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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,20 +1,40 @@
-FROM python:3.13.5-slim
+# ✅ Gunakan Python 3.10 (versi stabil & didukung Streamlit)
+FROM python:3.10-slim
 
-WORKDIR /app
+# Non-interactive mode
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
+# Install deps minimal (tanpa build-essential berat)
+RUN apt-get update && apt-get install -y --no-install-recommends \
     curl \
     git \
+    libgl1 \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Set workdir
+WORKDIR /app
+
+# Salin requirements & install
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Salin file app (langsung di root — bukan src/)
+COPY app.py .
+COPY df_final.xlsx .
 
+# Port Streamlit (HF pakai 7860 atau 8501 — keduanya oke)
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# ✅ HEALTHCHECK aman: gunakan python, bukan curl
+HEALTHCHECK --interval=30s --timeout=5s --start-period=60s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8501/_stcore/health', timeout=3)"
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# ✅ ENTRYPOINT: tambahkan opsi headless & server address
+ENTRYPOINT ["streamlit", "run", "app.py", \
+    "--server.port=8501", \
+    "--server.address=0.0.0.0", \
+    "--server.headless=true", \
+    "--browser.serverAddress=0.0.0.0", \
+    "--browser.gatherUsageStats=false"]

README.md

@@ -1,5 +1,5 @@
 ---
-title: Tyre
+title: Michellin Str
 emoji: 🚀
 colorFrom: red
 colorTo: red

app.py

@@ -0,0 +1,1279 @@
+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 (Dark White + Power BI Style Filters + LLM Insight Boxes) =================
+st.markdown("""
+<style>
+/* ================= ROOT & COLORS ================= */
+:root {
+    --michelin-blue: #003A8F;
+    --surface: #FFFFFF;
+    --surface-alt: #F9FAFB;
+    --text-dark: #1F2D3D;
+    --text-muted: #6C757D;
+    --border: #E9ECEF;
+    --shadow-sm: 0 2px 6px rgba(0,0,0,0.04);
+    --shadow: 0 6px 16px rgba(0,0,0,0.06);
+    --accent-yellow: #FFD100;
+    --filter-bg: #F5F7FA;
+}
+
+/* ================= GLOBAL TYPOGRAPHY & ALIGNMENT ================= */
+.stApp {
+    background-color: var(--surface);
+    font-family: 'Segoe UI', system-ui, sans-serif;
+    color: var(--text-dark);
+}
+
+/* Force center alignment for ALL headings */
+h1, h2, h3, h4, h5, h6,
+.stMarkdown, .stText, p, div[data-testid="stMarkdownContainer"],
+label, .stSelectbox label, .stMultiselect label, .stCheckbox label {
+    text-align: center !important;
+}
+
+/* Fix bullet/number list centering */
+.stMarkdown ul, .stMarkdown ol {
+    text-align: left !important;
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 800px;
+}
+
+/* ================= SIDEBAR (Power BI Style) ================= */
+[data-testid="stSidebar"] {
+    background: var(--filter-bg) !important;
+    color: var(--text-dark);
+    padding: 20px 12px;
+}
+
+[data-testid="stSidebar"] h3 {
+    color: var(--michelin-blue);
+    font-weight: 700;
+    margin-bottom: 12px;
+}
+
+/* Power BI-style dropdowns */
+[data-testid="stSelectbox"] div[data-baseweb="select"],
+[data-testid="stMultiselect"] div[data-baseweb="select"] {
+    background-color: white !important;
+    border-radius: 8px !important;
+    border: 1px solid var(--border) !important;
+    box-shadow: var(--shadow-sm);
+    padding: 6px 10px !important;
+    min-height: 40px !important;
+}
+
+[data-testid="stSelectbox"] div[data-baseweb="select"] > div,
+[data-testid="stMultiselect"] div[data-baseweb="select"] > div {
+    color: var(--text-dark) !important;
+    font-weight: 500;
+}
+
+/* Remove red tags from multiselect */
+[data-testid="stMultiselect"] div[data-baseweb="select"] .stMultiSelectTag {
+    display: none !important;
+}
+
+/* Submit button */
+[data-testid="stSidebar"] .stButton > button {
+    width: 100%;
+    background: var(--accent-yellow);
+    color: var(--michelin-blue);
+    font-weight: 700;
+    border-radius: 10px;
+    padding: 12px 0;
+    margin-top: 16px;
+    box-shadow: var(--shadow);
+    border: none;
+    font-size: 1.05rem;
+}
+[data-testid="stSidebar"] .stButton > button:hover {
+    background: #FFC107;
+    transform: translateY(-1px);
+    box-shadow: 0 8px 16px rgba(0,0,0,0.12);
+}
+
+/* ================= HEADER ================= */
+.main-header h1 {
+    font-size: 2.4rem;
+    margin-bottom: 6px;
+    font-weight: 800;
+    color: var(--michelin-blue);
+}
+.main-header p {
+    font-size: 1.15rem;
+    color: var(--text-muted);
+    margin-top: 0;
+}
+
+/* ================= OBJECTIVE TITLE (NO BACKGROUND BOX) ================= */
+.objective-title {
+    text-align: center !important;
+    font-size: 1.6rem;
+    font-weight: 800;
+    color: var(--michelin-blue);
+    margin: 40px 0 24px 0;
+}
+
+/* ================= INSIGHT LLM-STYLE (Like Screenshot) ================= */
+.insight-box {
+    background: var(--surface-alt);
+    border: 1px solid var(--border);
+    border-radius: 12px;
+    padding: 20px;
+    box-shadow: var(--shadow-sm);
+    margin: 20px 0 30px 0;
+    position: relative;
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+}
+
+.insight-box .content {
+    flex: 1;
+    font-size: 1.05rem;
+    line-height: 1.65;
+    color: var(--text-dark);
+    text-align: left;
+}
+
+.insight-box .tag {
+    position: absolute;
+    top: 12px;
+    right: 16px;
+    background: var(--michelin-blue);
+    color: white;
+    font-size: 0.85rem;
+    font-weight: 700;
+    padding: 6px 12px;
+    border-radius: 8px;
+    letter-spacing: 0.5px;
+}
+
+/* ================= PLOTLY ================= */
+.plotly-graph-div {
+    border-radius: 12px;
+    overflow: hidden;
+    box-shadow: var(--shadow-sm);
+    border: 1px solid var(--border);
+}
+
+/* ================= LOGO ================= */
+.logo-container {
+    position: fixed;
+    top: 20px;
+    right: 20px;
+    z-index: 9999;
+    background: white;
+    padding: 6px;
+    border-radius: 10px;
+    box-shadow: var(--shadow);
+    border: 1px solid var(--border);
+}
+.logo-container img {
+    width: 120px;
+    height: auto;
+}
+
+/* ================= FOOTER ================= */
+.footer {
+    text-align: center;
+    font-size: 0.9rem;
+    color: var(--text-muted);
+    margin-top: 50px;
+    padding: 20px 0;
+    border-top: 1px solid var(--border);
+}
+
+/* ================= STREAMLIT TWEAKS ================= */
+div.block-container {
+    padding-top: 2rem;
+}
+section[data-testid="stSidebar"] {
+    width: 280px !important;
+    min-width: 280px !important;
+}
+</style>
+""", unsafe_allow_html=True)
+
+
+# ================= LOAD DATA =================
+@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()
+
+# ================= HEADER =================
+st.markdown("""
+<div class="main-header" style="text-align:center;">
+    <h1>Michelin Mining Tyre Analytics</h1>
+    <p style="font-size:14px; color:#b0b0b0; margin-top:-10px;">
+        Analysis is based on daily aggregated data
+    </p>
+</div>
+""", unsafe_allow_html=True)
+
+
+# ================= LOGO =================
+st.markdown("""
+<div style="text-align:right;">
+    <img src="logo.png" width="120">
+</div>
+""", unsafe_allow_html=True)
+
+# ================= 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 =================
+# ================= OBJECTIVE 1 =================
+# st.markdown('<h3 class="objective-title">OBJECTIVE 1: Pressure & Temperature Trends — How Do Front and Rear Tyres Distributin?</h3>', unsafe_allow_html=True)
+
+# col1, col2 = st.columns(2)
+
+# with col1:
+#     st.markdown('<h5 style="text-align:center; margin-top: 0;">Pressure Distribution per Tyre Position</h5>', unsafe_allow_html=True)
+#     fig1 = px.box(
+#         dff,
+#         x='Position',
+#         y='Pressure (psi)',
+#         color='Position',
+#         color_discrete_map={1: '#d50000', 2: '#ff6d00', 3: '#ffcc00', 4: '#007acc'},
+#         template="plotly_white"
+#     )
+#     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))
+#     st.plotly_chart(fig1, use_container_width=True)
+
+# with col2:
+#     st.markdown('<h5 style="text-align:center; margin-top: 0;">Temperature Distribution per Tyre Position</h5>', unsafe_allow_html=True)
+#     fig2 = px.box(
+#         dff,
+#         x='Position',
+#         y='Temperature (°C)',
+#         color='Position',
+#         color_discrete_map={1: '#d50000', 2: '#ff6d00', 3: '#ffcc00', 4: '#007acc'},
+#         template="plotly_white"
+#     )
+#     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))
+#     st.plotly_chart(fig2, use_container_width=True)
+
+# Insight 1
+# Insight 1
+# ================= OBJECTIVE 1 =================
+# Ensure 'Position' is treated as ordered categorical for consistent sorting
+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('<h3 class="objective-title">OBJECTIVE 1: Pressure & Temperature Trends — How Do Front and Rear Tyres Distribute?</h3>', 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('<h5 style="text-align:center; margin-top: 0;">Pressure Distribution per Tyre Position</h5>', 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('<h5 style="text-align:center; margin-top: 0;">Temperature Distribution per Tyre Position</h5>', 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"""
+<div class="insight-box">
+    <div class="content">
+{insight_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+# ================= OBJECTIVE 2 =================
+# ================= OBJECTIVE 2 =================
+# ================= OBJECTIVE 2 =================
+st.markdown('<h3 class="objective-title">OBJECTIVE 2: Alarm Frequency Analysis — When, Where, and Which Tyres Matter Most?</h3>', unsafe_allow_html=True)
+
+col_a, col_b = st.columns(2)
+
+# --- COL A: Alarm Distribution by Hour (Polar Chart) ---
+with col_a:
+    st.markdown('<h5 style="text-align:center; margin-top: 0;">Alarm Distribution by Hour</h5>', 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='<b>Hour:</b> %{theta:0f}:00<br><b>Alarms:</b> %{r}<extra></extra>'
+                ))
+        
+        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('<h5 style="text-align:center; margin-top: 0;">Alarm Hotspots by Tyre, Position & Zone</h5>', 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"""
+<div class="insight-box">
+    <div class="content">
+{insight_text}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+# ================= OBJECTIVE 3 =================
+st.markdown('<h3 class="objective-title">OBJECTIVE 3: 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 ===============
+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:
+        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="<b>%{marker.color}</b><br>Temp: %{x:.1f}°C<br>Pressure: %{y:.1f} psi<extra></extra>",
+                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('<h5 style="text-align:center; margin-top: 0;">Front Tyres: Temperature → Speed</h5>', 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="<b>%{marker.color}</b><br>Temp: %{x:.1f}°C<br>Speed: %{y:.1f} km/h<extra></extra>",
+                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('<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' 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="<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=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('<h5 style="text-align:center; margin-top: 0;">Rear Tyres: Temperature → Speed</h5>', 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="<b>%{marker.color}</b><br>Temp: %{x:.1f}°C<br>Speed: %{y:.1f} km/h<extra></extra>",
+                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"""
+<div class="insight-box">
+    <div class="content">
+{insight_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+# ================= OBJECTIVE 4 =================
+st.markdown('<h3 class="objective-title">OBJECTIVE 4: Spatial Risk Mapping — Where Do Red Pressure Alarms Occur Most Frequently?</h3>', unsafe_allow_html=True)
+
+st.markdown('<h5 style="text-align:center; margin-top: 0;">Tyre Alarms Distribution by Location</h5>', 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"""
+        <div style="font-family:Segoe UI; font-size:13px; line-height:1.4">
+        <b>SN:</b> {r['TyreSN']} | Pos: {int(r['Position'])}<br>
+        <b>Zone:</b> {r['Zone']}<br>
+        <b>Press:</b> {r['Pressure (psi)']:.1f} psi<br>
+        <b>Temp:</b> {r['Temperature (°C)']:.1f} °C<br>
+        <b>Speed:</b> {r['Speed (km/h)']:.1f} km/h<br>
+        <b>Alarm:</b> {r['Alarm Status']}
+        </div>
+        """
+        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 = '''
+    <div style="
+        position: fixed; 
+        bottom: 60px; right: 20px; 
+        background: white; 
+        border: 1px solid #E9ECEF;
+        border-radius: 10px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.08);
+        padding: 12px; 
+        font-family: Segoe UI;
+        font-size: 13px;
+        z-index: 9999;
+    ">
+        <b>Legend</b><br>
+        <span style="color:#2E7D32">●</span> Normal (No Alarm)<br>
+        <span style="color:#D32F2F">●</span> Red Pressure<br>
+        <span style="color:#1976D2">▲</span> Front Tyre<br>
+        <span style="color:#1976D2">★</span> Rear Tyre<br>
+        <i>Size ∝ Temperature</i>
+    </div>
+    '''
+    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"""
+<div class="insight-box">
+    <div class="content">
+{insight_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+# ================= OBJECTIVE 5 =================
+# ================= OBJECTIVE 5 =================
+st.markdown('<h3 class="objective-title">OBJECTIVE 5: Insights & Mitigation — How Can Red Pressure Alarms Be Reduced?</h3>', 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).
+<br>
+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).
+<br>
+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).
+<br>
+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"]
+
+    # Pisahkan recommendation dan risk mitigation dari generated text
+    # Di sini Anda bisa menambahkan kode untuk memisahkan recommendation dan risk mitigation dari generated text
+    # Contoh: recommendation_text, risk_mitigation_text = generated_text.split("Risk Mitigation")
+    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).
+<br>
+2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency (Objective 2).
+<br>
+3. Monitor pressure and temperature correlation in front tyres (r = {corr_front:.2f}) to prevent overheating and premature wear (Objective 3).
+<br>
+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).
+<br>
+2. Schedule additional inspections during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) when {dominant_percentage:.1f}% of alarms occur (Objective 2).
+<br>
+3. Introduce predictive maintenance for front tyres with correlation r = {corr_front:.2f} to prevent unplanned downtime (Objective 3).
+<br>
+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).
+<br>
+2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency (Objective 2).
+<br>
+3. Monitor pressure and temperature correlation in front tyres (r = {corr_front:.2f}) to prevent overheating and premature wear (Objective 3).
+<br>
+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).
+<br>
+2. Schedule additional inspections during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) when {dominant_percentage:.1f}% of alarms occur (Objective 2).
+<br>
+3. Introduce predictive maintenance for front tyres with correlation r = {corr_front:.2f} to prevent unplanned downtime (Objective 3).
+<br>
+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('<h4 style="text-align:center; margin:10px 0 5px 0; font-weight:bold;">INSIGHT</h4>', unsafe_allow_html=True)
+st.markdown(f"""
+<div class="insight-box">
+    <div class="content">
+{insight_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# ============== SUBHEADER + BOX 2: RECOMMENDATION ==============
+st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">RECOMMENDATION</h4>', unsafe_allow_html=True)
+st.markdown(f"""
+<div class="insight-box">
+    <div class="content">
+{recommendation_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# ============== SUBHEADER + BOX 3: RISK MITIGATION ==============
+st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">RISK MITIGATION</h4>', unsafe_allow_html=True)
+st.markdown(f"""
+<div class="insight-box">
+    <div class="content">
+{risk_mitigation_text.strip()}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# ================= FOOTER =================
+st.markdown("""
+<div class="footer">
+    Michelin Mining Tyre Analytics
+</div>
+""", unsafe_allow_html=True)

gitattributes

@@ -0,0 +1 @@
+logo.png filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -1,3 +1,12 @@
-altair
-pandas
-streamlit
+streamlit==1.39.0
+pandas==2.2.2
+numpy==1.26.4
+openpyxl==3.1.5
+plotly==5.22.0
+folium==0.17.0
+streamlit-folium==0.17.0
+torch==2.1.0
+transformers==4.35.0
+accelerate==0.24.0
+sentencepiece==0.2.0
+scikit-learn==1.3.0