import streamlit as st import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots import folium from streamlit_folium import st_folium from sklearn.linear_model import LinearRegression # ================= CONFIG st.set_page_config( page_title="Michelin Mining Tyre Analytics", page_icon="", layout="wide", initial_sidebar_state="expanded" ) # ================= CUSTOM CSS st.markdown(""" """, unsafe_allow_html=True) # ================= LOAD @st.cache_data def load_data(): try: df = pd.read_excel("df_final.xlsx", sheet_name="Sheet1") except FileNotFoundError: st.error("❌ File `df_final.xlsx` not found. Please ensure it's in the same directory.") st.stop() # Fix encoding (e.g., '°C' → '°C') df.columns = df.columns.str.replace("Â", "") for col in df.select_dtypes(include='object').columns: df[col] = df[col].astype(str).str.replace("Â", "") # Parse datetime df['Time'] = pd.to_datetime(df['Time'], errors='coerce') df = df.dropna(subset=['Time']) df['hour'] = df['Time'].dt.hour # Alarm flag df['is_alarm'] = (~df['Alarm Status'].str.contains('No Alarm', na=False)).astype(int) # Dynamic risk score p = df['Pressure (psi)'] p_red_high = df['Red High Press (psi)'] p_amber_high = df['Amber High Press (psi)'] t = df['Temperature (°C)'] t_red = df['Absolute Red Temp (°C)'] t_amber = df['Absolute Amber Temp (°C)'] p_norm = np.clip((p - p_amber_high) / (p_red_high - p_amber_high), 0, 1) t_norm = np.clip((t - t_amber) / (t_red - t_amber), 0, 1) df['risk_score'] = 0.6 * p_norm + 0.4 * t_norm def get_risk_label(score): if score >= 0.8: return 'Very High Risk' elif score >= 0.6: return 'High Risk' elif score >= 0.3: return 'Moderate Risk' else: return 'Slight Risk' df['Risk Level'] = df['risk_score'].apply(get_risk_label) # Add Position Group df['Position Group'] = df['Position'].apply(lambda x: 'Front' if x in [1, 2] else 'Rear') return df df = load_data() # ================= LOGO FUNCTION (Perbaikan: Tanpa Base64, Gunakan st.image) ================= def render_pln_logo(): try: st.image("logo.png", width=120) except Exception: # Fallback: logo placeholder (putih, border halus) st.markdown('
', unsafe_allow_html=True) # ================= HEADER (Perbaikan: Dengan Logo di Kanan) ================= st.markdown('
', unsafe_allow_html=True) col1, col2, col3 = st.columns([1, 5, 1]) with col1: st.write("") # left spacer with col2: st.markdown('

Michelin Mining Tyre Analytics

', unsafe_allow_html=True) st.markdown('

Analysis is based on daily aggregated data

', unsafe_allow_html=True) with col3: render_pln_logo() # ← logo di kanan, seperti PLN st.markdown('
', unsafe_allow_html=True) # Lanjutkan dengan sidebar dan objek lainnya di bawah sini... # ================= SIDEBAR FILTERS ================= with st.sidebar: st.markdown("### Filter") # Tyre Type: with 'All' option, behaves like before tyre_types = st.selectbox( "Tyre Type", options=['All'] + sorted(df['Tyre Type'].dropna().unique()), index=0 ) if tyre_types != 'All': tyre_types = [tyre_types] else: tyre_types = sorted(df['Tyre Type'].dropna().unique()) # Date: behaves like Tyre Type — show 'All' by default date_options = sorted(df['date'].astype(str).unique()) dates_selected = st.selectbox( "Date", options=['All'] + date_options, index=0 ) if dates_selected != 'All': dates = [dates_selected] else: dates = date_options # Zone: behaves like Tyre Type — show 'All' by default zone_options = sorted(df['Zone'].dropna().unique()) zones_selected = st.selectbox( "Zone", options=['All'] + zone_options, index=0 ) if zones_selected != 'All': zones = [zones_selected] else: zones = zone_options # Position: behaves like Tyre Type — show 'All' by default pos_options = sorted(df['Position'].astype(int).dropna().unique()) positions_selected = st.selectbox( "Position", options=['All'] + pos_options, index=0 ) if positions_selected != 'All': positions = [positions_selected] else: positions = pos_options # Alarm Status: behaves like Tyre Type — show 'All' by default alarm_options = ["No Alarm", "Red High Pressure"] alarms_selected = st.selectbox( "Alarm Status", options=['All'] + alarm_options, index=0 ) if alarms_selected != 'All': alarms = [alarms_selected] else: alarms = alarm_options submit = st.button("Submit") # Apply filters if submit: dff = df.copy() if dates: dff = dff[dff['date'].astype(str).isin(dates)] if zones: dff = dff[dff['Zone'].isin(zones)] if positions: dff = dff[dff['Position'].astype(int).isin(positions)] if tyre_types: dff = dff[dff['Tyre Type'].isin(tyre_types)] if alarms: dff = dff[dff['Alarm Status'].isin(alarms)] else: dff = df # ================= OBJECTIVE 1 ================= dff = dff.copy() dff['Position'] = pd.Categorical(dff['Position'], categories=[1, 2, 3, 4], ordered=True) # Optional: Use descriptive labels (if desired) position_labels = { 1: '1: Front Left', 2: '2: Front Right', 3: '3: Rear Left', 4: '4: Rear Right' } # Or keep as just '1', '2'... if minimal # position_labels = {1: '1', 2: '2', 3: '3', 4: '4'} dff['Position_Label'] = dff['Position'].map(position_labels) st.markdown('

OBJECTIVE 1: Pressure & Temperature Trends — How Do Front and Rear Tyres Distribute?

', unsafe_allow_html=True) col1, col2 = st.columns(2) # Define consistent color mapping color_map = {1: '#d50000', 2: '#ff6d00', 3: '#ffcc00', 4: '#007acc'} category_order = [1, 2, 3, 4] with col1: st.markdown('
Pressure Distribution per Tyre Position
', unsafe_allow_html=True) fig1 = px.box( dff, x='Position_Label', y='Pressure (psi)', color='Position', color_discrete_map=color_map, category_orders={'Position': category_order}, template="plotly_white", labels={'Position_Label': 'Position'} ) red_high = dff['Red High Press (psi)'].min() amber_high = dff['Amber High Press (psi)'].min() fig1.add_hline(y=red_high, line_dash="dash", line_color="red", annotation_text="Red High Press", annotation_position="top right") fig1.add_hline(y=amber_high, line_color="orange", annotation_text="Amber High Press", annotation_position="bottom right") fig1.update_layout( margin=dict(t=40), legend_title_text='Position', legend=dict( yanchor="top", y=0.99, xanchor="left", x=1.02 # Place legend outside plot to avoid overlap ) ) st.plotly_chart(fig1, use_container_width=True) with col2: st.markdown('
Temperature Distribution per Tyre Position
', unsafe_allow_html=True) fig2 = px.box( dff, x='Position_Label', y='Temperature (°C)', color='Position', color_discrete_map=color_map, category_orders={'Position': category_order}, template="plotly_white", labels={'Position_Label': 'Position'} ) red_temp = dff['Absolute Red Temp (°C)'].min() amber_temp = dff['Absolute Amber Temp (°C)'].min() fig2.add_hline(y=red_temp, line_dash="dash", line_color="red", annotation_text="Red Temp", annotation_position="top right") fig2.add_hline(y=amber_temp, line_color="orange", annotation_text="Amber Temp", annotation_position="bottom right") fig2.update_layout( margin=dict(t=40), legend_title_text='Position', legend=dict( yanchor="top", y=0.99, xanchor="left", x=1.02 ) ) st.plotly_chart(fig2, use_container_width=True) # Insight 1 # Analisis data untuk menentukan pola front_pressure_avg = dff[dff['Position'].isin([1, 2])]['Pressure (psi)'].mean() rear_pressure_avg = dff[dff['Position'].isin([3, 4])]['Pressure (psi)'].mean() front_temp_avg = dff[dff['Position'].isin([1, 2])]['Temperature (°C)'].mean() rear_temp_avg = dff[dff['Position'].isin([3, 4])]['Temperature (°C)'].mean() if front_pressure_avg < rear_pressure_avg and front_temp_avg > rear_temp_avg: insight_text = f""" Front tyres (Pos 1 & 2): Higher average pressure and temperature ({front_pressure_avg:.1f} psi, {front_temp_avg:.1f}°C) indicate higher loading and heat generation. Rear tyres (Pos 3 & 4): Lower average temperature ({rear_temp_avg:.1f}°C) suggests lighter effective loading. """ elif front_pressure_avg > rear_pressure_avg and front_temp_avg < rear_temp_avg: insight_text = f""" Front tyres (Pos 1 & 2): Average pressure {front_pressure_avg:.1f} psi and average temperature {front_temp_avg:.1f}°C show lower heat levels compared to the rear tyres. Rear tyres (Pos 3 & 4): Average pressure {rear_pressure_avg:.1f} psi and average temperature {rear_temp_avg:.1f}°C show higher heat levels. """ else: insight_text = f""" Front tyres: Pressure {front_pressure_avg:.1f} psi, temperature {front_temp_avg:.1f}°C. Rear tyres: Pressure {rear_pressure_avg:.1f} psi, temperature {rear_temp_avg:.1f}°C. """ st.markdown(f"""
{insight_text.strip()}
""", unsafe_allow_html=True) # ================= OBJECTIVE 2 ================= st.markdown('

OBJECTIVE 2: Alarm Frequency Analysis — When, Where, and Which Tyres Matter Most?

', unsafe_allow_html=True) col_a, col_b = st.columns(2) # --- COL A: Alarm Distribution by Hour (Polar Chart) --- with col_a: st.markdown('
Alarm Distribution by Hour
', unsafe_allow_html=True) alarm_hour_pos = dff[dff['is_alarm'] == 1][['hour', 'Position']].copy() if alarm_hour_pos.empty: st.warning("No alarm data to display.") else: # Hitung alarm per jam & posisi hourly_pos_counts = alarm_hour_pos.groupby(['hour', 'Position']).size().unstack(fill_value=0) positions = sorted([p for p in [1, 2, 3, 4] if p in hourly_pos_counts.columns]) # enforce 1-4 order color_map = {1: '#d50000', 2: '#ff6d00', 3: '#ffcc00', 4: '#007acc'} fig_polar = go.Figure() max_r = max(hourly_pos_counts.sum(axis=1)) * 1.05 if not hourly_pos_counts.empty else 10 for pos in positions: if pos in hourly_pos_counts.columns: counts = hourly_pos_counts[pos].reindex(range(24), fill_value=0).values theta = [h * 15 for h in range(24)] # 24 jam → 360° / 24 = 15° per jam fig_polar.add_trace(go.Barpolar( r=counts, theta=theta, width=15, name=f'Position {pos}', marker_color=color_map[pos], opacity=0.85, hovertemplate='Hour: %{theta:0f}:00
Alarms: %{r}' )) fig_polar.update_layout( polar=dict( radialaxis=dict(visible=True, range=[0, max_r], tickfont=dict(size=10)), angularaxis=dict( direction="clockwise", tickvals=[0, 90, 180, 270], ticktext=["00:00", "06:00", "12:00", "18:00"], tickfont=dict(size=11) ) ), legend=dict( title_text='Tyre Position', yanchor="top", y=0.98, xanchor="left", x=1.02, bgcolor="rgba(255,255,255,0.7)", borderwidth=0.5, itemclick=False, # prevent accidental legend toggle itemdoubleclick=False ), margin=dict(t=40, b=20, l=20, r=120), hovermode="closest" ) st.plotly_chart(fig_polar, use_container_width=True) # --- COL B: Alarm Hotspots (Front Tyres Only: Pos 1 & 2) --- with col_b: st.markdown('
Alarm Hotspots by Tyre, Position & Zone
', unsafe_allow_html=True) # Filter hanya alarm di front tyres (Pos 1 & 2) front_alarm_data = dff[(dff['is_alarm'] == 1) & (dff['Position'].isin([1, 2]))].copy() if front_alarm_data.empty: st.warning("No alarm data for front tyres to display.") else: agg_data = ( front_alarm_data .groupby(['TyreSN', 'Position', 'Zone']) .size() .reset_index(name='Count') ) agg_data['Percentage'] = (agg_data['Count'] / agg_data['Count'].sum()) * 100 # Warna eksplisit untuk 1 & 2 color_map_front = {1: '#d50000', 2: '#ff6d00'} fig_bubble = px.scatter( agg_data, x='Position', y='Zone', size='Count', color='Position', color_discrete_map=color_map_front, hover_name='TyreSN', hover_data={'Position': True, 'Zone': True, 'Count': True, 'Percentage': ':.1f%'}, size_max=55, template='plotly_white', category_orders={'Position': [1, 2]} ) # Tambahkan label singkat di tengah bubble (4 digit akhir SN) fig_bubble.update_traces( text=agg_data['TyreSN'].str[-4:], textposition='middle center', textfont=dict(color='white', size=9) ) fig_bubble.update_layout( xaxis=dict( title='Position', tickmode='array', tickvals=[1, 2], ticktext=['1', '2'], tickfont=dict(size=12) ), yaxis=dict(title='Zone', tickfont=dict(size=12)), legend=dict( title_text='Tyre Position', yanchor="top", y=0.98, xanchor="left", x=1.02, bgcolor="rgba(255,255,255,0.7)", borderwidth=0.5 ), margin=dict(t=40, b=20, l=20, r=120), showlegend=True ) # Rename legend entries fig_bubble.for_each_trace(lambda t: t.update( name=f'Position {int(t.name)}' )) st.plotly_chart(fig_bubble, use_container_width=True) # --- INSIGHT 2: Actionable, Numeric, Time-Group Based --- alarm_hours = dff[dff['is_alarm'] == 1]['hour'] if alarm_hours.empty: insight_text = "• No alarm data available for analysis." else: # Group hours into time bands def hour_to_band(h): if 0 <= h < 6: return "00:00–06:00 (Night)" if 6 <= h < 12: return "06:00–12:00 (Morning)" if 12 <= h < 18: return "12:00–18:00 (Afternoon)" return "18:00–00:00 (Evening)" alarm_hours_df = pd.DataFrame({'hour': alarm_hours}) alarm_hours_df['band'] = alarm_hours_df['hour'].apply(hour_to_band) band_counts = alarm_hours_df['band'].value_counts().sort_index() # sort by natural order # Identify dominant & second-dominant bands top_bands = band_counts.nlargest(2) dominant_band = top_bands.index[0] if len(top_bands) > 0 else "N/A" second_dominant_band = top_bands.index[1] if len(top_bands) > 1 else "N/A" dominant_pct = (top_bands.iloc[0] / band_counts.sum() * 100) if len(top_bands) > 0 else 0 second_pct = (top_bands.iloc[1] / band_counts.sum() * 100) if len(top_bands) > 1 else 0 # Front vs Rear alarm share front_alarms = dff[(dff['is_alarm'] == 1) & (dff['Position'].isin([1, 2]))].shape[0] rear_alarms = dff[(dff['is_alarm'] == 1) & (dff['Position'].isin([3, 4]))].shape[0] total_alarms = front_alarms + rear_alarms front_pct = front_alarms / total_alarms * 100 if total_alarms > 0 else 0 # Top zone top_zone = dff[dff['is_alarm'] == 1]['Zone'].value_counts().index[0] if not dff[dff['is_alarm'] == 1].empty else "N/A" # Build insight bullets insight_lines = [ f"{dominant_band} is the dominant alarm period ({dominant_pct:.1f}% of all alarms).", f"{second_dominant_band} is the second-highest period ({second_pct:.1f}% of alarms)." ] if front_alarms > 0: insight_lines.append(f"Front tyres (Pos 1 & 2) account for {front_pct:.1f}% of all alarms, indicating higher stress or usage intensity upfront.") if top_zone != "N/A": insight_lines.append(f"Zone {top_zone} records the highest alarm frequency across all positions.") insight_lines.append("• Alarm clustering in specific hours and front positions suggests opportunity for targeted inspection scheduling.") insight_text = "\n".join(insight_lines) # --- Display Insight Box --- st.markdown(f"""
{insight_text}
""", unsafe_allow_html=True) # ================= OBJECTIVE 3 ================= st.markdown('

OBJECTIVE 3: Correlation — How Does Heat Influence Pressure and Which Tyres Trigger Red Alarms?

', 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 =============== with col1: st.markdown('
Front Tyres: Temperature → Pressure
', unsafe_allow_html=True) if not front_df.empty: front_df['Category'] = front_df.apply( lambda row: f"{'Normal' if row['Alarm Status'] == 'No Alarm' else 'Red Pressure'} Front Tyre", axis=1 ) categories = ["Normal 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: 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] fig1 = px.scatter( valid_data, x='Temperature (°C)', y='Pressure (psi)', color='Category', color_discrete_map={ "Normal Front Tyre": "#2E7D32", "Red Pressure Front Tyre": "#D32F2F" }, category_orders={'Category': categories}, template="plotly_white", labels={'Temperature (°C)': 'Temperature (°C)', 'Pressure (psi)': 'Pressure (psi)'} ) fig1.update_traces( hovertemplate="%{marker.color}
Temp: %{x:.1f}°C
Pressure: %{y:.1f} psi", marker=dict(size=6) ) fig1.add_trace(go.Scatter( x=x_line.flatten(), y=y_line, mode='lines', name='Trend Line', line=dict(color='#1976D2', dash='dot', width=2) )) 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='Tyre Status', bgcolor="white", bordercolor="lightgray", borderwidth=1, itemclick=False, itemdoubleclick=False ) ) st.plotly_chart(fig1, use_container_width=True) else: st.warning("Insufficient data for front tyres.") else: st.warning("No front tyre data.") # =============== COL 2: Front — Temperature vs Speed =============== with col2: st.markdown('
Front Tyres: Temperature → Speed
', unsafe_allow_html=True) if not front_df.empty: front_df['Category'] = front_df.apply( lambda row: f"{'Normal' if row['Alarm Status'] == 'No Alarm' else 'Red Pressure'} Front Tyre", axis=1 ) categories = ["Normal Front Tyre", "Red Pressure Front Tyre"] front_df['Category'] = pd.Categorical(front_df['Category'], categories=categories, ordered=True) valid_data = front_df.dropna(subset=['Temperature (°C)', 'Speed (km/h)']) if len(valid_data) > 1: X = valid_data[['Temperature (°C)']] y = valid_data['Speed (km/h)'] 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['Speed (km/h)'])[0, 1] fig2 = px.scatter( valid_data, x='Temperature (°C)', y='Speed (km/h)', color='Category', color_discrete_map={ "Normal Front Tyre": "#2E7D32", "Red Pressure Front Tyre": "#D32F2F" }, category_orders={'Category': categories}, template="plotly_white" ) fig2.update_traces( hovertemplate="%{marker.color}
Temp: %{x:.1f}°C
Speed: %{y:.1f} km/h", marker=dict(size=6) ) fig2.add_trace(go.Scatter( x=x_line.flatten(), y=y_line, mode='lines', name='Trend Line', line=dict(color='#1976D2', dash='dot', width=2) )) fig2.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='Tyre Status', bgcolor="white", bordercolor="lightgray", borderwidth=1, itemclick=False, itemdoubleclick=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 =============== col3, col4 = st.columns(2) with col3: st.markdown('
Rear Tyres: Temperature → Pressure
', unsafe_allow_html=True) if not rear_df.empty: rear_df['Category'] = rear_df.apply( lambda row: f"{'Normal' if row['Alarm Status'] == 'No Alarm' else 'Red Pressure'} Rear Tyre", axis=1 ) categories = ["Normal 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)']] 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='Category', color_discrete_map={ "Normal Rear Tyre": "#2E7D32", "Red Pressure Rear Tyre": "#D32F2F" }, category_orders={'Category': categories}, template="plotly_white" ) fig3.update_traces( hovertemplate="%{marker.color}
Temp: %{x:.1f}°C
Pressure: %{y:.1f} psi", marker=dict(size=6) ) fig3.add_trace(go.Scatter( x=x_line.flatten(), y=y_line, mode='lines', name='Trend Line', line=dict(color='#1976D2', dash='dot', width=2) )) 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='Tyre Status', bgcolor="white", bordercolor="lightgray", borderwidth=1, itemclick=False, itemdoubleclick=False ) ) st.plotly_chart(fig3, use_container_width=True) else: st.warning("Insufficient data for rear tyres.") else: st.warning("No rear tyre data.") # =============== COL 4: Rear — Temperature vs Speed =============== with col4: st.markdown('
Rear Tyres: Temperature → Speed
', unsafe_allow_html=True) if not rear_df.empty: rear_df['Category'] = rear_df.apply( lambda row: f"{'Normal' if row['Alarm Status'] == 'No Alarm' else 'Red Pressure'} Rear Tyre", axis=1 ) categories = ["Normal 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)', 'Speed (km/h)']) if len(valid_data) > 1: X = valid_data[['Temperature (°C)']] y = valid_data['Speed (km/h)'] 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['Speed (km/h)'])[0, 1] fig4 = px.scatter( valid_data, x='Temperature (°C)', y='Speed (km/h)', color='Category', color_discrete_map={ "Normal Rear Tyre": "#2E7D32", "Red Pressure Rear Tyre": "#D32F2F" }, category_orders={'Category': categories}, template="plotly_white" ) fig4.update_traces( hovertemplate="%{marker.color}
Temp: %{x:.1f}°C
Speed: %{y:.1f} km/h", marker=dict(size=6) ) fig4.add_trace(go.Scatter( x=x_line.flatten(), y=y_line, mode='lines', name='Trend Line', line=dict(color='#1976D2', dash='dot', width=2) )) fig4.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='Tyre Status', bgcolor="white", bordercolor="lightgray", borderwidth=1, itemclick=False, itemdoubleclick=False ) ) st.plotly_chart(fig4, use_container_width=True) else: st.warning("Insufficient data for rear tyres.") else: st.warning("No rear tyre data.") # =============== INSIGHT 3 =============== # Compute correlations safely 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] 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_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. """ st.markdown(f"""
{insight_text.strip()}
""", unsafe_allow_html=True) # ================= OBJECTIVE 4 ================= st.markdown('

OBJECTIVE 4: Spatial Risk Mapping — Where Do Red Pressure Alarms Occur Most Frequently?

', unsafe_allow_html=True) st.markdown('
Tyre Alarms Distribution by Location
', unsafe_allow_html=True) valid_gps = dff.dropna(subset=['Latitude_y', 'Longitude_y']) if valid_gps.empty: st.warning("No valid GNSS coordinates for selected filters.") else: center_lat = valid_gps['Latitude_y'].mean() center_lon = valid_gps['Longitude_y'].mean() m = folium.Map( location=[center_lat, center_lon], zoom_start=16, tiles='CartoDB positron', width='100%', height='520px' ) for _, r in valid_gps.iterrows(): color = '#D32F2F' if r['Alarm Status'] == 'Red High Pressure' else '#2E7D32' radius = 6 + (r['Temperature (°C)'] - valid_gps['Temperature (°C)'].min()) / (valid_gps['Temperature (°C)'].max() - valid_gps['Temperature (°C)'].min() + 1e-5) * 12 popup_html = f"""
SN: {r['TyreSN']} | Pos: {int(r['Position'])}
Zone: {r['Zone']}
Press: {r['Pressure (psi)']:.1f} psi
Temp: {r['Temperature (°C)']:.1f} °C
Speed: {r['Speed (km/h)']:.1f} km/h
Alarm: {r['Alarm Status']}
""" folium.CircleMarker( location=[r['Latitude_y'], r['Longitude_y']], radius=radius, color=color, fill=True, fill_color=color, fill_opacity=0.75, weight=1, popup=folium.Popup(popup_html, max_width=250) ).add_to(m) # Legend legend_html = '''
Legend
● Normal (No Alarm)
● Red Pressure
▲ Front Tyre
★ Rear Tyre
Size ∝ Temperature
''' m.get_root().html.add_child(folium.Element(legend_html)) st_folium(m, width='100%', height=520, returned_objects=[]) # Insight 4 # Analisis data untuk menentukan pola spasial if not valid_gps.empty: # Hitung jumlah alarm per zona zone_counts = valid_gps[valid_gps['is_alarm'] == 1]['Zone'].value_counts() if not zone_counts.empty: top_zone = zone_counts.index[0] top_zone_count = zone_counts.iloc[0] total_alarms = valid_gps[valid_gps['is_alarm'] == 1].shape[0] percentage = (top_zone_count / total_alarms) * 100 else: top_zone = "N/A" percentage = 0 # Hitung jumlah alarm per posisi (front vs rear) front_alarms = valid_gps[(valid_gps['is_alarm'] == 1) & (valid_gps['Position'].isin([1, 2]))].shape[0] rear_alarms = valid_gps[(valid_gps['is_alarm'] == 1) & (valid_gps['Position'].isin([3, 4]))].shape[0] total_alarms = front_alarms + rear_alarms if total_alarms > 0: front_percentage = (front_alarms / total_alarms) * 100 else: front_percentage = 0 insight_text = f""" Alarm concentration is highest in {top_zone}, with {top_zone_count} alarms representing {percentage:.1f}% of total alarms. Front tyres account for {front_percentage:.1f}% of all alarms, indicating a higher alarm occurrence compared to rear tyres. GNSS data confirms alarm clustering within specific operational zones. Alarm events are concentrated by location and tyre position based on observed data distribution. """ else: insight_text = """ No valid GNSS data available for analysis. """ st.markdown(f"""
{insight_text.strip()}
""", unsafe_allow_html=True) # ================= OBJECTIVE 5 ================= st.markdown('

OBJECTIVE 5: Insights & Mitigation — How Can Red Pressure Alarms Be Reduced?

', unsafe_allow_html=True) # --- DATA PREP --- front_pressure_avg = dff[dff['Position'].isin([1, 2])]['Pressure (psi)'].mean() front_temp_avg = dff[dff['Position'].isin([1, 2])]['Temperature (°C)'].mean() hourly_counts = dff[dff['is_alarm'] == 1]['hour'].value_counts().reindex(range(24), fill_value=0) dominant_hour = hourly_counts.idxmax() if len(hourly_counts) > 0 else "N/A" total_alarms = hourly_counts.sum() dominant_percentage = (hourly_counts[dominant_hour] / total_alarms) * 100 if total_alarms > 0 else 0 zone_counts = dff[dff['is_alarm'] == 1]['Zone'].value_counts() top_zone = zone_counts.index[0] if not zone_counts.empty else "N/A" top_zone_percentage = (zone_counts.iloc[0] / total_alarms) * 100 if total_alarms > 0 else 0 # Correlation analysis front_df = dff[dff['Position'].isin([1, 2])] rear_df = dff[dff['Position'].isin([3, 4])] if not front_df.empty and len(front_df[['Pressure (psi)']].dropna()) > 1 and len(front_df[['Temperature (°C)']].dropna()) > 1: corr_front = np.corrcoef(front_df['Pressure (psi)'], front_df['Temperature (°C)'])[0,1] else: corr_front = 0 if not rear_df.empty and len(rear_df[['Speed (km/h)']].dropna()) > 1 and len(rear_df[['Temperature (°C)']].dropna()) > 1: corr_rear = np.corrcoef(rear_df['Speed (km/h)'], rear_df['Temperature (°C)'])[0,1] else: corr_rear = 0 # Insight insight_text = f"""1. Front tyres (Pos 1 & 2) show average pressure of {front_pressure_avg:.1f} psi and temperature of {front_temp_avg:.1f}°C, indicating potential over-inflation or insufficient load distribution (Objective 1).
2. Peak alarms occur at {dominant_hour}:00–{(dominant_hour+1)%24}:00, accounting for {dominant_percentage:.1f}% of total alarms, primarily in {top_zone} (Objective 2).
3. Front tyres exhibit a pressure–temperature correlation of r = {corr_front:.2f}, while rear tyres show r = {corr_rear:.2f}, indicating higher operational stress on front tyres (Objective 3).
4. {top_zone} contains {top_zone_percentage:.1f}% of all alarms, confirmed as a high-risk hotspot through GNSS data (Objective 4).""" try: import requests import json API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta " prompt = f""" Role: Fleet Operations Risk Analyst Insights: - High-risk zone: {top_zone} ({top_zone_percentage:.1f}% of alarms) - Front tyres: 62% of total alarms - Peak alarm hour: {dominant_hour}:00 ({dominant_percentage:.1f}%) - Front tyre pressure–temperature correlation r = {corr_front:.2f} Task: Generate: 1. Business Recommendations 2. Risk Mitigation Actions Rules: - Use only provided insights - No root-cause speculation - Business-ready language """ payload = { "inputs": prompt, "parameters": { "max_new_tokens": 25000, "temperature": 0.8, "top_p": 0.9 } } response = requests.post(API_URL, json=payload) generated_text = response.json()[0]["generated_text"] recommendation_text = generated_text risk_mitigation_text = generated_text # Jika response kosong, gunakan versi manual if recommendation_text == "": recommendation_text = f"""1. Calibrate front tyre pressure regularly to maintain optimal {front_pressure_avg:.1f} psi and prevent over-inflation (Objective 1).
2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency (Objective 2).
3. Monitor pressure and temperature correlation in front tyres (r = {corr_front:.2f}) to prevent overheating and premature wear (Objective 3).
4. Restrict vehicle access to {top_zone} until pavement maintenance is completed, as it contributes to {top_zone_percentage:.1f}% of alarms (Objective 4).""" if risk_mitigation_text == "": risk_mitigation_text = f"""1. Adjust front tyre load distribution to reduce {front_temp_avg:.1f}°C temperature and prevent overheating (Objective 1).
2. Schedule additional inspections during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) when {dominant_percentage:.1f}% of alarms occur (Objective 2).
3. Introduce predictive maintenance for front tyres with correlation r = {corr_front:.2f} to prevent unplanned downtime (Objective 3).
4. Implement real-time monitoring in {top_zone} where {top_zone_percentage:.1f}% of alarms are concentrated (Objective 4).""" except: # Jika response dari model kosong atau gagal, gunakan versi manual recommendation_text = f"""1. Calibrate front tyre pressure regularly to maintain optimal {front_pressure_avg:.1f} psi and prevent over-inflation (Objective 1).
2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency (Objective 2).
3. Monitor pressure and temperature correlation in front tyres (r = {corr_front:.2f}) to prevent overheating and premature wear (Objective 3).
4. Restrict vehicle access to {top_zone} until pavement maintenance is completed, as it contributes to {top_zone_percentage:.1f}% of alarms (Objective 4).""" # Risk Mitigation risk_mitigation_text = f"""1. Adjust front tyre load distribution to reduce {front_temp_avg:.1f}°C temperature and prevent overheating (Objective 1).
2. Schedule additional inspections during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) when {dominant_percentage:.1f}% of alarms occur (Objective 2).
3. Introduce predictive maintenance for front tyres with correlation r = {corr_front:.2f} to prevent unplanned downtime (Objective 3).
4. Implement real-time monitoring in {top_zone} where {top_zone_percentage:.1f}% of alarms are concentrated (Objective 4).""" # ============== SUBHEADER + BOX 1: INSIGHT ============== st.markdown('

INSIGHT

', unsafe_allow_html=True) st.markdown(f"""
{insight_text.strip()}
""", unsafe_allow_html=True) # ============== SUBHEADER + BOX 2: RECOMMENDATION ============== st.markdown('

RECOMMENDATION

', unsafe_allow_html=True) st.markdown(f"""
{recommendation_text.strip()}
""", unsafe_allow_html=True) # ============== SUBHEADER + BOX 3: RISK MITIGATION ============== st.markdown('

RISK MITIGATION

', unsafe_allow_html=True) st.markdown(f"""
{risk_mitigation_text.strip()}
""", unsafe_allow_html=True) # ================= FOOTER ================= st.markdown("""
Michelin Mining Tyre Analytics
""", unsafe_allow_html=True)