Update app.py

app.py

@@ -1158,12 +1158,11 @@ import numpy as np
 import plotly.graph_objects as go
 
 # =================== OBJECTIVE 5: Operator Fatigue Risk Gradient Dashboard =====================
-
 # ... (kode sebelumnya tetap sama) ...
 
-st.subheader("OBJECTIVE 5:See your team’s fatigue Fatigue Hazard Profile!")
+st.subheader("OBJECTIVE 5: See your team’s fatigue Fatigue Hazard Profile!")
 
-# Custom CSS — tetap seperti sebelumnya (sudah sesuai preferensi)
+# ✅ Custom CSS — diperbarui dengan styling list untuk rekomendasi
 st.markdown("""
 <style>
     .big-title {
@@ -1264,20 +1263,30 @@ st.markdown("""
     .recommendation-title {
         font-weight: bold;
         color: white;
-        margin-bottom: 8px;
+        margin-bottom: 12px;
         font-size: 14px;
         background: rgba(255,255,255,0.2);
         padding: 8px;
         border-radius: 5px;
         border-left: 4px solid white;
     }
+    .recommendation-box ul {
+        padding-left: 24px;
+        margin: 12px 0;
+        line-height: 1.6;
+        font-size: 14px;
+    }
+    .recommendation-box ul li {
+        margin-bottom: 8px;
+    }
     .recommendation-reason {
         font-size: 12px;
-        margin-top: 10px;
+        margin-top: 12px;
         padding: 8px;
-        background: rgba(255,255,255,0.1);
+        background: rgba(255,255,255,0.15);
         border-radius: 5px;
-        border-left: 3px solid rgba(255,255,255,0.3);
+        border-left: 3px solid rgba(255,255,255,0.4);
+        font-style: italic;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -1411,7 +1420,7 @@ else:
                     <div class="legend-color" style="background-color: #ffcdd2;"></div>
                     <span>Slight Worsening (0–0.5)</span>
                 </div>
-                                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
+                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
                     Note: Positive slope indicates increasing fatigue event frequency over weeks.
                 </i>
             </div>
@@ -1432,12 +1441,11 @@ else:
                 <div class="legend-item">
                     <div class="legend-color" style="background-color: #c8e6c9;"></div>
                     <span>Slight Improvement (−0.5 to 0)</span>
-                    </div>
-                                    <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
+                </div>
+                <i style="display: block; margin-top: 12px; font-size: 12px; color: #666; font-style: italic;">
                     Note: Negative slope reflects a consistent decline in fatigue events.
                 </i>
-                </div>
-            <!-- One-Time Events -->
+            </div>
             <div class="legend-box">
                 <div class="legend-title">One-Time Events (Zero Slope):</div>
                 <div class="legend-item">
@@ -1451,81 +1459,81 @@ else:
         </div>
         """, unsafe_allow_html=True)
 
-# ===============================================================
-# 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,
-            showarrow=False,
-            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"  # ✅ Kuning untuk One Time Event
-        elif slope > 0:
-            if slope < 0.5:
-                return "#ffcdd2"
-            elif slope < 1.0:
-                return "#ef9a9a"
-            elif slope < 1.5:
-                return "#e57373"
-            else:
-                return "#d32f2f"
-        else:  # slope < 0
-            if slope > -0.5:
-                return "#c8e6c9"
-            elif slope > -1.0:
-                return "#a5d6a7"
-            elif slope > -1.5:
-                return "#81c784"
-            else:
-                return "#388e3c"
-
-    colors = [get_color(s) for s in data_sorted["slope"]]
-
-    bar_trace = go.Bar(
-        x=data_sorted[col_operator].astype(str),
-        y=data_sorted["weekly_avg"],
-        marker=dict(
-            color=colors,
-            line=dict(width=2, color="rgba(0,0,0,0.2)")
-        ),
-        text=[f"{v:.1f}" for v in data_sorted["weekly_avg"]],
-        textposition="outside",
-        hovertemplate=(
-            "<b>%{x}</b><br>" +
-            "Weekly Avg: %{y:.2f}<br>" +
-            "Trend Slope: %{customdata[0]:+.3f}<br>" +
-            "Total Events: %{customdata[1]}<br>" +
-            "Weeks Active: %{customdata[2]}<br>" +
-            "<extra></extra>"
-        ),
-        customdata=np.stack([data_sorted["slope"], data_sorted["total_events"], data_sorted["n_weeks"]], axis=-1)
-    )
+        # ===============================================================
+        # 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,
+                    showarrow=False,
+                    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"  # ✅ Yellow for One Time Event
+                elif slope > 0:
+                    if slope < 0.5:
+                        return "#ffcdd2"
+                    elif slope < 1.0:
+                        return "#ef9a9a"
+                    elif slope < 1.5:
+                        return "#e57373"
+                    else:
+                        return "#d32f2f"
+                else:  # slope < 0
+                    if slope > -0.5:
+                        return "#c8e6c9"
+                    elif slope > -1.0:
+                        return "#a5d6a7"
+                    elif slope > -1.5:
+                        return "#81c784"
+                    else:
+                        return "#388e3c"
+
+            colors = [get_color(s) for s in data_sorted["slope"]]
+
+            bar_trace = go.Bar(
+                x=data_sorted[col_operator].astype(str),
+                y=data_sorted["weekly_avg"],
+                marker=dict(
+                    color=colors,
+                    line=dict(width=2, color="rgba(0,0,0,0.2)")
+                ),
+                text=[f"{v:.1f}" for v in data_sorted["weekly_avg"]],
+                textposition="outside",
+                hovertemplate=(
+                    "<b>%{x}</b><br>" +
+                    "Weekly Avg: %{y:.2f}<br>" +
+                    "Trend Slope: %{customdata[0]:+.3f}<br>" +
+                    "Total Events: %{customdata[1]}<br>" +
+                    "Weeks Active: %{customdata[2]}<br>" +
+                    "<extra></extra>"
+                ),
+                customdata=np.stack([data_sorted["slope"], data_sorted["total_events"], data_sorted["n_weeks"]], axis=-1)
+            )
 
-    fig = go.Figure(bar_trace)
-    fig.update_layout(
-        title=dict(text=f"<b>{title}</b>", x=0.5),
-        height=450,
-        margin=dict(l=50, r=20, t=60, b=120),
-        xaxis_title="<b>Operator Name</b>",
-        yaxis_title="<b>Weekly Avg Events</b>",
-        font=dict(family="Segoe UI", size=12),
-        bargap=0.3,
-        plot_bgcolor="rgba(0,0,0,0)",
-        paper_bgcolor="rgba(0,0,0,0)",
-        xaxis=dict(tickangle=45)
-    )
-    return fig
+            fig = go.Figure(bar_trace)
+            fig.update_layout(
+                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 Name</b>",
+                yaxis_title="<b>Weekly Avg Events</b>",
+                font=dict(family="Segoe UI", size=12),
+                bargap=0.3,
+                plot_bgcolor="rgba(0,0,0,0)",
+                paper_bgcolor="rgba(0,0,0,0)",
+                xaxis=dict(tickangle=45)
+            )
+            return fig
 
         # ===============================================================
         # CHARTS
@@ -1596,74 +1604,103 @@ def plot_chart(data, title):
                 st.info("No HAULING COAL data for analysis.")
 
         # ===============================================================
-        # RECOMMENDATIONS
+        # RECOMMENDATIONS — FORMAT BARU: Judul + 3 List Langsung
         # ===============================================================
         col_rec1, col_rec2 = st.columns(2)
 
-        def generate_recommendations(top_ob, top_coal):
-            rec = {}
+        # === OB HAULER RECOMMENDATIONS (3-point list) ===
+        with col_rec1:
             if not top_ob.empty:
                 w = len(top_ob[top_ob['slope'] > 0])
                 ot = len(top_ob[top_ob['slope'] == 0])
                 avg = top_ob['weekly_avg'].mean()
+
+                rec_list_ob = []
+
+                # Point 1: Trend-driven action
                 if w > 5:
-                    r = "Prioritize fatigue intervention for operators with worsening trends."
-                    reason = "High proportion of deteriorating operators signals emerging fatigue risks."
+                    rec_list_ob.append("Conduct targeted fatigue risk assessments for operators with worsening trends (slope > 0).")
                 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."
+                    rec_list_ob.append("Investigate data completeness — high count of One Time Events may reflect inconsistent reporting.")
                 elif avg > 8:
-                    r = "Review scheduling and rest protocols to reduce event frequency."
-                    reason = "Elevated average event rate increases cumulative fatigue exposure."
+                    rec_list_ob.append("Review shift scheduling and rest-break compliance to reduce event frequency.")
                 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
+                    rec_list_ob.append("Continue current protocols with routine monitoring of top-risk operators.")
 
-            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."
+                # Point 2: One-Time Event follow-up
+                if ot > 0:
+                    rec_list_ob.append(f"Re-engage {ot} operators flagged as <b>One Time Event</b> to verify activity continuity.")
                 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
+                    rec_list_ob.append("No One Time Event operators identified — trend analysis remains reliable.")
 
-        ai_rec = generate_recommendations(top_ob, top_coal)
+                # Point 3: Benchmarking
+                rec_list_ob.append(f"Use cohort average ({avg:.2f} events/week) as baseline for monthly fatigue KPI reviews.")
+
+                # Ensure exactly 3 items
+                while len(rec_list_ob) < 3:
+                    rec_list_ob.append("—")
 
-        with col_rec1:
-            if 'ob' in ai_rec:
                 st.markdown("### OB HAULER Recommendations")
                 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>
+                    <ul>
+                        <li>{rec_list_ob[0]}</li>
+                        <li>{rec_list_ob[1]}</li>
+                        <li>{rec_list_ob[2]}</li>
+                    </ul>
+                    <div class="recommendation-reason">
+                        Based on trend slope, activity duration, and cohort event frequency.
+                    </div>
                 </div>
                 """, unsafe_allow_html=True)
             else:
                 st.info("No OB HAULER recommendations.")
 
+        # === HAULING COAL RECOMMENDATIONS (3-point list) ===
         with col_rec2:
-            if 'coal' in ai_rec:
+            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()
+
+                rec_list_coal = []
+
+                # Point 1: Trend-driven action
+                if w > 5:
+                    rec_list_coal.append("Conduct targeted fatigue risk assessments for operators with worsening trends (slope > 0).")
+                elif ot > 4:
+                    rec_list_coal.append("Investigate data completeness — high count of One Time Events may reflect inconsistent reporting.")
+                elif avg > 8:
+                    rec_list_coal.append("Review shift scheduling and rest-break compliance to reduce event frequency.")
+                else:
+                    rec_list_coal.append("Continue current protocols with routine monitoring of top-risk operators.")
+
+                # Point 2: One-Time Event follow-up
+                if ot > 0:
+                    rec_list_coal.append(f"Re-engage {ot} operators flagged as <b>One Time Event</b> to verify activity continuity.")
+                else:
+                    rec_list_coal.append("No One Time Event operators identified — trend analysis remains reliable.")
+
+                # Point 3: Benchmarking
+                rec_list_coal.append(f"Use cohort average ({avg:.2f} events/week) as baseline for monthly fatigue KPI reviews.")
+
+                # Ensure exactly 3 items
+                while len(rec_list_coal) < 3:
+                    rec_list_coal.append("—")
+
                 st.markdown("### HAULING COAL Recommendations")
                 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>
+                    <ul>
+                        <li>{rec_list_coal[0]}</li>
+                        <li>{rec_list_coal[1]}</li>
+                        <li>{rec_list_coal[2]}</li>
+                    </ul>
+                    <div class="recommendation-reason">
+                        Based on trend slope, activity duration, and cohort event frequency.
+                    </div>
                 </div>
                 """, unsafe_allow_html=True)
             else:
@@ -1671,9 +1708,7 @@ def plot_chart(data, title):
 
     except Exception as e:
         st.error(f"Error in Top 10 Operator analysis: {str(e)}")
-        st.exception(e)  # optionally show full traceback during dev
-
-
+        # st.exception(e)  # Uncomment during development only
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")