year_week → date (harus datetime.date agar bisa diurutkan)
-        df_op["date"] = pd.to_datetime(df_op["start"]).dt.date
+        df_op["year_week"] = df_op["start"].dt.strftime("%Y-W%U")
 
-        # Fuzzy match fleet names (tetap)
+        # 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)
@@ -1198,60 +1309,107 @@ else:
         ob_data = df_op[df_op["is_ob"]]
         coal_data = df_op[df_op["is_coal"]]
 
-        # ✅ PERBAIKAN 2: FUNGSI BARU — Hitung slope berdasarkan DAILY rata-rata
-        def get_top10_daily_slope(data, top_n=10):
-            if data.empty or col_operator is None or col_operator not in data.columns:
+        # 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.")
                 return pd.DataFrame()
 
-            # ✅ Hitung jumlah event per operator per hari
-            daily = data.groupby([col_operator, "date"]).size().reset_index(name="daily_count")
-            
+            weekly = data.groupby([col_operator, "year_week"]).size().reset_index(name="weekly_sum")
             metrics = []
-            for nik, grp in daily.groupby(col_operator):
-                if pd.isna(nik):
-                    continue
-                # Urutkan berdasarkan tanggal (pastikan sorted)
-                grp = grp.sort_values("date")
-                # Ambil hanya tanggal & count
-                dates = pd.to_datetime(grp["date"]).dt.date
-                counts = grp["daily_count"].values
-                n_days = len(counts)
-                
-                # ✅ Rata-rata harian → untuk ranking & Y-axis
-                daily_avg = np.mean(counts)
-                total_events = int(np.sum(counts))
-                
-                # ✅ Slope: regresi linier terhadap urutan hari (0, 1, 2, ...)
-                if n_days >= 2:
-                    x = np.arange(n_days)  # [0, 1, 2, ..., n-1]
-                    y = counts
-                    slope, intercept = np.polyfit(x, y, 1)  # Lebih stabil daripada manual
-                else:
-                    slope = 0.0
-
-                metrics.append({
-                    col_operator: nik,
-                    "daily_avg": daily_avg,
-                    "slope": slope,
-                    "total_events": total_events,
-                    "n_days": n_days
-                })
-            
+            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)
+            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.")
+                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()
-            
-            result_df = pd.DataFrame(metrics)
-            # ✅ Ambil top N berdasarkan daily_avg (descending)
-            return result_df.nlargest(top_n, "daily_avg")
+            return pd.DataFrame(metrics)
 
-        # Panggil fungsi baru
-        top_ob = get_top10_daily_slope(ob_data, top_n=10)
-        top_coal = get_top10_daily_slope(coal_data, top_n=10)
-        all_ob = get_top10_daily_slope(ob_data, top_n=1000)  # semua, tanpa batas
-        all_coal = get_top10_daily_slope(coal_data, top_n=1000)
+        all_ob = get_all_operators_with_slope(ob_data)
+        all_coal = get_all_operators_with_slope(coal_data)
 
         # ===============================================================
-        # LEGEND (tetap sama — sudah sesuai preferensi Anda)
+        # LEGEND DI LUAR CHART - 3 KOTAK DENGAN UKURAN SAMA
         # ===============================================================
         st.subheader("Risk Gradient Legend")
         st.markdown("""
@@ -1264,34 +1422,34 @@ 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 to 0)</span>
+                    <span>Slight Improvement (-0.5-0)</span>
                 </div>
             </div>
             <div class="legend-box">
@@ -1301,66 +1459,89 @@ else:
                     <span>Stable (0)</span>
                 </div>
                 <br>
-                <i>Note: Appears when only one day observed or flat trend.</i>
+                <i>Note: Only appears when operator data shows consistent behavior within a single week observation period.</i>
             </div>
         </div>
         """, unsafe_allow_html=True)
 
         # ===============================================================
-        # PLOT FUNCTION — DIPERBAIKI: daily_avg + tooltip harian
+        # PLOT FUNCTION (Bar Chart with Risk Gradient Colors) - PERBAIKAN DI SINI
         # ===============================================================
-        def plot_daily_risk_chart(data, title):
+        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=title, title_x=0.5)
+                fig.add_annotation(
+                    text="No Data",
+                    x=0.5, y=0.5,
+                    showarrow=False,
+                    font_size=16
+                )
+                # Gunakan update_layout untuk menetapkan judul
+                fig.update_layout(height=350, title=title)
                 return fig
 
-            # Urutkan berdasarkan daily_avg tertinggi
-            data_sorted = data.sort_values('daily_avg', ascending=False)
+            # Urutkan data berdasarkan weekly_avg dari besar ke kecil
+            data_sorted = data.sort_values('weekly_avg', ascending=False)
 
-            # ✅ Warna tetap sesuai preferensi Anda (4-level merah/hijau/abu-abu)
+            # Kategorisasi warna berdasarkan slope dengan gradasi yang berbeda
             def get_color(slope):
                 if slope == 0:
-                    return "#95a5a6"
+                    return "#95a5a6"  # Abu-abu (Stabil)
                 elif slope > 0:
-                    if slope < 0.5: return "#ffcdd2"
-                    elif slope < 1.0: return "#ef9a9a"
-                    elif slope < 1.5: return "#e57373"
-                    else: return "#d32f2f"
+                    # Gradasi merah untuk slope positif
+                    if slope < 0.5:
+                        return "#ffcdd2"  # Merah sangat muda
+                    elif slope < 1.0:
+                        return "#ef9a9a"  # Merah muda
+                    elif slope < 1.5:
+                        return "#e57373"  # Merah sedang
+                    else:
+                        return "#d32f2f"  # Merah gelap
                 else:  # slope < 0
-                    if slope > -0.5: return "#c8e6c9"
-                    elif slope > -1.0: return "#a5d6a7"
-                    elif slope > -1.5: return "#81c784"
-                    else: return "#388e3c"
+                    # Gradasi hijau untuk slope negatif
+                    if slope > -0.5:
+                        return "#c8e6c9"  # Hijau sangat muda
+                    elif slope > -1.0:
+                        return "#a5d6a7"  # Hijau muda
+                    elif slope > -1.5:
+                        return "#81c784"  # Hijau sedang
+                    else:
+                        return "#388e3c"  # Hijau gelap
 
             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["daily_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["daily_avg"]],
+                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>" +
-                    "Daily Avg: %{y:.2f}<br>" +  # ✅ Weekly → Daily
+                    "Weekly Avg: %{y:.2f}<br>" +
                     "Trend Slope: %{customdata[0]:+.3f}<br>" +
                     "Total Events: %{customdata[1]}<br>" +
-                    "Days Active: %{customdata[2]}<br>" +  # ✅ Weeks → Days
+                    "Weeks Active: %{customdata[2]}<br>" +
                     "<extra></extra>"
                 ),
-                customdata=np.stack([data_sorted["slope"], data_sorted["total_events"], data_sorted["n_days"]], axis=-1)
+                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,
+                title_x=0.5, # Pusatkan judul
                 height=450,
                 margin=dict(l=50, r=20, t=60, b=120),
                 xaxis_title="<b>Operator ID</b>",
-                yaxis_title="<b>Daily Avg Events</b>",  # ✅ Weekly → Daily
+                yaxis_title="<b>Weekly Avg Events</b>",
                 font=dict(family="Segoe UI", size=12),
                 bargap=0.3,
                 plot_bgcolor="rgba(0,0,0,0)",
@@ -1369,32 +1550,37 @@ else:
             return fig
 
         # ===============================================================
-        # TAMPILKAN BAR CHART — SUDAH HARIAN
+        # TAMPILKAN BAR CHART
         # ===============================================================
         col1, col2 = st.columns(2)
         with col1:
-            st.plotly_chart(plot_daily_risk_chart(top_ob, "OB HAULER Operators (Risk Gradient)"), use_container_width=True)
+            st.plotly_chart(plot_chart(top_ob, "OB HAULER Operators (Risk Gradient)"), use_container_width=True)
         with col2:
-            st.plotly_chart(plot_daily_risk_chart(top_coal, "HAULING COAL Operators (Risk Gradient)"), use_container_width=True)
+            st.plotly_chart(plot_chart(top_coal, "HAULING COAL Operators (Risk Gradient)"), use_container_width=True)
 
         # ===============================================================
-        # AI INSIGHTS — DIPERBAIKI: Weekly → Daily
+        # AI INSIGHTS - DIBEDAKAN UNTUK OB HAULER DAN COAL HAULING - SEKARANG BERSEBELAHAN
         # ===============================================================
+        # 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_avg_risk = top_ob['daily_avg'].mean()  # ✅ weekly_avg → daily_avg
-                ob_max_risk = top_ob['daily_avg'].max()   # ✅ weekly_avg → daily_avg
+                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.")
                 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 day with maximum {ob_max_risk:.2f}.")
+                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}.")
 
                 for insight in ob_insights:
                     st.markdown(f"""
@@ -1406,19 +1592,20 @@ else:
             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_avg_risk = top_coal['daily_avg'].mean()  # ✅ weekly_avg → daily_avg
-                coal_max_risk = top_coal['daily_avg'].max()   # ✅ weekly_avg → daily_avg
+                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.")
                 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 day with maximum {coal_max_risk:.2f}.")
+                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}.")
 
                 for insight in coal_insights:
                     st.markdown(f"""
@@ -1431,45 +1618,51 @@ else:
                 st.info("No HAULING COAL data for analysis.")
 
         # ===============================================================
-        # AI RECOMMENDATIONS — DIPERBAIKI: threshold berdasarkan daily_avg
+        # AI RECOMMENDATIONS - JUGA BERSEBELAHAN
         # ===============================================================
+        # 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_daily(top_ob, top_coal):
+        def generate_recommendations(top_ob, top_coal):
             recommendations = {}
             if not top_ob.empty:
                 ob_worsening = len(top_ob[top_ob['slope'] > 0])
-                ob_avg_risk = top_ob['daily_avg'].mean()
-                # ✅ threshold disesuaikan: daily avg > 2 = high (misal: >2 event/hari = perlu perhatian)
-                if ob_worsening > 5:
-                    recommendations['ob'] = "Implement immediate fatigue monitoring protocols for operators showing worsening daily trends."
-                    reason_ob = "High percentage of operators with increasing daily fatigue events suggests emerging risk patterns."
-                elif ob_avg_risk > 2.0:
-                    recommendations['ob'] = "Consider workload redistribution to reduce daily fatigue exposure."
-                    reason_ob = f"Average daily fatigue events ({ob_avg_risk:.2f}) exceeds safe threshold (2.0 events/day)."
+                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."
                 else:
-                    recommendations['ob'] = "Continue current safety protocols with enhanced daily monitoring."
-                    reason_ob = "Stable daily fatigue profile — maintain vigilance for early trend shifts."
+                    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
 
             if not top_coal.empty:
                 coal_worsening = len(top_coal[top_coal['slope'] > 0])
-                coal_avg_risk = top_coal['daily_avg'].mean()
-                if coal_worsening > 5:
-                    recommendations['coal'] = "Implement immediate fatigue monitoring protocols for operators showing worsening daily trends."
-                    reason_coal = "High percentage of operators with increasing daily fatigue events requires urgent intervention."
-                elif coal_avg_risk > 2.0:
-                    recommendations['coal'] = "Review task allocation to reduce daily fatigue burden."
-                    reason_coal = f"Average daily fatigue events ({coal_avg_risk:.2f}) exceeds safe threshold (2.0 events/day)."
+                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."
                 else:
-                    recommendations['coal'] = "Continue current safety protocols with enhanced daily monitoring."
-                    reason_coal = "Stable daily fatigue profile — maintain proactive monitoring."
+                    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
 
             return recommendations
 
-        ai_recommendations = generate_recommendations_daily(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:
                 st.markdown("### OB HAULER Recommendations")
@@ -1483,6 +1676,7 @@ else:
             else:
                 st.info("No OB HAULER recommendations generated.")
 
+        # Recommendation untuk HAULING COAL - Ditampilkan di kolom kanan
         with col_rec2:
             if 'coal' in ai_recommendations:
                 st.markdown("### HAULING COAL Recommendations")
@@ -1497,11 +1691,12 @@ else:
                 st.info("No HAULING COAL recommendations generated.")
 
     except Exception as e:
-        st.error(f"Error in Daily Operator Fatigue Analysis: {str(e)}")
+        st.error(f"Error in Top 10 Operator analysis: {str(e)}")
         st.code(f"Error: {e}", language="python")
 
 
 
+
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")