Update streamlit_app.py

streamlit_app.py

@@ -10,11 +10,10 @@ if Path("/data").exists():
 else:
     CSV_PATH = Path(__file__).resolve().parent / "policy_tracker.csv"
 
-# --- DATA LOADING (Timezone Safe) ---
+# --- DATA LOADING ---
 def load_data():
     if CSV_PATH.exists():
         df = pd.read_csv(CSV_PATH, dtype=str)
-        # Convert and forcefully strip all timezones to prevent crashes
         df['event_date'] = pd.to_datetime(df['event_date'], errors='coerce')
         if df['event_date'].dt.tz is not None:
             df['event_date'] = df['event_date'].dt.tz_localize(None)
@@ -24,15 +23,15 @@ def load_data():
     return None
 
 # --- UI SETUP ---
-st.set_page_config(page_title="PolicyPilot Intelligence", layout="wide", page_icon="🏛️")
-st.title("🏛️ PolicyPilot Intelligence Dashboard")
+st.set_page_config(page_title="PolicyPilot Intelligence", layout="wide")
+st.title("PolicyPilot Intelligence Dashboard")
 
 df = load_data()
 
 # --- SIDEBAR ---
 with st.sidebar:
     st.header("Controls")
-    if st.button("🚀 Run Live Sweep", use_container_width=True):
+    if st.button("Run Live Sweep", use_container_width=True):
         with st.spinner("Syncing Gov Schedules, API, Agencies, and Media..."):
             importlib.reload(main)
             count = main.run()
@@ -43,7 +42,7 @@ with st.sidebar:
     st.header("Triage Filters")
     
     if df is not None and not df.empty:
-        show_high_only = st.checkbox("🚨 Show HIGH PRIORITY Only", value=False)
+        show_high_only = st.checkbox("Show HIGH PRIORITY Only", value=False)
         available_types = df['type'].dropna().unique().tolist()
         selected_types = st.multiselect(
             "Filter by Source Type:", 
@@ -53,14 +52,14 @@ with st.sidebar:
 
 # --- EXECUTIVE BRIEFING ---
 if df is not None and not df.empty:
-    st.subheader("📝 Latest Intel Summary")
+    st.subheader("Latest Intel Summary")
     
     if 'executive_summary' not in st.session_state:
         st.session_state.executive_summary = "Click the button below to generate a high-level briefing of recent updates."
 
     st.info(st.session_state.executive_summary)
 
-    if st.button("✨ Generate Executive Briefing"):
+    if st.button("Generate Executive Briefing"):
         with st.spinner("AI is analyzing the latest updates..."):
             top_items = df.head(10)
             context_text = "\n".join([f"- {row['title']} (Source: {row['source']})" for _, row in top_items.iterrows()])
@@ -87,21 +86,18 @@ def render_event_cards(display_df, is_radar=False):
         return
         
     for _, row in display_df.iterrows():
-        # Setup the date strings
         event_dt = row['event_date']
         event_str = event_dt.strftime('%b %d, %Y') if pd.notnull(event_dt) else "Date Unknown"
         
-        # Countdown logic for the Radar tab
         countdown_badge = ""
         if is_radar and pd.notnull(event_dt):
-            # Timezone naive now()
             days_until = (event_dt.date() - pd.Timestamp.now().tz_localize(None).date()).days
             if days_until == 0:
-                countdown_badge = " [🚨 TODAY]"
+                countdown_badge = " [TODAY]"
             elif days_until > 0:
-                countdown_badge = f" [⏳ In {days_until} days]"
+                countdown_badge = f" [In {days_until} days]"
 
-        flag = row.get('triage_flag', 'ℹ️ LOW - MONITOR')
+        flag = row.get('triage_flag', 'LOW - MONITOR')
         
         with st.expander(f"{flag}{countdown_badge} | {event_str} | {row['type']}: {row['source']} | {row['title'][:65]}..."):
             col1, col2 = st.columns([3, 1])
@@ -117,18 +113,16 @@ def render_event_cards(display_df, is_radar=False):
                 st.link_button("Review Original Source", str(row['link']), use_container_width=True)
 
 if df is not None and not df.empty:
-    # 1. Apply Filters
     filtered_df = df.copy()
     if selected_types:
         filtered_df = filtered_df[filtered_df['type'].isin(selected_types)]
     if show_high_only:
         filtered_df = filtered_df[filtered_df['triage_flag'].str.contains("HIGH", na=False)]
         
-    search = st.text_input("🔍 Search matches...", "")
+    search = st.text_input("Search matches...", "")
     if search:
         filtered_df = filtered_df[filtered_df.apply(lambda r: r.astype(str).str.contains(search, case=False).any(), axis=1)]
 
-    # 2. Timezone-naive date splitting
     today = pd.Timestamp.now().tz_localize(None).normalize()
     
     is_future = filtered_df['event_date'] >= today
@@ -137,8 +131,7 @@ if df is not None and not df.empty:
     radar_df = filtered_df[is_future].sort_values(by="event_date", ascending=True)
     archive_df = filtered_df[is_past].sort_values(by="event_date", ascending=False)
 
-    # 3. Render Tabs
-    tab1, tab2 = st.tabs([f"📡 Radar (Upcoming: {len(radar_df)})", f"🗄️ Archive (Past: {len(archive_df)})"])
+    tab1, tab2 = st.tabs([f"Radar (Upcoming: {len(radar_df)})", f"Archive (Past: {len(archive_df)})"])
     
     with tab1:
         st.subheader("Upcoming Events & Scheduled Action")