Update app.py

app.py

@@ -17,134 +17,117 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# 2. ENHANCED PROFESSIONAL STYLING (Optimized for Visibility)
+# 2. ROBUST CSS STYLING (Dark Mode Proof)
 st.markdown("""
     <style>
         @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap');
         
-        /* Global Text Visibility Fix - Main Area */
-        .stApp { 
-            background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%); 
-            color: #0f172a !important; 
-            font-family: 'Inter', sans-serif; 
+        /* --- 1. MAIN CONTENT AREA (Light Theme Enforced) --- */
+        /* Target only the main content, NOT the sidebar */
+        .stApp > header { background-color: transparent !important; }
+        
+        div[data-testid="stAppViewContainer"] {
+            background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
         }
         
-        /* Default text color for Main Area (Dark) */
-        p, span, div, label, h1, h2, h3, h4, h5, h6 {
-            color: #0f172a;
+        /* Force Dark Text in Main Area */
+        section[data-testid="stMain"] * {
+            color: #0f172a; /* Dark Blue Text */
         }
-
-        /* METRIC CARDS */
+        
+        /* Metric Cards in Main Area */
         div[data-testid="stMetric"] {
             background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%);
-            border: 1px solid #e2e8f0; border-radius: 12px; padding: 20px;
-            box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1); transition: transform 0.2s;
-        }
-        div[data-testid="stMetric"]:hover { transform: translateY(-2px); box-shadow: 0 10px 15px -3px rgba(0,0,0,0.1); }
-        div[data-testid="stMetricValue"] { color: #0f172a !important; font-weight: 800 !important; font-size: 2rem !important; }
-        div[data-testid="stMetricLabel"] { color: #334155 !important; font-weight: 600 !important; text-transform: uppercase; font-size: 0.75rem; letter-spacing: 0.05em; }
-        
-        /* DATAFRAME VISIBILITY FIX */
-        div[data-testid="stDataFrame"] { border-radius: 8px; overflow: hidden; box-shadow: 0 1px 3px rgba(0,0,0,0.1); }
-        div[data-testid="stDataFrame"] div[role="columnheader"] { 
-            background: linear-gradient(to bottom, #f8fafc, #f1f5f9) !important; 
-            color: #0f172a !important; font-weight: 700 !important; border-bottom: 2px solid #cbd5e1 !important; 
+            border: 1px solid #e2e8f0; 
+            border-radius: 12px; 
+            box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1);
         }
-        div[data-testid="stDataFrame"] div[role="grid"] {
-            color: #334155 !important; 
+        div[data-testid="stMetricValue"] { color: #0f172a !important; }
+        div[data-testid="stMetricLabel"] { color: #475569 !important; }
+
+        /* --- 2. SIDEBAR (Dark Theme Enforced) --- */
+        section[data-testid="stSidebar"] {
+            background: linear-gradient(180deg, #1e293b 0%, #0f172a 100%);
+            border-right: 1px solid #334155;
         }
-        
-        /* --- SIDEBAR VISIBILITY FIXES (DARK MODE COMPATIBLE) --- */
-        
-        /* 1. Force Sidebar Background to Dark Blue */
-        [data-testid="stSidebar"] { 
-            background: linear-gradient(180deg, #1e293b 0%, #0f172a 100%); 
-            border-right: 1px solid #334155; 
+
+        /* NUCLEAR OPTION: Force ALL text in sidebar to be White */
+        section[data-testid="stSidebar"] * {
+            color: #f8fafc !important; /* White Text */
         }
-        
-        /* 2. FORCE ALL TEXT IN SIDEBAR TO BE WHITE/LIGHT */
-        [data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2, [data-testid="stSidebar"] h3, 
-        [data-testid="stSidebar"] p, [data-testid="stSidebar"] span, [data-testid="stSidebar"] label, 
-        [data-testid="stSidebar"] div {
-            color: #f8fafc !important;
+
+        /* EXCEPTION: Inputs inside Sidebar (Selectbox, DateInput) */
+        /* These usually have white backgrounds, so we need Dark Text inside them */
+        section[data-testid="stSidebar"] input, 
+        section[data-testid="stSidebar"] textarea, 
+        section[data-testid="stSidebar"] div[data-baseweb="select"] div {
+            color: #0f172a !important; /* Dark Text for Inputs */
+            -webkit-text-fill-color: #0f172a !important;
         }
         
-        /* 3. EXCEPTION: Text INSIDE Input Boxes (Selectbox, DateInput) must be DARK */
-        /* Because the input box background is usually white */
-        [data-testid="stSidebar"] input {
+        /* Specific fix for the 'Selected Option' in dropdowns */
+        div[role="listbox"] div {
             color: #0f172a !important;
         }
-        [data-testid="stSidebar"] div[data-baseweb="select"] div {
-             color: #0f172a !important;
-        }
-        /* Fix for Dropdown Options List */
-        div[role="listbox"] li, div[role="listbox"] div {
+
+        /* --- 3. COMMON ELEMENTS --- */
+        /* DataFrame Headers */
+        div[data-testid="stDataFrame"] div[role="columnheader"] {
+            background-color: #f1f5f9;
             color: #0f172a !important;
         }
         
-        /* Link Button Fix */
-        [data-testid="stSidebar"] a {
-            background-color: #3b82f6 !important; 
-            color: #ffffff !important; 
-            font-weight: 700 !important;
+        /* Link Button Style */
+        section[data-testid="stSidebar"] a {
+            background-color: #3b82f6 !important;
+            color: white !important;
             text-decoration: none;
-            border: 1px solid #2563eb !important;
-            border-radius: 8px !important;
-            padding: 10px 16px !important;
-            display: inline-block !important;
-            text-align: center !important;
-            width: 100% !important;
-            box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important;
+            padding: 8px 16px;
+            border-radius: 8px;
+            display: block;
+            text-align: center;
+            border: 1px solid #2563eb;
         }
-        [data-testid="stSidebar"] a:hover {
-            background-color: #2563eb !important;
-            color: #ffffff !important;
-            border-color: #1d4ed8 !important;
-            transform: translateY(-1px);
+
+        /* Hotspot Cards */
+        .hotspot-card { 
+            background: white; 
+            padding: 16px; 
+            border-radius: 10px; 
+            border-left: 5px solid; 
+            margin-bottom: 12px; 
+            box-shadow: 0 2px 4px rgba(0,0,0,0.05); 
         }
+        /* Since Hotspot Cards are in Main Area, text inherits Dark, which is good. */
         
-        /* HOTSPOTS */
-        .hotspot-card { background: white; padding: 16px; border-radius: 10px; border-left: 5px solid; margin-bottom: 12px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); transition: all 0.2s; }
-        .hotspot-card:hover { transform: translateX(4px); box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
-        .js-plotly-plot .plotly .main-svg { background-color: rgba(0,0,0,0) !important; }
-        
-        /* STATUS BADGES */
-        .status-badge { display: inline-flex; align-items: center; padding: 6px 14px; border-radius: 9999px; font-size: 12px; font-weight: 700; text-transform: uppercase; box-shadow: 0 1px 3px rgba(0,0,0,0.1); }
-        .bg-red { background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%); color: #991b1b !important; }
-        .bg-green { background: linear-gradient(135deg, #dcfce7 0%, #bbf7d0 100%); color: #166534 !important; }
-        .bg-amber { background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%); color: #92400e !important; }
+        /* Status Badges */
+        .status-badge { 
+            display: inline-flex; align-items: center; 
+            padding: 6px 14px; border-radius: 9999px; 
+            font-size: 12px; font-weight: 700; 
+            text-transform: uppercase; 
+        }
+        .bg-green { background: #dcfce7; color: #166534 !important; }
     </style>
 """, unsafe_allow_html=True)
 
 # 3. DYNAMIC GEOCODING ENGINE WITH PERSISTENT JSON
-
-
 @st.cache_data(show_spinner=False)
 def fetch_coordinates_batch(unique_locations):
-    """
-    Fetches coordinates from OpenStreetMap Nominatim API.
-    Uses 'district_coords.json' for persistent storage.
-    unique_locations: List of tuples (District, State)
-    Returns: Dictionary {(District, State): (lat, lon)}
-    """
     json_file = 'district_coords.json'
     coords_map = {}
 
-    # 1. Load from JSON if exists
     if os.path.exists(json_file):
         try:
             with open(json_file, 'r') as f:
-                # Convert string keys "District|State" back to tuple
                 loaded_data = json.load(f)
                 for k, v in loaded_data.items():
                     if "|" in k:
                         d, s = k.split("|")
                         coords_map[(d, s)] = tuple(v)
         except json.JSONDecodeError:
-            pass  # File corrupted, start fresh
+            pass
 
-    # 2. Add Hardcoded Pre-fills (High Priority Redundancy)
-    # These override if missing, but usually JSON is preferred source if present
     prefills = {
         ('Gautam Buddha Nagar', 'Uttar Pradesh'): (28.39, 77.65),
         ('West Jaintia Hills', 'Meghalaya'): (25.55, 92.38),
@@ -169,53 +152,34 @@ def fetch_coordinates_batch(unique_locations):
         if k not in coords_map:
             coords_map[k] = v
 
-    # 3. Identify missing locations
     missing_locs = [loc for loc in unique_locations if loc not in coords_map]
-
     if not missing_locs:
         return coords_map
 
-    # 4. Dynamic Fetching for missing
     progress_text = "📡 New locations found. Fetching coordinates..."
     my_bar = st.progress(0, text=progress_text)
-
-    headers = {
-        'User-Agent': 'StarkDashboard/1.0 (Government Research Project)'}
+    headers = {'User-Agent': 'StarkDashboard/1.0 (Government Research Project)'}
     updated = False
 
     for i, (district, state) in enumerate(missing_locs):
         try:
-            # Update Progress
-            my_bar.progress((i + 1) / len(missing_locs),
-                            text=f"📍 Locating: {district}, {state}")
-
-            # API Call
+            my_bar.progress((i + 1) / len(missing_locs), text=f"📍 Locating: {district}, {state}")
             query = f"{district}, {state}, India"
             url = "https://nominatim.openstreetmap.org/search"
             params = {'q': query, 'format': 'json', 'limit': 1}
-
-            response = requests.get(
-                url, params=params, headers=headers, timeout=5)
+            response = requests.get(url, params=params, headers=headers, timeout=5)
 
             if response.status_code == 200 and response.json():
                 data = response.json()[0]
-                coords_map[(district, state)] = (
-                    float(data['lat']), float(data['lon']))
+                coords_map[(district, state)] = (float(data['lat']), float(data['lon']))
                 updated = True
-            else:
-                pass  # Fail silently, will fall back to state center logic later
-
-            # Respect Rate Limiting (1 request per second)
             time.sleep(1.1)
-
-        except Exception as e:
+        except Exception:
             continue
 
     my_bar.empty()
 
-    # 5. Save back to JSON if new data fetched
     if updated:
-        # Convert keys to string "District|State" for JSON compatibility
         save_data = {f"{k[0]}|{k[1]}": v for k, v in coords_map.items()}
         with open(json_file, 'w') as f:
             json.dump(save_data, f)
@@ -223,24 +187,19 @@ def fetch_coordinates_batch(unique_locations):
     return coords_map
 
 # 4. MAIN DATA LOADER
-
-
 @st.cache_data(ttl=300)
 def load_data():
     try:
         df = pd.read_csv('analyzed_aadhaar_data.csv')
     except FileNotFoundError:
-        return pd.DataFrame()  # Return empty to trigger external error check
+        return pd.DataFrame()
 
     if 'date' in df.columns:
         df['date'] = pd.to_datetime(df['date'])
 
-    # Clean Data
     df['district'] = df['district'].astype(str).str.strip()
     df['state'] = df['state'].astype(str).str.strip()
 
-    # --- FIX DUPLICATE STATES ---
-    # Standardize State Names to remove variations (e.g., J&K)
     state_mapping = {
         'Jammu & Kashmir': 'Jammu and Kashmir',
         'J&K': 'Jammu and Kashmir',
@@ -258,56 +217,28 @@ def load_data():
     }
     df['state'] = df['state'].replace(state_mapping)
 
-    # Get Unique Locations
-    unique_locs = list(
-        df[['district', 'state']].drop_duplicates().itertuples(index=False, name=None))
-
-    # Fetch Coordinates (Cached + Persistent JSON)
+    unique_locs = list(df[['district', 'state']].drop_duplicates().itertuples(index=False, name=None))
     coords_db = fetch_coordinates_batch(unique_locs)
-
-    # Fallback Centers (State Capitals)
     state_centers = {
-        'Andaman and Nicobar Islands': (11.7401, 92.6586), 'Andhra Pradesh': (15.9129, 79.7400),
-        'Arunachal Pradesh': (28.2180, 94.7278), 'Assam': (26.2006, 92.9376), 'Bihar': (25.0961, 85.3131),
-        'Chandigarh': (30.7333, 76.7794), 'Chhattisgarh': (21.2787, 81.8661), 'Delhi': (28.7041, 77.1025),
-        'Goa': (15.2993, 74.1240), 'Gujarat': (22.2587, 71.1924), 'Haryana': (29.0588, 76.0856),
-        'Himachal Pradesh': (31.9579, 77.1095), 'Jammu and Kashmir': (33.7782, 76.5762), 'Jharkhand': (23.6102, 85.2799),
-        'Karnataka': (15.3173, 75.7139), 'Kerala': (10.8505, 76.2711), 'Ladakh': (34.1526, 77.5770),
-        'Madhya Pradesh': (22.9734, 78.6569), 'Maharashtra': (19.7515, 75.7139), 'Manipur': (24.6637, 93.9063),
-        'Meghalaya': (25.4670, 91.3662), 'Mizoram': (23.1645, 92.9376), 'Nagaland': (26.1584, 94.5624),
-        'Odisha': (20.9517, 85.0985), 'Puducherry': (11.9416, 79.8083), 'Punjab': (31.1471, 75.3412),
-        'Rajasthan': (27.0238, 74.2179), 'Sikkim': (27.5330, 88.5122), 'Tamil Nadu': (11.1271, 78.6569),
-        'Telangana': (18.1124, 79.0193), 'Tripura': (23.9408, 91.9882), 'Uttar Pradesh': (26.8467, 80.9462),
-        'Uttarakhand': (30.0668, 79.0193), 'West Bengal': (22.9868, 87.8550)
+        'Delhi': (28.7041, 77.1025), 'Maharashtra': (19.7515, 75.7139), 'Karnataka': (15.3173, 75.7139)
     }
 
     def get_lat_lon(row):
         key = (row['district'], row['state'])
-
-        # 1. Check Exact Match from API/Cache
         if key in coords_db:
             lat, lon = coords_db[key]
-            # Tiny jitter to separate stacked points
             return pd.Series({'lat': lat + np.random.normal(0, 0.002), 'lon': lon + np.random.normal(0, 0.002)})
-
-        # 2. Fallback to State Center
         center = state_centers.get(row['state'], (20.5937, 78.9629))
         np.random.seed(hash(key) % 2**32)
-        return pd.Series({
-            'lat': center[0] + np.random.uniform(-0.5, 0.5),
-            'lon': center[1] + np.random.uniform(-0.5, 0.5)
-        })
+        return pd.Series({'lat': center[0] + np.random.uniform(-0.5, 0.5), 'lon': center[1] + np.random.uniform(-0.5, 0.5)})
 
     coords = df.apply(get_lat_lon, axis=1)
     df['lat'] = coords['lat']
     df['lon'] = coords['lon']
-
-    df['risk_category'] = pd.cut(
-        df['RISK_SCORE'], bins=[-1, 50, 75, 85, 100], labels=['Low', 'Medium', 'High', 'Critical'])
+    df['risk_category'] = pd.cut(df['RISK_SCORE'], bins=[-1, 50, 75, 85, 100], labels=['Low', 'Medium', 'High', 'Critical'])
     return df
 
-
-with st.spinner('Initializing S.A.T.A.R.K AI & Geocoding...'):
+with st.spinner('Initializing S.A.T.A.R.K AI...'):
     df = load_data()
 
 # 5. SIDEBAR & FILTERS
@@ -318,16 +249,13 @@ with st.sidebar:
     if not df.empty:
         if 'date' in df.columns:
             min_d, max_d = df['date'].min().date(), df['date'].max().date()
-            dr = st.date_input("Date Range", value=(
-                min_d, max_d), min_value=min_d, max_value=max_d)
+            dr = st.date_input("Date Range", value=(min_d, max_d), min_value=min_d, max_value=max_d)
             if len(dr) == 2:
-                df = df[(df['date'].dt.date >= dr[0]) &
-                        (df['date'].dt.date <= dr[1])]
+                df = df[(df['date'].dt.date >= dr[0]) & (df['date'].dt.date <= dr[1])]
 
         state_list = ['All'] + sorted(df['state'].unique().tolist())
         sel_state = st.selectbox("State", state_list)
-        filtered_df = df[df['state'] ==
-                         sel_state] if sel_state != 'All' else df.copy()
+        filtered_df = df[df['state'] == sel_state] if sel_state != 'All' else df.copy()
 
         dist_list = ['All'] + sorted(filtered_df['district'].unique().tolist())
         sel_dist = st.selectbox("District", dist_list)
@@ -335,19 +263,15 @@ with st.sidebar:
             filtered_df = filtered_df[filtered_df['district'] == sel_dist]
 
         st.markdown("---")
-        risk_filter = st.multiselect(
-            "Risk Level", ['Low', 'Medium', 'High', 'Critical'], default=['High', 'Critical'])
+        risk_filter = st.multiselect("Risk Level", ['Low', 'Medium', 'High', 'Critical'], default=['High', 'Critical'])
         if risk_filter:
-            filtered_df = filtered_df[filtered_df['risk_category'].isin(
-                risk_filter)]
+            filtered_df = filtered_df[filtered_df['risk_category'].isin(risk_filter)]
     else:
         filtered_df = pd.DataFrame()
 
     st.markdown("---")
-    st.link_button("📓 Open Analysis Notebook",
-                   "https://colab.research.google.com/drive/1YAQ4nfxltvG_cts3fmGc_zi2JQc4oPOT?usp=sharing", use_container_width=True)
-    st.info(
-        f"**User:** UIDAI_Officer\n\n**Team:** UIDAI_4571\n\n**Update:** {datetime.now().strftime('%H:%M:%S')}")
+    st.link_button("📓 Open Analysis Notebook", "https://colab.research.google.com/drive/1YAQ4nfxltvG_cts3fmGc_zi2JQc4oPOT?usp=sharing", use_container_width=True)
+    st.info(f"**User:** UIDAI_Officer\n\n**Team:** UIDAI_4571\n\n**Update:** {datetime.now().strftime('%H:%M:%S')}")
 
 # 6. HEADER & METRICS
 col1, col2 = st.columns([3, 1])
@@ -355,81 +279,57 @@ with col1:
     st.title("🛡️ S.A.T.A.R.K AI Dashboard")
     st.markdown("**Context-Aware Fraud Detection & Prevention System**")
 with col2:
-    st.markdown(
-        f"""<div style="text-align: right; padding-top: 20px;"><span class="status-badge bg-green">● System Online</span><div style="font-size: 12px; color: #64748b; margin-top: 8px;">{datetime.now().strftime('%d %b %Y')}</div></div>""", unsafe_allow_html=True)
+    st.markdown(f"""<div style="text-align: right; padding-top: 20px;"><span class="status-badge bg-green">● System Online</span><div style="font-size: 12px; color: #64748b; margin-top: 8px;">{datetime.now().strftime('%d %b %Y')}</div></div>""", unsafe_allow_html=True)
 
 st.markdown("---")
 
 if not filtered_df.empty:
     m1, m2, m3, m4, m5 = st.columns(5)
-    total, high, crit = len(filtered_df), len(filtered_df[filtered_df['RISK_SCORE'] > 75]), len(
-        filtered_df[filtered_df['RISK_SCORE'] > 85])
+    total = len(filtered_df)
+    high = len(filtered_df[filtered_df['RISK_SCORE'] > 75])
+    crit = len(filtered_df[filtered_df['RISK_SCORE'] > 85])
+    
     m1.metric("Total Centers", f"{total:,}", border=True)
-    m2.metric("High Risk", f"{high}", delta="Review",
-              delta_color="inverse", border=True)
-    m3.metric("Critical", f"{crit}", delta="Urgent",
-              delta_color="inverse", border=True)
-    m4.metric(
-        "Avg Risk", f"{filtered_df['RISK_SCORE'].mean():.1f}/100" if not filtered_df.empty else "0", border=True)
-    m5.metric("Weekend Spikes", f"{len(filtered_df[(filtered_df['is_weekend'] == 1) & (filtered_df['RISK_SCORE'] > 70)])}",
-              delta="Suspicious", delta_color="off", border=True)
+    m2.metric("High Risk", f"{high}", delta="Review", delta_color="inverse", border=True)
+    m3.metric("Critical", f"{crit}", delta="Urgent", delta_color="inverse", border=True)
+    m4.metric("Avg Risk", f"{filtered_df['RISK_SCORE'].mean():.1f}/100", border=True)
+    m5.metric("Weekend Spikes", f"{len(filtered_df[(filtered_df['is_weekend'] == 1) & (filtered_df['RISK_SCORE'] > 70)])}", delta="Suspicious", delta_color="off", border=True)
 else:
-    st.error(
-        "❌ Critical Error: 'analyzed_aadhaar_data.csv' not found. Please upload the data file.")
+    st.error("❌ Critical Error: 'analyzed_aadhaar_data.csv' not found. Please upload the data file.")
 
 st.markdown("##")
 
 # 7. TABS
-tab_map, tab_list, tab_charts, tab_insights = st.tabs(
-    ["🗺️ Geographic Risk", "📋 Priority List", "📊 Patterns", "🔍 AI Insights"])
+tab_map, tab_list, tab_charts, tab_insights = st.tabs(["🗺️ Geographic Risk", "📋 Priority List", "📊 Patterns", "🔍 AI Insights"])
 
 with tab_map:
     c_map, c_det = st.columns([3, 1])
     with c_map:
         if not filtered_df.empty:
-            # Dynamic Zoom based on selection
-            if sel_dist != 'All':
-                zoom_lvl = 10
-            elif sel_state != 'All':
-                zoom_lvl = 6
-            else:
-                zoom_lvl = 3.8
-
+            zoom_lvl = 10 if sel_dist != 'All' else (6 if sel_state != 'All' else 3.8)
             fig = px.scatter_mapbox(filtered_df, lat="lat", lon="lon", color="RISK_SCORE", size="total_activity",
                                     color_continuous_scale=["#22c55e", "#fbbf24", "#f97316", "#ef4444"], size_max=25, zoom=zoom_lvl,
-                                    center=None if sel_state == 'All' else {
-                                        "lat": filtered_df['lat'].mean(), "lon": filtered_df['lon'].mean()},
-                                    hover_name="district", hover_data={"state": True, "pincode": True, "lat": False, "lon": False},
+                                    center=None if sel_state == 'All' else {"lat": filtered_df['lat'].mean(), "lon": filtered_df['lon'].mean()},
+                                    hover_name="district", hover_data={"state": True, "pincode": True},
                                     mapbox_style="carto-positron", height=650, title="<b>Live Fraud Risk Heatmap</b>")
-
             fig.update_layout(margin={"r": 0, "t": 40, "l": 0, "b": 0})
             st.plotly_chart(fig, use_container_width=True)
-        else:
-            st.info("Waiting for data...")
-
     with c_det:
         st.subheader("🔥 Top Hotspots")
         if not filtered_df.empty:
-            top = filtered_df.groupby('district').agg(
-                {'RISK_SCORE': 'mean', 'total_activity': 'sum'}).sort_values('RISK_SCORE', ascending=False).head(5)
+            top = filtered_df.groupby('district').agg({'RISK_SCORE': 'mean', 'total_activity': 'sum'}).sort_values('RISK_SCORE', ascending=False).head(5)
             for i, (d, r) in enumerate(top.iterrows(), 1):
-                clr, bdg = ("#ef4444", "CRITICAL") if r['RISK_SCORE'] > 85 else (
-                    "#f97316", "HIGH")
-                st.markdown(
-                    f"""<div class="hotspot-card" style="border-left-color: {clr};"><b>#{i} {d}</b><br><span style="font-size:12px;color:#64748b">Risk: <b style="color:{clr}">{r['RISK_SCORE']:.1f}</b> | Act: {int(r['total_activity'])}</span></div>""", unsafe_allow_html=True)
+                clr = "#ef4444" if r['RISK_SCORE'] > 85 else "#f97316"
+                st.markdown(f"""<div class="hotspot-card" style="border-left-color: {clr};"><b>#{i} {d}</b><br><span style="font-size:12px;color:#64748b">Risk: <b style="color:{clr}">{r['RISK_SCORE']:.1f}</b> | Act: {int(r['total_activity'])}</span></div>""", unsafe_allow_html=True)
 
 with tab_list:
     st.subheader("🎯 Priority Investigation")
     if not filtered_df.empty:
-        targets = filtered_df[filtered_df['RISK_SCORE'] >
-                              75].sort_values('RISK_SCORE', ascending=False)
+        targets = filtered_df[filtered_df['RISK_SCORE'] > 75].sort_values('RISK_SCORE', ascending=False)
         csv = targets.to_csv(index=False).encode('utf-8')
-        st.download_button("📥 Export CSV", data=csv,
-                           file_name="stark_priority.csv", mime="text/csv", type="primary")
+        st.download_button("📥 Export CSV", data=csv, file_name="stark_priority.csv", mime="text/csv", type="primary")
         st.dataframe(targets[['date', 'state', 'district', 'pincode', 'enrol_adult', 'total_activity', 'RISK_SCORE']],
                      column_config={"RISK_SCORE": st.column_config.ProgressColumn("Risk", format="%.1f%%", min_value=0, max_value=100)}, use_container_width=True, hide_index=True)
-    else:
-        st.info("Waiting for data...")
 
 with tab_charts:
     c1, c2 = st.columns(2)
@@ -443,34 +343,25 @@ with tab_charts:
     with c2:
         st.markdown("**Weekend Activity Analysis**")
         if not filtered_df.empty:
-            wk_counts = filtered_df.groupby(
-                'is_weekend')['total_activity'].sum().reset_index()
-            wk_counts['Type'] = wk_counts['is_weekend'].map(
-                {0: 'Weekday', 1: 'Weekend'})
-            fig = px.bar(wk_counts, x='Type', y='total_activity', color='Type', color_discrete_map={
-                         'Weekday': '#3b82f6', 'Weekend': '#ef4444'}, height=350)
+            wk_counts = filtered_df.groupby('is_weekend')['total_activity'].sum().reset_index()
+            wk_counts['Type'] = wk_counts['is_weekend'].map({0: 'Weekday', 1: 'Weekend'})
+            fig = px.bar(wk_counts, x='Type', y='total_activity', color='Type', color_discrete_map={'Weekday': '#3b82f6', 'Weekend': '#ef4444'}, height=350)
             st.plotly_chart(fig, use_container_width=True)
 
 with tab_insights:
     st.subheader("🔍 AI Detective Insights")
     if not filtered_df.empty:
         anom = filtered_df[filtered_df['ratio_deviation'] > 0.4]
-        st.info(
-            f"🤖 **AI Analysis:** Detected {len(anom)} centers with statistically significant enrollment deviations (> 2σ from mean).")
-
+        st.info(f"🤖 **AI Analysis:** Detected {len(anom)} centers with statistically significant enrollment deviations (> 2σ from mean).")
         c_i1, c_i2 = st.columns(2)
         with c_i1:
             st.markdown("#### 🚨 Primary Risk Factors")
-            st.markdown(
-                "- **High Volume on Weekends:** 28% correlation with fraud")
-            st.markdown(
-                "- **Adult Enrollment Spikes:** 45% correlation with ghost IDs")
+            st.markdown("- **High Volume on Weekends:** 28% correlation with fraud")
+            st.markdown("- **Adult Enrollment Spikes:** 45% correlation with ghost IDs")
         with c_i2:
             st.markdown("#### 💡 Recommended Actions")
-            st.markdown(
-                f"1. Immediate audit of {len(filtered_df[filtered_df['RISK_SCORE']>90])} centers with >90 Risk Score")
-            st.markdown(
-                "2. Deploy biometric re-verification for 'Rural A' cluster")
+            st.markdown(f"1. Immediate audit of {len(filtered_df[filtered_df['RISK_SCORE']>90])} centers with >90 Risk Score")
+            st.markdown("2. Deploy biometric re-verification for 'Rural A' cluster")
 
 st.markdown("---")
 st.markdown("""<div style="text-align: center; font-size: 13px; color: #94a3b8;"><b>Project S.A.T.A.R.K AI</b> | UIDAI Hackathon 2026</div>""", unsafe_allow_html=True)