Update app.py

app.py

@@ -8,7 +8,7 @@ from datetime import datetime
 # 1. PAGE CONFIGURATION
 st.set_page_config(
     page_title="S.T.A.R.K AI | UIDAI Fraud Detection",
-    page_icon="",
+    page_icon="🛡️",
     layout="wide",
     initial_sidebar_state="expanded"
 )
@@ -163,54 +163,54 @@ def load_data():
         'West Bengal': (22.9868, 87.8550)
     }
 
-def get_coords(row):
-    state = row.get('state', 'Delhi')
-    district = str(row.get('district', 'Unknown'))
-    
-    # 1. Get State Base Coordinates (Use your updated list)
-    base_lat, base_lon = state_centers.get(state, (20.5937, 78.9629))
-    
-    # 2. DEFINE STATE RADIUS SCALER (In Degrees)
-    # Default is 0.5 (~55km) which is safer than 1.5
-    default_radius = 0.5 
-    
-    # Tighter constraints for small States/UTs
-    radius_map = {
-        'Chandigarh': 0.04,
-        'Delhi': 0.15,
-        'Goa': 0.15,
-        'Puducherry': 0.1,
-        'Lakshadweep': 0.05,
-        'Daman and Diu': 0.05,
-        'Dadra and Nagar Haveli': 0.05,
-        'Kerala': 0.3,      # Narrow state
-        'Haryana': 0.4,
-        'Punjab': 0.4,
-        'Tripura': 0.3,
-        'Sikkim': 0.15,
-        'Andaman and Nicobar Islands': 1.0 # Long archipelago
-    }
-    
-    # Get the specific radius for this state
-    radius = radius_map.get(state, default_radius)
-    
-    # 3. DETERMINISTIC HASHING
-    district_hash = hash(state + district)
-    np.random.seed(district_hash % 2**32) 
-    
-    # Offset using the specific radius
-    dist_lat_offset = np.random.uniform(-radius, radius)
-    dist_lon_offset = np.random.uniform(-radius, radius)
-    
-    # 4. MICRO JITTER (Random noise for individual points)
-    np.random.seed(None) 
-    noise_lat = np.random.normal(0, 0.02 * radius) # Scale noise relative to state size
-    noise_lon = np.random.normal(0, 0.02 * radius)
-    
-    return pd.Series({
-        'lat': base_lat + dist_lat_offset + noise_lat,
-        'lon': base_lon + dist_lon_offset + noise_lon
-    })
+    def get_coords(row):
+        state = row.get('state', 'Delhi')
+        district = str(row.get('district', 'Unknown'))
+        
+        # 1. Get State Base Coordinates (Use your updated list)
+        base_lat, base_lon = state_centers.get(state, (20.5937, 78.9629))
+        
+        # 2. DEFINE STATE RADIUS SCALER (In Degrees)
+        # Default is 0.5 (~55km) which is safer than 1.5
+        default_radius = 0.5 
+        
+        # Tighter constraints for small States/UTs
+        radius_map = {
+            'Chandigarh': 0.04,
+            'Delhi': 0.15,
+            'Goa': 0.15,
+            'Puducherry': 0.1,
+            'Lakshadweep': 0.05,
+            'Daman and Diu': 0.05,
+            'Dadra and Nagar Haveli': 0.05,
+            'Kerala': 0.3,      # Narrow state
+            'Haryana': 0.4,
+            'Punjab': 0.4,
+            'Tripura': 0.3,
+            'Sikkim': 0.15,
+            'Andaman and Nicobar Islands': 1.0 # Long archipelago
+        }
+        
+        # Get the specific radius for this state
+        radius = radius_map.get(state, default_radius)
+        
+        # 3. DETERMINISTIC HASHING
+        district_hash = hash(state + district)
+        np.random.seed(district_hash % 2**32) 
+        
+        # Offset using the specific radius
+        dist_lat_offset = np.random.uniform(-radius, radius)
+        dist_lon_offset = np.random.uniform(-radius, radius)
+        
+        # 4. MICRO JITTER (Random noise for individual points)
+        np.random.seed(None) 
+        noise_lat = np.random.normal(0, 0.02 * radius) # Scale noise relative to state size
+        noise_lon = np.random.normal(0, 0.02 * radius)
+        
+        return pd.Series({
+            'lat': base_lat + dist_lat_offset + noise_lat,
+            'lon': base_lon + dist_lon_offset + noise_lon
+        })
     
     # Apply coordinates
     coords = df.apply(get_coords, axis=1)
@@ -379,7 +379,7 @@ with tab_list:
     st.download_button(
         "Download CSV",
         data=csv,
-        file_name="uidai_S.T.A.R.K AI_priority_list.csv",
+        file_name="uidai_stark_ai_priority_list.csv",
         mime="text/csv",
         type="primary"
     )