Update app.py

app.py

@@ -80,23 +80,25 @@ st.markdown("""
             color: #0f172a !important;
         }
         
-        /* Link Button Fix - CRITICAL OVERRIDE */
+        /* Link Button Fix - CRITICAL OVERRIDE FOR DARK TEXT */
         [data-testid="stSidebar"] a {
-            color: #3b82f6 !important; /* Brighter blue */
+            background-color: #3b82f6 !important; /* Brighter blue background */
+            color: #ffffff !important; /* White text for contrast */
             font-weight: 700 !important;
             text-decoration: none;
-            background-color: #ffffff !important; /* Force white background for button look */
-            border: 1px solid #e2e8f0 !important;
+            border: 1px solid #2563eb !important;
             border-radius: 8px !important;
-            padding: 8px 16px !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;
         }
         [data-testid="stSidebar"] a:hover {
-            background-color: #f1f5f9 !important;
-            color: #2563eb !important;
-            border-color: #cbd5e1 !important;
+            background-color: #2563eb !important;
+            color: #ffffff !important;
+            border-color: #1d4ed8 !important;
+            transform: translateY(-1px);
         }
         
         /* Link Button Container Background */
@@ -157,6 +159,7 @@ def fetch_coordinates_batch(unique_locations):
             pass  # File corrupted, start fresh
 
     # 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),
@@ -251,6 +254,18 @@ def load_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',
+        'Orissa': 'Odisha',
+        'Chattisgarh': 'Chhattisgarh',
+        'Telengana': 'Telangana',
+        'Pondicherry': 'Puducherry'
+    }
+    df['state'] = df['state'].replace(state_mapping)
+
     # Get Unique Locations
     unique_locs = list(
         df[['district', 'state']].drop_duplicates().itertuples(index=False, name=None))