Update app.py

app.py

@@ -31,7 +31,7 @@ st.markdown("""
         }
         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: #64748b !important; font-weight: 600 !important; text-transform: uppercase; font-size: 0.75rem; letter-spacing: 0.05em; }
+        div[data-testid="stMetricLabel"] { color: #334155 !important; font-weight: 600 !important; text-transform: uppercase; font-size: 0.75rem; letter-spacing: 0.05em; }
         
         /* DATAFRAME */
         div[data-testid="stDataFrame"] { border-radius: 8px; overflow: hidden; box-shadow: 0 1px 3px rgba(0,0,0,0.1); }
@@ -39,6 +39,9 @@ st.markdown("""
             background: linear-gradient(to bottom, #f8fafc, #f1f5f9) !important; 
             color: #0f172a !important; font-weight: 700 !important; border-bottom: 2px solid #cbd5e1 !important; 
         }
+        div[data-testid="stDataFrame"] div[role="grid"] {
+            color: #334155 !important; 
+        }
         
         /* SIDEBAR */
         [data-testid="stSidebar"] { background: linear-gradient(180deg, #1e293b 0%, #0f172a 100%); border-right: 1px solid #334155; }
@@ -47,6 +50,7 @@ st.markdown("""
         
         /* UI ELEMENTS */
         h1 { background: linear-gradient(135deg, #0f172a 0%, #334155 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-weight: 800 !important; }
+        h2, h3 { color: #0f172a !important; }
         .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; }
         .bg-green { background: linear-gradient(135deg, #dcfce7 0%, #bbf7d0 100%); color: #166534; }
@@ -144,8 +148,7 @@ def load_data():
     try:
         df = pd.read_csv('analyzed_aadhaar_data.csv')
     except FileNotFoundError:
-        st.error("❌ Critical Error: 'analyzed_aadhaar_data.csv' not found. Please upload the file.")
-        return pd.DataFrame()
+        return pd.DataFrame() # Return empty to trigger external error check
 
     if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
     
@@ -250,7 +253,7 @@ if not filtered_df.empty:
     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)
 else:
-    st.warning("No data available to calculate metrics.")
+    st.error("❌ Critical Error: 'analyzed_aadhaar_data.csv' not found. Please upload the data file.")
 
 st.markdown("##")
 
@@ -274,7 +277,7 @@ with tab_map:
             
             fig.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
             st.plotly_chart(fig, use_container_width=True)
-        else: st.warning("No data found to map.")
+        else: st.info("Waiting for data...")
     
     with c_det:
         st.subheader("🔥 Top Hotspots")
@@ -292,7 +295,7 @@ with tab_list:
         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.warning("No data available.")
+    else: st.info("Waiting for data...")
 
 with tab_charts:
     c1, c2 = st.columns(2)