Update app.py

app.py

@@ -570,34 +570,38 @@ st.markdown('''
     position: relative;
     top: 0; right: 0;
   ">
-    OBJECTIVE 2: Hourly Alarm Trend Analysis — By Tyre Position
+    OBJECTIVE 2: Hourly Data Capture vs Alarm Count Analysis
   </div>
 </div>
 ''', unsafe_allow_html=True)
 
-# Ambil data alarm
-alarm_data = dff[dff['is_alarm'] == 1].copy()
+col1, col2 = st.columns(2)
 
-if alarm_data.empty:
-    st.warning("No alarm data to display.")
-else:
-    # Hitung jumlah alarm per jam dan per posisi (pastikan posisi 1–4)
-    hourly_pos_counts = alarm_data.groupby(['hour', 'Position']).size().unstack(fill_value=0)
+# =============== COL 1: Capture Data per Jam per Tyre ===============
+with col1:
+    st.markdown('<h5 style="text-align:center; margin-top: 0;">Data Capture per Hour by Tyre Position</h5>', unsafe_allow_html=True)
+
+    # Hitung jumlah data capture per jam dan per posisi (semua data, bukan hanya alarm)
+    capture_data = dff.copy()
+    hourly_capture_counts = capture_data.groupby(['hour', 'Position']).size().unstack(fill_value=0)
+
+    # Pastikan semua posisi (1,2,3,4) ada
     for pos in [1, 2, 3, 4]:
-        if pos not in hourly_pos_counts.columns:
-            hourly_pos_counts[pos] = 0
-    hourly_pos_counts = hourly_pos_counts[[1, 2, 3, 4]].copy()
+        if pos not in hourly_capture_counts.columns:
+            hourly_capture_counts[pos] = 0
+
+    hourly_capture_counts = hourly_capture_counts[[1, 2, 3, 4]].copy()
 
-    # === PERBAIKAN UTAMA: gunakan melt + string column names ===
-    hourly_melted = hourly_pos_counts.reset_index().melt(
+    # Melt data untuk plotting
+    hourly_capture_melted = hourly_capture_counts.reset_index().melt(
         id_vars='hour',
         value_vars=[1, 2, 3, 4],
         var_name='Position',
-        value_name='Alarm Count'
+        value_name='Capture Count'
     )
-    hourly_melted['Position'] = hourly_melted['Position'].astype(str)  # pastikan string
+    hourly_capture_melted['Position'] = hourly_capture_melted['Position'].astype(str)
 
-    # Warna sesuai preferensi (exact match)
+    # Warna sesuai preferensi
     color_map = {
         '1': '#003DA5',   # Dark blue
         '2': '#7FA6E8',   # Light blue
@@ -605,20 +609,19 @@ else:
         '4': '#FFE082'    # Light amber
     }
 
-    # Plot trend
-    fig = px.line(
-        hourly_melted,
+    fig1 = px.line(
+        hourly_capture_melted,
         x='hour',
-        y='Alarm Count',
+        y='Capture Count',
         color='Position',
         color_discrete_map=color_map,
-        title="Hourly Alarm Count by Tyre Position",
-        labels={'Alarm Count': 'Number of Alarms', 'Position': 'Tyre Position'},
+        title="Data Capture per Hour by Tyre Position",
+        labels={'Capture Count': 'Number of Records', 'Position': 'Tyre Position'},
         line_shape='linear',
         template="plotly_white"
     )
 
-    fig.update_layout(
+    fig1.update_layout(
         xaxis=dict(
             title="Hour of Day",
             tickmode='array',
@@ -626,304 +629,91 @@ else:
             ticktext=[f"{h:02d}:00" for h in range(24)],
             tickangle=45
         ),
-        yaxis=dict(title="Number of Alarms"),
+        yaxis=dict(title="Number of Records"),
         legend_title_text='Tyre Position',
         margin=dict(t=40, b=40, l=40, r=20),
-        title_x=0.5  # center title
+        title_x=0.5
     )
 
-    st.plotly_chart(fig, use_container_width=True)
-
-
-    # === ANALISIS WAKTU DOMINAN (00–18 vs 18–24) ===
-    alarm_data['period'] = alarm_data['hour'].apply(
-        lambda h: '00:00–18:00' if 0 <= h < 18 else '18:00–24:00'
-    )
-    dom_per_pos = alarm_data.groupby(['Position', 'period']).size().unstack(fill_value=0)
-    # Pastikan kolom ada
-    for per in ['00:00–18:00', '18:00–24:00']:
-        if per not in dom_per_pos.columns:
-            dom_per_pos[per] = 0
-    dom_per_pos['Dominant Period'] = dom_per_pos.idxmax(axis=1)
-    dom_per_pos['Dominant %'] = (
-        dom_per_pos[['00:00–18:00', '18:00–24:00']].max(axis=1) / 
-        dom_per_pos[['00:00–18:00', '18:00–24:00']].sum(axis=1) * 100
-    ).round(2)
-
-    # Total per posisi
-    total_by_pos = alarm_data['Position'].value_counts().reindex([1,2,3,4], fill_value=0)
-
-
-    insight_text = f"""
-    Positions 1 & 2 (front) show stronger daytime concentration; Positions 3 & 4 (rear) have more balanced distribution."""
-    st.markdown(f"""
-    <div class="insight-box">
-    <div class="content">
-    {insight_text.strip()}
-    </div>
-    </div>
-    """, unsafe_allow_html=True)
-# ================= OBJECTIVE 3 =================
-st.markdown('<h3 class="objective-title">OBJECTIVE 4: Correlation — How Does Heat Influence Pressure and Which Tyres Trigger Red Alarms?</h3>', unsafe_allow_html=True)
-
-# Prepare data
-front_df = dff[dff['Position'].isin([1, 2])].copy()
-rear_df = dff[dff['Position'].isin([3, 4])].copy()
-
-col1, col2 = st.columns(2)
-
-# =============== COL 1: Front — Temperature → Pressure (Scatter + Regression Area) ===============
-with col1:
-    st.markdown('<h5 style="text-align:center; margin-top: 0;">Front Tyres: Temperature → Pressure</h5>', unsafe_allow_html=True)
-    
-    if not front_df.empty:
-        # Filter valid data
-        valid_data = front_df.dropna(subset=['Temperature (°C)', 'Pressure (psi)'])
-        if len(valid_data) > 1:
-            X = valid_data[['Temperature (°C)']]
-            y = valid_data['Pressure (psi)']
-            model = LinearRegression().fit(X, y)
-            x_line = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)
-            y_line = model.predict(x_line)
-            corr = np.corrcoef(valid_data['Temperature (°C)'], valid_data['Pressure (psi)'])[0, 1]
-
-            # Buat scatter plot
-            fig1 = px.scatter(
-                valid_data,
-                x='Temperature (°C)',
-                y='Pressure (psi)',
-                color_discrete_sequence=['#003DA5'],
-                template="plotly_white",
-                labels={'Temperature (°C)': 'Temperature (°C)', 'Pressure (psi)': 'Pressure (psi)'}
-            )
-
-            # Tambahkan garis regresi
-            fig1.add_trace(go.Scatter(
-                x=x_line.flatten(), y=y_line,
-                mode='lines',
-                name='Linear Regression',
-                line=dict(color='#D32F2F', width=2)
-            ))
-
-            # Tambahkan area confidence interval (soft background)
-            # Hitung standard error
-            y_pred = model.predict(X)
-            residuals = y - y_pred
-            mse = np.mean(residuals**2)
-            std_error = np.sqrt(mse)
-            y_upper = y_line + 1.96 * std_error
-            y_lower = y_line - 1.96 * std_error
-
-            # Tambahkan area
-            fig1.add_trace(go.Scatter(
-                x=np.concatenate([x_line.flatten(), x_line.flatten()[::-1]]),
-                y=np.concatenate([y_upper, y_lower[::-1]]),
-                fill='toself',
-                fillcolor='rgba(211, 47, 47, 0.1)',  # Merah transparan
-                line=dict(color='rgba(255,255,255,0)'),
-                showlegend=False,
-                name='Confidence Interval'
-            ))
-
-            fig1.update_layout(
-                margin=dict(t=40),
-                annotations=[
-                    dict(
-                        x=0.95, y=0.95,
-                        xref="paper", yref="paper",
-                        text=f"r = {corr:.2f}",
-                        showarrow=False,
-                        bgcolor="white",
-                        bordercolor="black",
-                        borderwidth=1,
-                        font=dict(color="black")
-                    )
-                ],
-                legend=dict(
-                    title_text='Data & Regression',
-                    bgcolor="white",
-                    bordercolor="lightgray",
-                    borderwidth=1,
-                    itemclick=False,
-                    itemdoubleclick=False
-                ),
-                showlegend=True
-            )
-            st.plotly_chart(fig1, use_container_width=True)
-        else:
-            st.warning("Insufficient data for front tyres.")
-    else:
-        st.warning("No front tyre data.")
+    st.plotly_chart(fig1, use_container_width=True)
 
-# =============== COL 2: Front — Temperature / Speed (Boxplot) ===============
+# =============== COL 2: Count Alarm (Amber & Red Only) ===============
 with col2:
-    st.markdown('<h5 style="text-align:center; margin-top: 0;">Front Tyres: Temperature / Speed (Boxplot)</h5>', unsafe_allow_html=True)
-    
-    if not front_df.empty:
-        # Hitung rasio suhu/kecepatan
-        front_df['Temp_Speed_Ratio'] = front_df['Temperature (°C)'] / (front_df['Speed (km/h)'] + 1e-6)
-
-        valid_data = front_df.dropna(subset=['Temp_Speed_Ratio'])
-        if not valid_data.empty:
-            fig2 = px.box(
-                valid_data,
-                y='Temp_Speed_Ratio',
-                template="plotly_white",
-                labels={'Temp_Speed_Ratio': 'Temperature / Speed'}
-            )
-
-            fig2.update_traces(
-                marker_color='#003DA5',
-                name='Front Tyres'
-            )
-
-            fig2.update_layout(
-                margin=dict(t=40),
-                yaxis_title='Temperature / Speed',
-                showlegend=False
-            )
-            st.plotly_chart(fig2, use_container_width=True)
-        else:
-            st.warning("Insufficient data for front tyres.")
-    else:
-        st.warning("No front tyre data.")
-
-# =============== COL 3: Rear — Temperature → Pressure (Scatter + Regression Area) ===============
-col3, col4 = st.columns(2)
-
-with col3:
-    st.markdown('<h5 style="text-align:center; margin-top: 0;">Rear Tyres: Temperature → Pressure</h5>', unsafe_allow_html=True)
-    
-    if not rear_df.empty:
-        valid_data = rear_df.dropna(subset=['Temperature (°C)', 'Pressure (psi)'])
-        if len(valid_data) > 1:
-            X = valid_data[['Temperature (°C)']]
-            y = valid_data['Pressure (psi)']
-            model = LinearRegression().fit(X, y)
-            x_line = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)
-            y_line = model.predict(x_line)
-            corr = np.corrcoef(valid_data['Temperature (°C)'], valid_data['Pressure (psi)'])[0, 1]
-
-            fig3 = px.scatter(
-                valid_data,
-                x='Temperature (°C)',
-                y='Pressure (psi)',
-                color_discrete_sequence=['#FFB300'],
-                template="plotly_white",
-                labels={'Temperature (°C)': 'Temperature (°C)', 'Pressure (psi)': 'Pressure (psi)'}
-            )
-
-            fig3.add_trace(go.Scatter(
-                x=x_line.flatten(), y=y_line,
-                mode='lines',
-                name='Linear Regression',
-                line=dict(color='#D32F2F', width=2)
-            ))
+    st.markdown('<h5 style="text-align:center; margin-top: 0;">Alarm Count (Amber & Red Only) per Hour by Tyre Position</h5>', unsafe_allow_html=True)
 
-            # Confidence interval area
-            y_pred = model.predict(X)
-            residuals = y - y_pred
-            mse = np.mean(residuals**2)
-            std_error = np.sqrt(mse)
-            y_upper = y_line + 1.96 * std_error
-            y_lower = y_line - 1.96 * std_error
+    # Filter hanya alarm Amber dan Red
+    alarm_data = dff[dff['is_alarm'] == 1].copy()
+    alarm_data = alarm_data[alarm_data['Alarm Status'].str.contains('Amber|Red', case=False, na=False)]
 
-            fig3.add_trace(go.Scatter(
-                x=np.concatenate([x_line.flatten(), x_line.flatten()[::-1]]),
-                y=np.concatenate([y_upper, y_lower[::-1]]),
-                fill='toself',
-                fillcolor='rgba(211, 47, 47, 0.1)',
-                line=dict(color='rgba(255,255,255,0)'),
-                showlegend=False,
-                name='Confidence Interval'
-            ))
-
-            fig3.update_layout(
-                margin=dict(t=40),
-                annotations=[
-                    dict(
-                        x=0.95, y=0.95,
-                        xref="paper", yref="paper",
-                        text=f"r = {corr:.2f}",
-                        showarrow=False,
-                        bgcolor="white",
-                        bordercolor="black",
-                        borderwidth=1,
-                        font=dict(color="black")
-                    )
-                ],
-                legend=dict(
-                    title_text='Data & Regression',
-                    bgcolor="white",
-                    bordercolor="lightgray",
-                    borderwidth=1,
-                    itemclick=False,
-                    itemdoubleclick=False
-                ),
-                showlegend=True
-            )
-            st.plotly_chart(fig3, use_container_width=True)
-        else:
-            st.warning("Insufficient data for rear tyres.")
+    if alarm_data.empty:
+        st.warning("No Amber or Red alarm data to display.")
     else:
-        st.warning("No rear tyre data.")
-
-# =============== COL 4: Rear — Temperature / Speed (Boxplot) ===============
-with col4:
-    st.markdown('<h5 style="text-align:center; margin-top: 0;">Rear Tyres: Temperature / Speed (Boxplot)</h5>', unsafe_allow_html=True)
-    
-    if not rear_df.empty:
-        rear_df['Temp_Speed_Ratio'] = rear_df['Temperature (°C)'] / (rear_df['Speed (km/h)'] + 1e-6)
-
-        valid_data = rear_df.dropna(subset=['Temp_Speed_Ratio'])
-        if not valid_data.empty:
-            fig4 = px.box(
-                valid_data,
-                y='Temp_Speed_Ratio',
-                template="plotly_white",
-                labels={'Temp_Speed_Ratio': 'Temperature / Speed'}
-            )
-
-            fig4.update_traces(
-                marker_color='#FFB300',
-                name='Rear Tyres'
-            )
+        # Hitung jumlah alarm per jam dan per posisi
+        hourly_alarm_counts = alarm_data.groupby(['hour', 'Position']).size().unstack(fill_value=0)
+
+        # Pastikan semua posisi (1,2,3,4) ada
+        for pos in [1, 2, 3, 4]:
+            if pos not in hourly_alarm_counts.columns:
+                hourly_alarm_counts[pos] = 0
+
+        hourly_alarm_counts = hourly_alarm_counts[[1, 2, 3, 4]].copy()
+
+        # Melt data untuk plotting
+        hourly_alarm_melted = hourly_alarm_counts.reset_index().melt(
+            id_vars='hour',
+            value_vars=[1, 2, 3, 4],
+            var_name='Position',
+            value_name='Alarm Count'
+        )
+        hourly_alarm_melted['Position'] = hourly_alarm_melted['Position'].astype(str)
+
+        fig2 = px.line(
+            hourly_alarm_melted,
+            x='hour',
+            y='Alarm Count',
+            color='Position',
+            color_discrete_map=color_map,
+            title="Alarm Count (Amber & Red Only) per Hour by Tyre Position",
+            labels={'Alarm Count': 'Number of Alarms', 'Position': 'Tyre Position'},
+            line_shape='linear',
+            template="plotly_white"
+        )
 
-            fig4.update_layout(
-                margin=dict(t=40),
-                yaxis_title='Temperature / Speed',
-                showlegend=False
-            )
-            st.plotly_chart(fig4, use_container_width=True)
-        else:
-            st.warning("Insufficient data for rear tyres.")
-    else:
-        st.warning("No rear tyre data.")
+        fig2.update_layout(
+            xaxis=dict(
+                title="Hour of Day",
+                tickmode='array',
+                tickvals=list(range(0, 24)),
+                ticktext=[f"{h:02d}:00" for h in range(24)],
+                tickangle=45
+            ),
+            yaxis=dict(title="Number of Alarms"),
+            legend_title_text='Tyre Position',
+            margin=dict(t=40, b=40, l=40, r=20),
+            title_x=0.5
+        )
 
-# =============== INSIGHT 4 ===============
-def safe_corr(a, b):
-    mask = ~(np.isnan(a) | np.isnan(b))
-    if mask.sum() < 2:
-        return 0.0
-    return np.corrcoef(a[mask], b[mask])[0, 1]
+        st.plotly_chart(fig2, use_container_width=True)
 
-corr_p_t_front = safe_corr(front_df['Temperature (°C)'], front_df['Pressure (psi)'])
-corr_t_s_front = safe_corr(front_df['Temperature (°C)'], front_df['Speed (km/h)'])
-corr_p_t_rear = safe_corr(rear_df['Temperature (°C)'], rear_df['Pressure (psi)'])
-corr_t_s_rear = safe_corr(rear_df['Temperature (°C)'], rear_df['Speed (km/h)'])
+# =============== INSIGHT ===============
+# Insight tetap bisa menampilkan perbandingan
+capture_by_pos = dff['Position'].value_counts().reindex([1,2,3,4], fill_value=0)
+alarm_by_pos = alarm_data['Position'].value_counts().reindex([1,2,3,4], fill_value=0)
 
 insight_text = f"""
-Front tyres show stronger temperature-driven pressure response (r = {corr_p_t_front:.2f}) vs rear (r = {corr_p_t_rear:.2f}), confirming heat has greater impact on front tyre inflation. Temperature speed correlation is low on both front (r = {corr_t_s_front:.2f}) and rear (r = {corr_t_s_rear:.2f}), indicating speed alone is not the primary heat source — likely dominated by load and friction. Red and amber alarms cluster in specific pressure-temperature zones, indicating critical failure thresholds.
+• Total data capture: Pos 1={capture_by_pos[1]}, 2={capture_by_pos[2]}, 3={capture_by_pos[3]}, 4={capture_by_pos[4]}
+• Total alarms (Amber/Red): Pos 1={alarm_by_pos[1]}, 2={alarm_by_pos[2]}, 3={alarm_by_pos[3]}, 4={alarm_by_pos[4]}
+• Alarm density varies significantly between positions — suggesting different operational stress levels.
 """
 
 st.markdown(f"""
 <div class="insight-box">
-    <div class="content">
+<div class="content">
 {insight_text.strip()}
-    </div>
+</div>
 </div>
 """, unsafe_allow_html=True)
-# ================= OBJECTIVE 4 =================
+# ================= OBJECTIVE 3 =================
 st.markdown('<h3 class="objective-title">OBJECTIVE 4: Correlation — How Does Heat Influence Pressure and Which Tyres Trigger Red Alarms?</h3>', unsafe_allow_html=True)
 
 # Prepare data
@@ -933,10 +723,20 @@ rear_df = dff[dff['Position'].isin([3, 4])].copy()
 col1, col2 = st.columns(2)
 
 # =============== COL 1: Front — Temperature → Pressure (Scatter + Regression Area) ===============
+# =============== COL 1: Front — Temperature → Pressure ===============
 with col1:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Front Tyres: Temperature → Pressure</h5>', unsafe_allow_html=True)
     
     if not front_df.empty:
+        # Tambahkan kategori alarm status
+        front_df['Category'] = front_df.apply(
+            lambda row: f"Normal Front Tyre" if row['Alarm Status'] == 'No Alarm'
+            else f"Amber Pressure Front Tyre" if 'Amber' in row['Alarm Status']
+            else f"Red Pressure Front Tyre", axis=1
+        )
+        categories = ["Normal Front Tyre", "Amber Pressure Front Tyre", "Red Pressure Front Tyre"]
+        front_df['Category'] = pd.Categorical(front_df['Category'], categories=categories, ordered=True)
+
         # Filter valid data
         valid_data = front_df.dropna(subset=['Temperature (°C)', 'Pressure (psi)'])
         if len(valid_data) > 1:
@@ -947,42 +747,30 @@ with col1:
             y_line = model.predict(x_line)
             corr = np.corrcoef(valid_data['Temperature (°C)'], valid_data['Pressure (psi)'])[0, 1]
 
-            # Buat scatter plot
             fig1 = px.scatter(
                 valid_data,
                 x='Temperature (°C)',
                 y='Pressure (psi)',
-                color_discrete_sequence=['#003DA5'],
+                color='Category',
+                color_discrete_map={
+                    "Normal Front Tyre": "#2E7D32",         # Hijau
+                    "Amber Pressure Front Tyre": "#FFC107", # Kuning
+                    "Red Pressure Front Tyre": "#D32F2F"    # Merah
+                },
+                category_orders={'Category': categories},
                 template="plotly_white",
                 labels={'Temperature (°C)': 'Temperature (°C)', 'Pressure (psi)': 'Pressure (psi)'}
             )
 
-            # Tambahkan garis regresi
-            fig1.add_trace(go.Scatter(
-                x=x_line.flatten(), y=y_line,
-                mode='lines',
-                name='Linear Regression',
-                line=dict(color='#D32F2F', width=2)
-            ))
-
-            # Tambahkan area confidence interval (soft background)
-            # Hitung standard error
-            y_pred = model.predict(X)
-            residuals = y - y_pred
-            mse = np.mean(residuals**2)
-            std_error = np.sqrt(mse)
-            y_upper = y_line + 1.96 * std_error
-            y_lower = y_line - 1.96 * std_error
+            fig1.update_traces(
+                hovertemplate="<b>%{marker.color}</b><br>Temp: %{x:.1f}°C<br>Pressure: %{y:.1f} psi<extra></extra>",
+                marker=dict(size=6)
+            )
 
-            # Tambahkan area
             fig1.add_trace(go.Scatter(
-                x=np.concatenate([x_line.flatten(), x_line.flatten()[::-1]]),
-                y=np.concatenate([y_upper, y_lower[::-1]]),
-                fill='toself',
-                fillcolor='rgba(211, 47, 47, 0.1)',  # Merah transparan
-                line=dict(color='rgba(255,255,255,0)'),
-                showlegend=False,
-                name='Confidence Interval'
+                x=x_line.flatten(), y=y_line,
+                mode='lines', name='Trend Line',
+                line=dict(color='#1976D2', dash='dot', width=2)
             ))
 
             fig1.update_layout(
@@ -1000,14 +788,13 @@ with col1:
                     )
                 ],
                 legend=dict(
-                    title_text='Data & Regression',
+                    title_text='Tyre Status',
                     bgcolor="white",
                     bordercolor="lightgray",
                     borderwidth=1,
                     itemclick=False,
                     itemdoubleclick=False
-                ),
-                showlegend=True
+                )
             )
             st.plotly_chart(fig1, use_container_width=True)
         else:
@@ -1015,13 +802,16 @@ with col1:
     else:
         st.warning("No front tyre data.")
 
+
 # =============== COL 2: Front — Temperature / Speed (Boxplot) ===============
 with col2:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Front Tyres: Temperature / Speed (Boxplot)</h5>', unsafe_allow_html=True)
     
     if not front_df.empty:
         # Hitung rasio suhu/kecepatan
-        front_df['Temp_Speed_Ratio'] = front_df['Temperature (°C)'] / (front_df['Speed (km/h)'] + 1e-6)
+        front_df = front_df[front_df['Speed (km/h)'] > 0]
+
+        front_df['Temp_Speed_Ratio'] = front_df['Temperature (°C)'] / (front_df['Speed (km/h)'])
 
         valid_data = front_df.dropna(subset=['Temp_Speed_Ratio'])
         if not valid_data.empty:
@@ -1050,11 +840,21 @@ with col2:
 
 # =============== COL 3: Rear — Temperature → Pressure (Scatter + Regression Area) ===============
 col3, col4 = st.columns(2)
+# =============== COL 3: Rear — Temperature → Pressure ===============
+col3, col4 = st.columns(2)
 
 with col3:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Rear Tyres: Temperature → Pressure</h5>', unsafe_allow_html=True)
     
     if not rear_df.empty:
+        rear_df['Category'] = rear_df.apply(
+            lambda row: f"Normal Rear Tyre" if row['Alarm Status'] == 'No Alarm'
+            else f"Amber Pressure Rear Tyre" if 'Amber' in row['Alarm Status']
+            else f"Red Pressure Rear Tyre", axis=1
+        )
+        categories = ["Normal Rear Tyre", "Amber Pressure Rear Tyre", "Red Pressure Rear Tyre"]
+        rear_df['Category'] = pd.Categorical(rear_df['Category'], categories=categories, ordered=True)
+
         valid_data = rear_df.dropna(subset=['Temperature (°C)', 'Pressure (psi)'])
         if len(valid_data) > 1:
             X = valid_data[['Temperature (°C)']]
@@ -1068,34 +868,25 @@ with col3:
                 valid_data,
                 x='Temperature (°C)',
                 y='Pressure (psi)',
-                color_discrete_sequence=['#FFB300'],
-                template="plotly_white",
-                labels={'Temperature (°C)': 'Temperature (°C)', 'Pressure (psi)': 'Pressure (psi)'}
+                color='Category',
+                color_discrete_map={
+                    "Normal Rear Tyre": "#2E7D32",
+                    "Amber Pressure Rear Tyre": "#FFC107",
+                    "Red Pressure Rear Tyre": "#D32F2F"
+                },
+                category_orders={'Category': categories},
+                template="plotly_white"
             )
 
-            fig3.add_trace(go.Scatter(
-                x=x_line.flatten(), y=y_line,
-                mode='lines',
-                name='Linear Regression',
-                line=dict(color='#D32F2F', width=2)
-            ))
-
-            # Confidence interval area
-            y_pred = model.predict(X)
-            residuals = y - y_pred
-            mse = np.mean(residuals**2)
-            std_error = np.sqrt(mse)
-            y_upper = y_line + 1.96 * std_error
-            y_lower = y_line - 1.96 * std_error
+            fig3.update_traces(
+                hovertemplate="<b>%{marker.color}</b><br>Temp: %{x:.1f}°C<br>Pressure: %{y:.1f} psi<extra></extra>",
+                marker=dict(size=6)
+            )
 
             fig3.add_trace(go.Scatter(
-                x=np.concatenate([x_line.flatten(), x_line.flatten()[::-1]]),
-                y=np.concatenate([y_upper, y_lower[::-1]]),
-                fill='toself',
-                fillcolor='rgba(211, 47, 47, 0.1)',
-                line=dict(color='rgba(255,255,255,0)'),
-                showlegend=False,
-                name='Confidence Interval'
+                x=x_line.flatten(), y=y_line,
+                mode='lines', name='Trend Line',
+                line=dict(color='#1976D2', dash='dot', width=2)
             ))
 
             fig3.update_layout(
@@ -1113,14 +904,13 @@ with col3:
                     )
                 ],
                 legend=dict(
-                    title_text='Data & Regression',
+                    title_text='Tyre Status',
                     bgcolor="white",
                     bordercolor="lightgray",
                     borderwidth=1,
                     itemclick=False,
                     itemdoubleclick=False
-                ),
-                showlegend=True
+                )
             )
             st.plotly_chart(fig3, use_container_width=True)
         else:
@@ -1133,7 +923,9 @@ with col4:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Rear Tyres: Temperature / Speed (Boxplot)</h5>', unsafe_allow_html=True)
     
     if not rear_df.empty:
-        rear_df['Temp_Speed_Ratio'] = rear_df['Temperature (°C)'] / (rear_df['Speed (km/h)'] + 1e-6)
+        rear_df = rear_df[rear_df['Speed (km/h)'] > 0]
+
+        rear_df['Temp_Speed_Ratio'] = rear_df['Temperature (°C)'] / (rear_df['Speed (km/h)'] )
 
         valid_data = rear_df.dropna(subset=['Temp_Speed_Ratio'])
         if not valid_data.empty: