Update app.py

app.py

@@ -2,50 +2,103 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 
-st.set_page_config(page_title="Fatigue Dashboard", layout="wide")
+# =============== STREAMLIT CONFIG ==================
+st.set_page_config(
+    page_title="Fatigue Camera Dashboard",
+    layout="wide",
+    page_icon="😴"
+)
 
-# Load Data
-df = pd.read_excel("manual fatique.xlsx")
+# =============== APP HEADER =========================
+st.title("😴📊 Fatigue & Distraction Camera Safety Dashboard")
+st.write("Upload data dari fatigue monitoring system untuk analisis safety.")
 
-# Normalize timestamp
-timestamp_col = [col for col in df.columns if "GMT" in col or "timestamp" in col.lower()][0]
-df["Timestamp"] = pd.to_datetime(df[timestamp_col])
+# =============== FILE UPLOADER ======================
+uploaded_file = st.file_uploader("📂 Upload File (Excel)", type=["xlsx", "xls"])
 
-st.title("⛑️ Fatigue Monitoring Dashboard")
+if uploaded_file:
+    df = pd.read_excel(uploaded_file)
+    
+    st.subheader("📄 Data Preview")
+    st.dataframe(df, use_container_width=True)
 
-# Sidebar Filters
-shift_filter = st.sidebar.multiselect("Filter Shift", df['Shift'].unique())
-unit_filter = st.sidebar.multiselect("Filter Unit", df['Fleet Number'].unique())
+    # =============== CLEANING OTOMATIS ===============
+    df.columns = df.columns.str.lower().str.replace(" ", "_").str.strip()
 
-filtered = df.copy()
-if shift_filter:
-    filtered = filtered[filtered['Shift'].isin(shift_filter)]
-if unit_filter:
-    filtered = filtered[filtered['Fleet Number'].isin(unit_filter)]
+    # Deteksi kolom penting
+    col_time = next((c for c in df.columns if "time" in c or "timestamp" in c or "date" in c), None)
+    col_operator = next((c for c in df.columns if "operator" in c or "driver" in c or "employee" in c), None)
+    col_fatigue = next((c for c in df.columns if "fatigue" in c), None)
+    col_distraction = next((c for c in df.columns if "distract" in c), None)
 
-# KPI Metrics
-col1, col2 = st.columns(2)
-col1.metric("Total Fatigue Alerts", len(filtered))
-col2.metric("Unique Operators", filtered['Operator Name'].nunique())
+    st.write("🧠 Kolom terdeteksi:")
+    st.code(f"""
+Time Column: {col_time}
+Operator Column: {col_operator}
+Fatigue Column: {col_fatigue}
+Distraction Column: {col_distraction}
+""")
 
-# Trend per day
-trend = filtered.groupby(filtered["Timestamp"].dt.date).size().reset_index(name="Count")
-fig = px.line(trend, x="Timestamp", y="Count", title="📈 Trend Fatigue Alerts Per Hari")
-st.plotly_chart(fig, use_container_width=True)
+    # =============== KPI SUMMARY =====================
+    st.divider()
+    st.subheader("📌 Safety Event Summary")
 
-# Shift comparison
-fig2 = px.bar(filtered, x="Shift", title="⚠️ Distribusi Alert per Shift", color="Shift")
-st.plotly_chart(fig2, use_container_width=True)
+    cols = st.columns(3)
 
-# Top Units
-unit = filtered.groupby("Fleet Number").size().reset_index(name="Count").sort_values("Count", ascending=False)
-fig3 = px.bar(unit.head(10), x="Fleet Number", y="Count", title="🚛 Top 10 Unit dengan Alert Tertinggi")
-st.plotly_chart(fig3, use_container_width=True)
+    if col_fatigue:
+        total_fatigue = df[col_fatigue].sum()
+        cols[0].metric("Total Fatigue Alerts", total_fatigue)
 
-# Heatmap jam kejadian
-filtered['Hour'] = filtered['Timestamp'].dt.hour
-hourly = filtered.groupby('Hour').size().reset_index(name="Count")
-fig4 = px.bar(hourly, x='Hour', y='Count', title="🕒 Peak Hour Fatigue Alerts")
-st.plotly_chart(fig4, use_container_width=True)
+    if col_distraction:
+        total_distraction = df[col_distraction].sum()
+        cols[1].metric("Total Distraction Alerts", total_distraction)
 
-st.caption("Created for Safety Improvement & Predictive Behavior Monitoring")
+    if col_operator:
+        operator_count = df[col_operator].nunique()
+        cols[2].metric("Operators Monitored", operator_count)
+
+    # =============== TREND CHART =====================
+    if col_time and col_fatigue:
+        df[col_time] = pd.to_datetime(df[col_time], errors="coerce")
+
+        fig = px.line(
+            df.sort_values(col_time),
+            x=col_time,
+            y=col_fatigue,
+            title="📈 Trend Fatigue Events Over Time"
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+    # =============== OPERATOR RANKING =================
+    if col_operator and col_fatigue:
+        st.subheader("🏆 Operator Ranking (Based on Fatigue Events)")
+        
+        rank_df = df.groupby(col_operator)[col_fatigue].sum().sort_values(ascending=False)
+        st.bar_chart(rank_df)
+
+    # =============== AUTO INSIGHT =====================
+    st.divider()
+    st.subheader("🧩 AI Insight (Auto Summary)")
+
+    insight = []
+
+    if col_time and col_fatigue:
+        peak_hour = df.groupby(df[col_time].dt.hour)[col_fatigue].sum().idxmax()
+        insight.append(f"⏰ **Jam paling rawan:** {peak_hour}:00")
+
+    if col_operator:
+        worst_operator = rank_df.index[0]
+        insight.append(f"��️ Operator dengan alert tertinggi: **{worst_operator}** → perlu coaching / fatigue management.")
+
+    if col_distraction and df[col_distraction].sum() > df[col_fatigue].sum():
+        insight.append("🚨 Distraction lebih sering daripada fatigue — kemungkinan faktor lingkungan atau policy.")
+
+    for i in insight:
+        st.write(i)
+
+# ================= HUGGING FACE FIX ==================
+if __name__ == "__main__":
+    import streamlit.web.cli as stcli
+    import sys
+    sys.argv = ["streamlit", "run", "app.py", "--server.enableCORS=false", "--server.enableXsrfProtection=false"]
+    sys.exit(stcli.main())