Update app.py

app.py

@@ -4,174 +4,189 @@ import plotly.express as px
 import plotly.graph_objects as go
 import numpy as np
 from datetime import datetime, timedelta
-import json
 
 # ==========================================
-# 1. ENHANCED PAGE CONFIGURATION
+# PAGE CONFIGURATION
 # ==========================================
 st.set_page_config(
-    page_title="Project Sentinel | UIDAI Fraud Detection System",
-    page_icon="🛡️",
+    page_title="Sentinel | UIDAI Fraud Detection",
+    page_icon="🛡",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
 # ==========================================
-# 2. ADVANCED CUSTOM STYLING
+# PROFESSIONAL STYLING
 # ==========================================
 st.markdown("""
     <style>
-        /* Professional Government Portal Theme */
         @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
         
         .main {
-            background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+            background-color: #f5f7fa;
             font-family: 'Inter', sans-serif;
         }
         
-        /* Enhanced Metric Cards */
         .stMetric {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            padding: 20px;
-            border-radius: 10px;
-            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
-            color: white !important;
+            background-color: white;
+            padding: 18px;
+            border-radius: 6px;
+            border-left: 4px solid #3b82f6;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.08);
         }
         
         .stMetric label {
-            color: rgba(255,255,255,0.9) !important;
-            font-weight: 600 !important;
+            font-weight: 500 !important;
+            color: #64748b !important;
+            font-size: 14px !important;
         }
         
         .stMetric [data-testid="stMetricValue"] {
-            color: white !important;
-            font-size: 32px !important;
-            font-weight: 700 !important;
+            font-size: 28px !important;
+            font-weight: 600 !important;
+            color: #1e293b !important;
         }
         
-        /* Headers */
-        h1, h2, h3 {
-            color: #2c3e50;
+        h1 {
+            color: #1e293b;
             font-weight: 700;
+            font-size: 32px;
         }
         
-        h1 {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            -webkit-background-clip: text;
-            -webkit-text-fill-color: transparent;
-            background-clip: text;
+        h2 {
+            color: #334155;
+            font-weight: 600;
+            font-size: 24px;
+        }
+        
+        h3 {
+            color: #475569;
+            font-weight: 600;
+            font-size: 18px;
         }
         
-        /* Sidebar Styling */
         [data-testid="stSidebar"] {
-            background: linear-gradient(180deg, #1e3c72 0%, #2a5298 100%);
+            background-color: #1e3a5f;
         }
         
         [data-testid="stSidebar"] * {
-            color: white !important;
+            color: #e2e8f0 !important;
         }
         
-        /* Alert Boxes */
-        .alert-critical {
-            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
-            padding: 15px;
-            border-radius: 8px;
-            color: white;
-            font-weight: 600;
-            margin: 10px 0;
-            box-shadow: 0 4px 12px rgba(255,107,107,0.3);
+        [data-testid="stSidebar"] .stSelectbox label,
+        [data-testid="stSidebar"] .stMultiSelect label,
+        [data-testid="stSidebar"] .stCheckbox label {
+            font-weight: 500 !important;
+            font-size: 14px !important;
         }
         
-        .alert-warning {
-            background: linear-gradient(135deg, #ffd93d 0%, #ff9a00 100%);
-            padding: 15px;
-            border-radius: 8px;
-            color: #2c3e50;
+        .status-badge {
+            display: inline-block;
+            padding: 4px 12px;
+            border-radius: 12px;
+            font-size: 12px;
             font-weight: 600;
-            margin: 10px 0;
-            box-shadow: 0 4px 12px rgba(255,217,61,0.3);
+            letter-spacing: 0.3px;
         }
         
-        .alert-safe {
-            background: linear-gradient(135deg, #6bcf7f 0%, #4caf50 100%);
-            padding: 15px;
-            border-radius: 8px;
-            color: white;
-            font-weight: 600;
-            margin: 10px 0;
-            box-shadow: 0 4px 12px rgba(107,207,127,0.3);
+        .status-critical {
+            background-color: #fee2e2;
+            color: #991b1b;
+        }
+        
+        .status-high {
+            background-color: #fed7aa;
+            color: #9a3412;
+        }
+        
+        .status-normal {
+            background-color: #d1fae5;
+            color: #065f46;
+        }
+        
+        .info-card {
+            background-color: white;
+            padding: 16px;
+            border-radius: 6px;
+            border-left: 3px solid #3b82f6;
+            margin: 12px 0;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.08);
+        }
+        
+        .info-card-warning {
+            border-left-color: #f59e0b;
+        }
+        
+        .info-card-danger {
+            border-left-color: #ef4444;
+        }
+        
+        .info-card-success {
+            border-left-color: #10b981;
         }
         
-        /* Data Table Enhancement */
         [data-testid="stDataFrame"] {
-            border-radius: 10px;
-            overflow: hidden;
-            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+            border: 1px solid #e2e8f0;
+            border-radius: 6px;
         }
         
-        /* Button Styling */
         .stDownloadButton button {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            background-color: #3b82f6;
             color: white;
             border: none;
-            padding: 12px 30px;
-            border-radius: 8px;
-            font-weight: 600;
-            box-shadow: 0 4px 12px rgba(102,126,234,0.3);
-            transition: transform 0.2s;
+            padding: 8px 20px;
+            border-radius: 6px;
+            font-weight: 500;
+            font-size: 14px;
+            transition: background-color 0.2s;
         }
         
         .stDownloadButton button:hover {
-            transform: translateY(-2px);
-            box-shadow: 0 6px 20px rgba(102,126,234,0.4);
+            background-color: #2563eb;
         }
         
-        /* Tab Styling */
         .stTabs [data-baseweb="tab-list"] {
-            gap: 8px;
+            gap: 4px;
         }
         
         .stTabs [data-baseweb="tab"] {
-            background-color: rgba(255,255,255,0.7);
-            border-radius: 8px 8px 0 0;
+            background-color: white;
+            border-radius: 6px 6px 0 0;
             padding: 10px 20px;
-            font-weight: 600;
+            font-weight: 500;
+            color: #64748b;
         }
         
         .stTabs [aria-selected="true"] {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white !important;
+            background-color: #3b82f6;
+            color: white;
         }
         
-        /* Pulse Animation for Critical Alerts */
-        @keyframes pulse {
-            0%, 100% { opacity: 1; }
-            50% { opacity: 0.7; }
+        .metric-delta-positive {
+            color: #10b981 !important;
         }
         
-        .pulse {
-            animation: pulse 2s infinite;
+        .metric-delta-negative {
+            color: #ef4444 !important;
         }
     </style>
 """, unsafe_allow_html=True)
 
 # ==========================================
-# 3. ENHANCED DATA LOADING WITH ANALYTICS
+# DATA LOADING
 # ==========================================
 @st.cache_data
 def load_data():
-    """Load and preprocess data with advanced analytics"""
     try:
         df = pd.read_csv('analyzed_aadhaar_data.csv')
         
-        # Date processing
         if 'date' in df.columns:
             df['date'] = pd.to_datetime(df['date'])
             df['month'] = df['date'].dt.month
             df['year'] = df['date'].dt.year
             df['day_name'] = df['date'].dt.day_name()
         
-        # Enhanced geospatial (production note included)
+        # Geographic coordinates (production: integrate with pincode database)
         np.random.seed(42)
         df['lat'] = np.random.uniform(20.0, 28.0, size=len(df))
         df['lon'] = np.random.uniform(77.0, 85.0, size=len(df))
@@ -183,53 +198,48 @@ def load_data():
             labels=['Low', 'Medium', 'High', 'Critical']
         )
         
-        # Trend indicators (simulated - in production would compare to historical data)
-        df['trend'] = np.random.choice(['↑', '→', '↓'], size=len(df), p=[0.3, 0.4, 0.3])
-        
         return df
     except FileNotFoundError:
-        st.error("⚠️ File 'analyzed_aadhaar_data.csv' not found. Please run the Notebook first.")
+        st.error("Data file not found. Please ensure 'analyzed_aadhaar_data.csv' exists.")
         return pd.DataFrame()
 
 @st.cache_data
 def calculate_insights(df):
-    """Calculate advanced analytics and insights"""
     insights = {
         'total_cases': len(df),
         'critical_cases': len(df[df['RISK_SCORE'] > 85]),
         'high_risk_cases': len(df[df['RISK_SCORE'] > 70]),
         'avg_risk': df['RISK_SCORE'].mean(),
         'max_risk': df['RISK_SCORE'].max(),
-        'weekend_fraud_rate': len(df[(df['is_weekend'] == 1) & (df['RISK_SCORE'] > 70)]) / len(df) * 100,
-        'top_state': df.groupby('state')['RISK_SCORE'].mean().idxmax() if len(df) > 0 else 'N/A',
-        'most_active_day': df['day_name'].mode()[0] if 'day_name' in df.columns and len(df) > 0 else 'N/A'
+        'weekend_fraud_rate': len(df[(df['is_weekend'] == 1) & (df['RISK_SCORE'] > 70)]) / len(df) * 100 if len(df) > 0 else 0,
+        'top_state': df.groupby('state')['RISK_SCORE'].mean().idxmax() if len(df) > 0 else 'N/A'
     }
     return insights
 
 # ==========================================
-# 4. LOAD DATA
+# LOAD DATA
 # ==========================================
 df = load_data()
 
 if df.empty:
-    st.error("⚠️ No data available. Please ensure the data file exists.")
+    st.error("No data available. Please check the data file.")
     st.stop()
 
 insights = calculate_insights(df)
 
 # ==========================================
-# 5. ENHANCED SIDEBAR WITH ADVANCED FILTERS
+# SIDEBAR FILTERS
 # ==========================================
 with st.sidebar:
-    st.image("https://upload.wikimedia.org/wikipedia/en/c/cf/Aadhaar_Logo.svg", width=150)
-    st.title("🛡️ Sentinel Control Panel")
+    st.image("https://upload.wikimedia.org/wikipedia/en/c/cf/Aadhaar_Logo.svg", width=140)
+    st.title("Control Panel")
     st.markdown("---")
     
-    # Date Range Filter
-    st.subheader("📅 Date Range")
+    # Date Range
+    st.subheader("Date Range")
     if 'date' in df.columns and not df['date'].isna().all():
         date_range = st.date_input(
-            "Select Date Range",
+            "Select Period",
             value=(df['date'].min(), df['date'].max()),
             min_value=df['date'].min(),
             max_value=df['date'].max()
@@ -244,10 +254,10 @@ with st.sidebar:
     
     st.markdown("---")
     
-    # Risk Level Filter
-    st.subheader("⚠️ Risk Level")
+    # Risk Level
+    st.subheader("Risk Level")
     risk_filter = st.multiselect(
-        "Filter by Risk Category",
+        "Categories",
         options=['Low', 'Medium', 'High', 'Critical'],
         default=['High', 'Critical']
     )
@@ -257,8 +267,8 @@ with st.sidebar:
     
     st.markdown("---")
     
-    # Geographic Filters
-    st.subheader("🗺️ Geographic Filters")
+    # Geographic
+    st.subheader("Location")
     state_list = ['All'] + sorted(filtered_df['state'].unique().tolist())
     selected_state = st.selectbox("State", state_list)
     
@@ -276,67 +286,54 @@ with st.sidebar:
     st.markdown("---")
     
     # Weekend Filter
-    show_weekend_only = st.checkbox("🔴 Weekend Anomalies Only", value=False)
+    show_weekend_only = st.checkbox("Weekend Activity Only", value=False)
     if show_weekend_only:
         filtered_df = filtered_df[filtered_df['is_weekend'] == 1]
     
     st.markdown("---")
     
     # Session Info
-    st.markdown("""
-        <div style='background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px;'>
-            <strong>👤 User:</strong> Vigilance Officer (L1)<br>
-            <strong>🔐 Session:</strong> UIDAI_4571_SECURE<br>
-            <strong>⏰ Login:</strong> {}<br>
-            <strong>📊 Active Filters:</strong> {}
+    st.markdown(f"""
+        <div style='background: rgba(255,255,255,0.1); padding: 12px; border-radius: 6px; font-size: 13px;'>
+            <strong>User:</strong> Vigilance Officer<br>
+            <strong>Session:</strong> UIDAI_4571<br>
+            <strong>Time:</strong> {datetime.now().strftime("%H:%M:%S")}<br>
+            <strong>Filters Active:</strong> {len([f for f in [selected_state, selected_district, risk_filter, show_weekend_only] if f not in ['All', False, []]])}
         </div>
-    """.format(
-        datetime.now().strftime("%H:%M:%S"),
-        len([f for f in [selected_state, selected_district, risk_filter, show_weekend_only] if f not in ['All', False, []]])
-    ), unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
 
 # ==========================================
-# 6. MAIN DASHBOARD - ENHANCED HEADER
+# HEADER
 # ==========================================
-col1, col2, col3 = st.columns([3, 1, 1])
+col1, col2 = st.columns([3, 1])
 
 with col1:
-    st.title("🛡️ Project Sentinel: AI-Powered Fraud Detection")
-    st.markdown("### Context-Aware Anomaly Detection for Aadhaar Enrolment Centers")
+    st.title("Project Sentinel")
+    st.markdown("**Context-Aware Fraud Detection for Aadhaar Enrolment Centers**")
 
 with col2:
-    st.markdown(f"""
-        <div style='text-align: right; padding: 10px;'>
-            <strong>📅 Data Date:</strong> {pd.Timestamp.now().strftime('%d-%b-%Y')}<br>
-            <strong>⏰ Last Update:</strong> {datetime.now().strftime('%H:%M:%S')}
-        </div>
-    """, unsafe_allow_html=True)
-
-with col3:
     if insights['critical_cases'] > 0:
-        st.markdown("""
-            <div class='alert-critical pulse' style='text-align: center;'>
-                🚨 CRITICAL ALERTS<br>
-                <span style='font-size: 24px;'>{}</span>
+        st.markdown(f"""
+            <div class='status-badge status-critical' style='font-size: 14px; padding: 8px 16px;'>
+                ⚠ {insights['critical_cases']} Critical Alerts
             </div>
-        """.format(insights['critical_cases']), unsafe_allow_html=True)
+        """, unsafe_allow_html=True)
     else:
         st.markdown("""
-            <div class='alert-safe' style='text-align: center;'>
-                ✅ SYSTEM NORMAL
+            <div class='status-badge status-normal' style='font-size: 14px; padding: 8px 16px;'>
+                ✓ System Normal
             </div>
         """, unsafe_allow_html=True)
 
-st.divider()
+st.markdown("---")
 
 # ==========================================
-# 7. ENHANCED KPI DASHBOARD WITH 6 METRICS
+# KPI METRICS
 # ==========================================
-st.subheader("📊 Real-Time Intelligence Dashboard")
+st.subheader("System Overview")
 
 kpi1, kpi2, kpi3, kpi4, kpi5, kpi6 = st.columns(6)
 
-# Calculate metrics
 total_centers = len(filtered_df)
 critical_alerts = len(filtered_df[filtered_df['RISK_SCORE'] > 85])
 high_risk_centers = len(filtered_df[filtered_df['RISK_SCORE'] > 70])
@@ -345,72 +342,39 @@ weekend_anomalies = len(filtered_df[(filtered_df['is_weekend'] == 1) & (filtered
 max_deviation = filtered_df['ratio_deviation'].max() if 'ratio_deviation' in filtered_df.columns else 0
 
 with kpi1:
-    st.metric(
-        "Total Cases",
-        f"{total_centers:,}",
-        delta=f"{int(total_centers*0.08)} from yesterday",
-        delta_color="off"
-    )
+    st.metric("Cases", f"{total_centers:,}", f"+{int(total_centers*0.08)}", delta_color="off")
 
 with kpi2:
-    st.metric(
-        "🔴 Critical",
-        f"{critical_alerts}",
-        delta=f"+{int(critical_alerts*0.15)} vs last week",
-        delta_color="inverse"
-    )
+    st.metric("Critical", f"{critical_alerts}", f"+{int(critical_alerts*0.15)}", delta_color="inverse")
 
 with kpi3:
-    st.metric(
-        "⚠️ High Risk",
-        f"{high_risk_centers}",
-        delta=f"+{int(high_risk_centers*0.12)} this week",
-        delta_color="inverse"
-    )
+    st.metric("High Risk", f"{high_risk_centers}", f"+{int(high_risk_centers*0.12)}", delta_color="inverse")
 
 with kpi4:
-    st.metric(
-        "Avg Risk Score",
-        f"{avg_risk:.1f}",
-        delta=f"{avg_risk - 65:.1f} vs baseline",
-        delta_color="inverse"
-    )
+    st.metric("Avg Risk", f"{avg_risk:.1f}", f"{avg_risk - 65:.1f}", delta_color="inverse")
 
 with kpi5:
-    st.metric(
-        "Weekend Spikes",
-        f"{weekend_anomalies}",
-        delta="Unauthorized ops",
-        delta_color="inverse"
-    )
+    st.metric("Weekend", f"{weekend_anomalies}", "Unauthorized", delta_color="off")
 
 with kpi6:
-    st.metric(
-        "Max Deviation",
-        f"{max_deviation:.2f}",
-        delta="From district avg",
-        delta_color="off"
-    )
+    st.metric("Max Dev", f"{max_deviation:.2f}", "From baseline", delta_color="off")
 
-st.divider()
+st.markdown("---")
 
 # ==========================================
-# 8. TABBED INTERFACE FOR BETTER ORGANIZATION
+# TABS
 # ==========================================
-tab1, tab2, tab3, tab4 = st.tabs(["🗺️ Geographic Analysis", "📈 Pattern Analysis", "📋 Priority Cases", "📊 Advanced Analytics"])
+tab1, tab2, tab3, tab4 = st.tabs(["Geographic Analysis", "Pattern Detection", "Priority Cases", "Analytics"])
 
 # ==========================================
-# TAB 1: GEOGRAPHIC ANALYSIS
+# TAB 1: GEOGRAPHIC
 # ==========================================
 with tab1:
-    st.markdown("### 🗺️ Geographic Risk Distribution")
-    
     col_map1, col_map2 = st.columns([2, 1])
     
     with col_map1:
-        st.info("💡 Visualizing fraud risk across India. Circle size = transaction volume, Color = risk score")
+        st.subheader("Risk Distribution Map")
         
-        # Enhanced map
         map_fig = px.scatter_mapbox(
             filtered_df,
             lat="lat",
@@ -422,34 +386,26 @@ with tab1:
                 "district": True,
                 "enrol_adult": True,
                 "ratio_deviation": ':.2f',
-                "risk_category": True,
                 "lat": False,
                 "lon": False,
                 "total_activity": True
             },
-            color_continuous_scale=["#2ecc71", "#f1c40f", "#e67e22", "#e74c3c"],
+            color_continuous_scale=["#10b981", "#fbbf24", "#f59e0b", "#ef4444"],
             zoom=4 if selected_state == 'All' else 6,
-            height=600,
+            height=550,
             mapbox_style="carto-positron"
         )
         
         map_fig.update_layout(
             margin={"r":0,"t":0,"l":0,"b":0},
-            coloraxis_colorbar=dict(
-                title="Risk Score",
-                thicknessmode="pixels",
-                thickness=15,
-                lenmode="pixels",
-                len=200
-            )
+            coloraxis_colorbar=dict(title="Risk Score", thickness=15, len=200)
         )
         
         st.plotly_chart(map_fig, use_container_width=True)
     
     with col_map2:
-        st.markdown("#### 🎯 Geographic Insights")
+        st.subheader("Top Risk Locations")
         
-        # Top risky states/districts
         if selected_state == 'All':
             top_locations = filtered_df.groupby('state')['RISK_SCORE'].agg(['mean', 'count']).sort_values('mean', ascending=False).head(5)
             location_type = "States"
@@ -457,44 +413,47 @@ with tab1:
             top_locations = filtered_df.groupby('district')['RISK_SCORE'].agg(['mean', 'count']).sort_values('mean', ascending=False).head(5)
             location_type = "Districts"
         
-        st.markdown(f"**Top 5 Riskiest {location_type}:**")
-        
         for idx, (location, row) in enumerate(top_locations.iterrows(), 1):
             risk_score = row['mean']
             count = int(row['count'])
             
             if risk_score > 85:
-                badge_color = "#e74c3c"
-                emoji = "🔴"
+                badge_class = "status-critical"
+                indicator = "●"
             elif risk_score > 70:
-                badge_color = "#e67e22"
-                emoji = "🟠"
+                badge_class = "status-high"
+                indicator = "●"
             else:
-                badge_color = "#f1c40f"
-                emoji = "🟡"
+                badge_class = "status-normal"
+                indicator = "●"
             
             st.markdown(f"""
-                <div style='background: {badge_color}; color: white; padding: 10px; border-radius: 8px; margin: 8px 0;'>
-                    <strong>{emoji} #{idx} {location}</strong><br>
-                    Risk: {risk_score:.1f} | Cases: {count}
+                <div class='info-card'>
+                    <div style='display: flex; justify-content: space-between; align-items: center;'>
+                        <div>
+                            <span style='font-weight: 600; font-size: 16px;'>{idx}. {location}</span><br>
+                            <span style='color: #64748b; font-size: 13px;'>Risk: {risk_score:.1f} | Cases: {count}</span>
+                        </div>
+                        <span class='status-badge {badge_class}'>{indicator}</span>
+                    </div>
                 </div>
             """, unsafe_allow_html=True)
         
-        st.markdown("---")
+        st.markdown("<br>", unsafe_allow_html=True)
         
-        # Risk distribution pie chart
+        # Distribution pie
         risk_dist = filtered_df['risk_category'].value_counts()
         
         pie_fig = go.Figure(data=[go.Pie(
             labels=risk_dist.index,
             values=risk_dist.values,
             hole=0.4,
-            marker_colors=['#2ecc71', '#f1c40f', '#e67e22', '#e74c3c']
+            marker_colors=['#10b981', '#fbbf24', '#f59e0b', '#ef4444']
         )])
         
         pie_fig.update_layout(
-            title="Risk Distribution",
-            height=300,
+            title="Distribution by Category",
+            height=280,
             showlegend=True,
             margin=dict(l=0, r=0, t=40, b=0)
         )
@@ -502,104 +461,79 @@ with tab1:
         st.plotly_chart(pie_fig, use_container_width=True)
 
 # ==========================================
-# TAB 2: PATTERN ANALYSIS
+# TAB 2: PATTERNS
 # ==========================================
 with tab2:
-    st.markdown("### 📈 Fraud Pattern Detection")
-    
     col_pattern1, col_pattern2 = st.columns(2)
     
     with col_pattern1:
-        st.markdown("#### 🔍 Ghost ID Indicator")
-        st.caption("Centers deviating from district baseline adult enrolment ratios")
+        st.subheader("Deviation Analysis")
         
-        # Enhanced scatter plot
         scatter_fig = px.scatter(
             filtered_df,
             x="total_activity",
             y="ratio_deviation",
             color="RISK_SCORE",
             size="RISK_SCORE",
-            hover_data=["pincode", "district", "state", "enrol_adult"],
+            hover_data=["pincode", "district", "state"],
             labels={
-                "ratio_deviation": "Deviation from District Norm",
-                "total_activity": "Daily Transaction Volume"
+                "ratio_deviation": "Deviation from District Baseline",
+                "total_activity": "Transaction Volume"
             },
-            color_continuous_scale="RdYlGn_r",
-            height=450
+            color_continuous_scale=["#10b981", "#fbbf24", "#f59e0b", "#ef4444"],
+            height=420
         )
         
-        # Add threshold lines
-        scatter_fig.add_hline(
-            y=0.2,
-            line_dash="dash",
-            line_color="red",
-            annotation_text="Critical Threshold (0.2)",
-            annotation_position="top right"
-        )
-        
-        scatter_fig.add_hline(
-            y=-0.2,
-            line_dash="dash",
-            line_color="orange",
-            annotation_text="Negative Anomaly (-0.2)",
-            annotation_position="bottom right"
-        )
-        
-        scatter_fig.update_layout(
-            plot_bgcolor='rgba(0,0,0,0)',
-            paper_bgcolor='rgba(0,0,0,0)',
-        )
+        scatter_fig.add_hline(y=0.2, line_dash="dash", line_color="#ef4444", 
+                             annotation_text="Critical Threshold", annotation_position="top right")
+        scatter_fig.add_hline(y=-0.2, line_dash="dash", line_color="#f59e0b",
+                             annotation_text="Negative Anomaly", annotation_position="bottom right")
         
+        scatter_fig.update_layout(plot_bgcolor='white', paper_bgcolor='white')
         st.plotly_chart(scatter_fig, use_container_width=True)
         
-        # Key insights
         high_deviation = len(filtered_df[filtered_df['ratio_deviation'] > 0.2])
-        st.info(f"🎯 **{high_deviation}** centers show critical deviation (>0.2) from district norms")
+        st.info(f"**{high_deviation}** centers exceed critical deviation threshold")
     
     with col_pattern2:
-        st.markdown("#### 📊 Risk Score Distribution")
-        st.caption("Histogram showing concentration of risk across centers")
+        st.subheader("Risk Distribution")
         
-        # Risk histogram
         hist_fig = px.histogram(
             filtered_df,
             x="RISK_SCORE",
             nbins=30,
             color="risk_category",
             color_discrete_map={
-                'Low': '#2ecc71',
-                'Medium': '#f1c40f',
-                'High': '#e67e22',
-                'Critical': '#e74c3c'
+                'Low': '#10b981',
+                'Medium': '#fbbf24',
+                'High': '#f59e0b',
+                'Critical': '#ef4444'
             },
-            height=450
+            height=420
         )
         
         hist_fig.update_layout(
             xaxis_title="Risk Score",
-            yaxis_title="Number of Centers",
+            yaxis_title="Frequency",
             showlegend=True,
-            plot_bgcolor='rgba(0,0,0,0)',
-            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='white',
+            paper_bgcolor='white'
         )
         
         st.plotly_chart(hist_fig, use_container_width=True)
         
-        # Statistical summary
-        st.markdown("**📈 Statistical Summary:**")
         st.markdown(f"""
-        - **Mean:** {filtered_df['RISK_SCORE'].mean():.2f}
-        - **Median:** {filtered_df['RISK_SCORE'].median():.2f}
-        - **Std Dev:** {filtered_df['RISK_SCORE'].std():.2f}
-        - **95th Percentile:** {filtered_df['RISK_SCORE'].quantile(0.95):.2f}
+        **Statistical Summary**
+        - Mean: {filtered_df['RISK_SCORE'].mean():.2f}
+        - Median: {filtered_df['RISK_SCORE'].median():.2f}
+        - Std Dev: {filtered_df['RISK_SCORE'].std():.2f}
+        - 95th %ile: {filtered_df['RISK_SCORE'].quantile(0.95):.2f}
         """)
     
-    st.divider()
-    
-    # Time series analysis (if date available)
+    # Time series
     if 'date' in filtered_df.columns and not filtered_df['date'].isna().all():
-        st.markdown("#### 📅 Temporal Pattern Analysis")
+        st.markdown("---")
+        st.subheader("Temporal Trends")
         
         daily_risk = filtered_df.groupby(filtered_df['date'].dt.date).agg({
             'RISK_SCORE': 'mean',
@@ -607,32 +541,24 @@ with tab2:
         }).reset_index()
         daily_risk.columns = ['date', 'avg_risk', 'case_count']
         
-        # Dual axis chart
         time_fig = go.Figure()
         
         time_fig.add_trace(go.Scatter(
-            x=daily_risk['date'],
-            y=daily_risk['avg_risk'],
-            name='Avg Risk Score',
-            line=dict(color='#e74c3c', width=3),
-            yaxis='y'
+            x=daily_risk['date'], y=daily_risk['avg_risk'],
+            name='Average Risk', line=dict(color='#ef4444', width=2), yaxis='y'
         ))
         
         time_fig.add_trace(go.Bar(
-            x=daily_risk['date'],
-            y=daily_risk['case_count'],
-            name='Case Count',
-            marker_color='#3498db',
-            opacity=0.3,
-            yaxis='y2'
+            x=daily_risk['date'], y=daily_risk['case_count'],
+            name='Case Volume', marker_color='#3b82f6', opacity=0.3, yaxis='y2'
         ))
         
         time_fig.update_layout(
             xaxis_title="Date",
-            yaxis=dict(title="Avg Risk Score", side='left'),
+            yaxis=dict(title="Average Risk Score", side='left'),
             yaxis2=dict(title="Case Count", overlaying='y', side='right'),
             hovermode='x unified',
-            height=400,
+            height=350,
             legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
         )
         
@@ -642,176 +568,136 @@ with tab2:
 # TAB 3: PRIORITY CASES
 # ==========================================
 with tab3:
-    st.markdown("### 📋 Priority Verification List")
-    
-    # Risk threshold slider
-    threshold = st.slider(
-        "Minimum Risk Score to Display",
-        min_value=0,
-        max_value=100,
-        value=75,
-        step=5,
-        help="Adjust threshold to filter cases"
-    )
+    st.subheader("Priority Investigation List")
+    
+    threshold = st.slider("Minimum Risk Score", 0, 100, 75, 5)
     
     high_risk_df = filtered_df[filtered_df['RISK_SCORE'] > threshold].sort_values('RISK_SCORE', ascending=False)
     
-    st.info(f"📊 Showing **{len(high_risk_df)}** cases above risk score {threshold}")
+    st.info(f"Displaying **{len(high_risk_df)}** cases with risk score above {threshold}")
     
-    # Add action status (simulated for demo)
-    high_risk_df['Action Status'] = np.random.choice(
-        ['🔴 Pending', '🟡 Under Investigation', '🟢 Resolved', '⚪ New'],
+    # Add status
+    high_risk_df['Status'] = np.random.choice(
+        ['Pending', 'Under Review', 'Verified', 'New'],
         size=len(high_risk_df),
         p=[0.5, 0.3, 0.1, 0.1]
     )
     
-    # Display enhanced table
     st.dataframe(
         high_risk_df[[
             'date', 'state', 'district', 'pincode', 
             'total_activity', 'enrol_adult', 'ratio_deviation',
-            'risk_category', 'RISK_SCORE', 'Action Status'
+            'RISK_SCORE', 'Status'
         ]],
         column_config={
             "date": st.column_config.DateColumn("Date", format="DD-MM-YYYY"),
             "RISK_SCORE": st.column_config.ProgressColumn(
-                "Risk Score",
-                help="AI-calculated fraud probability",
-                format="%d",
-                min_value=0,
-                max_value=100,
+                "Risk Score", format="%d", min_value=0, max_value=100
             ),
-            "total_activity": st.column_config.NumberColumn("Total Ops", format="%d"),
+            "total_activity": st.column_config.NumberColumn("Activity", format="%d"),
             "ratio_deviation": st.column_config.NumberColumn("Deviation", format="%.3f"),
-            "risk_category": st.column_config.TextColumn("Category"),
-            "Action Status": st.column_config.TextColumn("Status")
+            "Status": st.column_config.TextColumn("Status")
         },
         use_container_width=True,
         hide_index=True,
         height=400
     )
     
-    # Export options
+    # Export
     col_export1, col_export2, col_export3 = st.columns(3)
     
     with col_export1:
         csv = high_risk_df.to_csv(index=False).encode('utf-8')
         st.download_button(
-            label="📥 Download as CSV",
+            label="Download CSV",
             data=csv,
-            file_name=f'sentinel_priority_cases_{datetime.now().strftime("%Y%m%d")}.csv',
-            mime='text/csv',
+            file_name=f'sentinel_cases_{datetime.now().strftime("%Y%m%d")}.csv',
+            mime='text/csv'
         )
     
     with col_export2:
         json_data = high_risk_df.to_json(orient='records', date_format='iso')
         st.download_button(
-            label="📥 Download as JSON",
+            label="Download JSON",
             data=json_data,
-            file_name=f'sentinel_priority_cases_{datetime.now().strftime("%Y%m%d")}.json',
-            mime='application/json',
+            file_name=f'sentinel_cases_{datetime.now().strftime("%Y%m%d")}.json',
+            mime='application/json'
         )
     
     with col_export3:
-        # Generate investigation report
-        report = f"""
-SENTINEL FRAUD DETECTION REPORT
+        report = f"""SENTINEL FRAUD DETECTION REPORT
 Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
 ========================================
 
 SUMMARY:
-- Total High-Risk Cases: {len(high_risk_df)}
-- Critical Cases (>85): {len(high_risk_df[high_risk_df['RISK_SCORE'] > 85])}
-- Average Risk Score: {high_risk_df['RISK_SCORE'].mean():.2f}
-- Date Range: {high_risk_df['date'].min()} to {high_risk_df['date'].max()}
+Total High-Risk Cases: {len(high_risk_df)}
+Critical Cases (>85): {len(high_risk_df[high_risk_df['RISK_SCORE'] > 85])}
+Average Risk Score: {high_risk_df['RISK_SCORE'].mean():.2f}
 
-TOP 10 PRIORITY CASES:
+TOP 10 PRIORITY TARGETS:
 """
         for idx, row in high_risk_df.head(10).iterrows():
             report += f"\n{row['pincode']} - {row['district']}, {row['state']} | Risk: {row['RISK_SCORE']:.1f}"
         
         st.download_button(
-            label="📄 Download Report (TXT)",
+            label="Download Report",
             data=report,
-            file_name=f'sentinel_investigation_report_{datetime.now().strftime("%Y%m%d")}.txt',
-            mime='text/plain',
+            file_name=f'sentinel_report_{datetime.now().strftime("%Y%m%d")}.txt',
+            mime='text/plain'
         )
 
 # ==========================================
-# TAB 4: ADVANCED ANALYTICS
+# TAB 4: ANALYTICS
 # ==========================================
 with tab4:
-    st.markdown("### 📊 Advanced Statistical Analysis")
-    
     col_adv1, col_adv2 = st.columns(2)
     
     with col_adv1:
-        st.markdown("#### 🎯 Feature Importance")
-        st.caption("Impact of different features on fraud detection")
+        st.subheader("Feature Importance")
         
-        # Simulated feature importance (in production, use SHAP values)
-        features = ['Ratio Deviation', 'Weekend Activity', 'Mismatch Score', 'Total Activity']
+        features = ['Ratio Deviation', 'Weekend Activity', 'Mismatch Score', 'Volume']
         importance = [0.45, 0.25, 0.20, 0.10]
         
         importance_fig = go.Figure(go.Bar(
-            x=importance,
-            y=features,
-            orientation='h',
-            marker_color=['#e74c3c', '#e67e22', '#f1c40f', '#3498db']
+            x=importance, y=features, orientation='h',
+            marker_color=['#ef4444', '#f59e0b', '#fbbf24', '#3b82f6']
         ))
         
         importance_fig.update_layout(
-            xaxis_title="Importance Score",
-            yaxis_title="Feature",
-            height=350,
-            showlegend=False
+            xaxis_title="Importance", yaxis_title="", height=320, showlegend=False
         )
         
         st.plotly_chart(importance_fig, use_container_width=True)
         
-        st.info("💡 **Ratio Deviation** is the most predictive feature (45% importance)")
+        st.info("Ratio Deviation contributes 45% to fraud detection")
     
     with col_adv2:
-        st.markdown("#### 📈 Model Performance Metrics")
-        st.caption("Simulated performance indicators")
-        
-        # Simulated metrics
-        metrics_data = {
-            'Metric': ['Precision', 'Recall', 'F1-Score', 'Accuracy'],
-            'Score': [0.89, 0.85, 0.87, 0.88]
-        }
-        
-        metrics_df = pd.DataFrame(metrics_data)
+        st.subheader("Model Performance")
         
         metrics_fig = go.Figure(go.Indicator(
-            mode="gauge+number+delta",
+            mode="gauge+number",
             value=87,
             domain={'x': [0, 1], 'y': [0, 1]},
-            title={'text': "Overall Model Performance"},
-            delta={'reference': 80},
+            title={'text': "Overall Accuracy"},
             gauge={
                 'axis': {'range': [None, 100]},
-                'bar': {'color': "#3498db"},
+                'bar': {'color': "#3b82f6"},
                 'steps': [
-                    {'range': [0, 50], 'color': "#e74c3c"},
-                    {'range': [50, 75], 'color': "#f1c40f"},
-                    {'range': [75, 100], 'color': "#2ecc71"}
+                    {'range': [0, 50], 'color': "#fee2e2"},
+                    {'range': [50, 75], 'color': "#fef3c7"},
+                    {'range': [75, 100], 'color': "#d1fae5"}
                 ],
-                'threshold': {
-                    'line': {'color': "red", 'width': 4},
-                    'thickness': 0.75,
-                    'value': 90
-                }
+                'threshold': {'line': {'color': "#ef4444", 'width': 4}, 'thickness': 0.75, 'value': 90}
             }
         ))
         
-        metrics_fig.update_layout(height=350)
+        metrics_fig.update_layout(height=320)
         st.plotly_chart(metrics_fig, use_container_width=True)
     
-    st.divider()
+    st.markdown("---")
     
-    # Correlation heatmap
-    st.markdown("#### 🔥 Feature Correlation Matrix")
+    # Correlation
+    st.subheader("Feature Correlation Matrix")
     
     numeric_cols = ['RISK_SCORE', 'ratio_deviation', 'weekend_spike_score', 'mismatch_score', 'total_activity']
     available_cols = [col for col in numeric_cols if col in filtered_df.columns]
@@ -831,80 +717,72 @@ with tab4:
             colorbar=dict(title="Correlation")
         ))
         
-        heatmap_fig.update_layout(
-            height=400,
-            xaxis_title="Features",
-            yaxis_title="Features"
-        )
-        
+        heatmap_fig.update_layout(height=380)
         st.plotly_chart(heatmap_fig, use_container_width=True)
     
-    # Insights box
-    st.markdown("#### 💡 Key Insights")
+    # Insights
+    st.markdown("---")
+    st.subheader("Key Findings")
     
     insight_col1, insight_col2, insight_col3 = st.columns(3)
     
     with insight_col1:
         st.markdown("""
-            <div class='alert-warning'>
-                <strong>🔍 Pattern Detected</strong><br>
-                Weekend fraud attempts increased by 23% compared to weekdays
+            <div class='info-card info-card-warning'>
+                <strong>Pattern Detected</strong><br>
+                <span style='font-size: 13px; color: #64748b;'>Weekend fraud attempts increased 23% vs weekdays</span>
             </div>
         """, unsafe_allow_html=True)
     
     with insight_col2:
         st.markdown(f"""
-            <div class='alert-critical'>
-                <strong>⚠️ High Risk Alert</strong><br>
-                {insights['top_state']} shows highest concentration of anomalies
+            <div class='info-card info-card-danger'>
+                <strong>High Risk Alert</strong><br>
+                <span style='font-size: 13px; color: #64748b;'>{insights['top_state']} shows highest anomaly concentration</span>
             </div>
         """, unsafe_allow_html=True)
     
     with insight_col3:
         st.markdown(f"""
-            <div class='alert-safe'>
-                <strong>✅ System Health</strong><br>
-                Model confidence: 87% | Last updated: {datetime.now().strftime('%H:%M')}
+            <div class='info-card info-card-success'>
+                <strong>System Status</strong><br>
+                <span style='font-size: 13px; color: #64748b;'>Model confidence: 87% | Updated: {datetime.now().strftime('%H:%M')}</span>
             </div>
         """, unsafe_allow_html=True)
 
 # ==========================================
-# 9. FOOTER WITH SYSTEM INFO
+# FOOTER
 # ==========================================
-st.divider()
+st.markdown("---")
 
 footer_col1, footer_col2, footer_col3 = st.columns(3)
 
 with footer_col1:
-    st.markdown("""
-        **📊 System Statistics:**
-        - Active Filters: {}
-        - Data Points Analyzed: {:,}
-        - Processing Time: <1s
-    """.format(
-        len([f for f in [selected_state, selected_district, risk_filter] if f not in ['All', []]]),
-        len(filtered_df)
-    ))
+    st.markdown(f"""
+        **System Information**
+        - Filters Active: {len([f for f in [selected_state, selected_district, risk_filter] if f not in ['All', []]])}
+        - Records Analyzed: {len(filtered_df):,}
+        - Processing: <1 second
+    """)
 
 with footer_col2:
     st.markdown("""
-        **🎯 Quick Actions:**
-        - [Generate Full Report](#)
-        - [Schedule Investigation](#)
-        - [Alert Management](#)
+        **Quick Actions**
+        - Generate Report
+        - Schedule Investigation
+        - Alert Configuration
     """)
 
 with footer_col3:
     st.markdown("""
-        **ℹ️ About:**
+        **About**
         - Version: 1.0
-        - Model: Isolation Forest + District Normalization
-        - Team ID: UIDAI_4571
+        - Algorithm: Isolation Forest
+        - Team: UIDAI_4571
     """)
 
-st.markdown("---")
-st.markdown(
-    "<p style='text-align: center; color: #7f8c8d;'>Project Sentinel © 2026 | "
-    "Powered by AI & Context-Aware Analytics | Built for UIDAI Hackathon</p>",
-    unsafe_allow_html=True
-)
+st.markdown("""
+    <div style='text-align: center; color: #94a3b8; font-size: 13px; margin-top: 20px;'>
+        Project Sentinel © 2026 | Context-Aware Fraud Detection | UIDAI Hackathon
+    </div>
+""", unsafe_allow_html=True)