Update streamlit_app.py

streamlit_app.py

@@ -39,7 +39,8 @@ def apply_retention_policy(df):
     # 1. Retention Filtering
     leg_df = df[df['type'] == 'Legislation']
     
-    news_types = ['News/Media', 'Federal/Exec Action']
+    # UPDATED: Added 'Legislative Office Press Release' to the 30-day retention bucket
+    news_types = ['News/Media', 'Federal/Exec Action', 'Legislative Office Press Release']
     news_mask = (df['type'].isin(news_types)) & ((df['event_date'] >= today - pd.Timedelta(days=30)) | df['event_date'].isna())
     news_df = df[news_mask]
     
@@ -88,7 +89,7 @@ start_background_scheduler()
 st.set_page_config(page_title="PolicyPilot Intel", layout="wide")
 st.title("AI Policy and News Dashboard - ALPHA Version")
 
-# Onboarding Text
+# RESTORED: Original Onboarding Text with new category
 st.markdown("""
 Welcome to the **AI Policy and News Dashboard**, an automated platform tracking technology policy developments, legislative movement, and media coverage.
 
@@ -105,11 +106,12 @@ To help you scan the chronological timeline quickly, entries are color-coded:
 * 🟢 **Federal / Exec Action** (Agencies, White House)
 * 🔵 **News / Media** (Press Coverage)
 * 🟠 **Schedule / Hearing** (Committee Meetings, Markups)
+* 📣 **Legislative Office Press Release** (Lawmaker Announcements)
 
 To generate a high-level summary of the most recent data entries, click the **"Generate Briefing"** button below.
 """)
 
-# Warning Notes
+# RESTORED: Warning Notes
 st.markdown("""
 ---
 ### Notes for Users
@@ -117,7 +119,7 @@ st.markdown("""
 2. **Work in Progress:** This is an alpha version. Improvements in coverage and AI logic will be made regularly.
 """)
 
-#  Expanded Architecture Notes
+# RESTORED: Expanded Architecture Notes
 with st.expander("🛠️ Technical Details & Architecture"):
     st.markdown("""
     * **AI Engine:** Summaries and keyword extraction are powered by **Meta-Llama-3.1-8B-Instruct**.
@@ -140,6 +142,7 @@ with st.sidebar:
     last_sync_str = "Pending First Run"
     if df is not None and not df.empty and 'date_collected' in df.columns:
         last_sync_dt = pd.to_datetime(df['date_collected']).max()
+        # UTC indicator maintained for accurate server time context
         last_sync_str = last_sync_dt.strftime('%b %d, %I:%M %p UTC')
     st.info(f"**Auto-Pilot:** Active (12h Cycle)\n\n**Last Sync:** {last_sync_str}")
     
@@ -160,7 +163,7 @@ with st.sidebar:
         with data_lock: csv_data = active_df.to_csv(index=False).encode('utf-8')
         st.download_button(label="Download Historical Archive (CSV)", data=csv_data, file_name=f"policy_pilot_archive_{pd.Timestamp.now().strftime('%Y-%m-%d')}.csv", mime="text/csv", use_container_width=True)
 
-# --- BRIEFING ---
+# --- EXECUTIVE BRIEFING ---
 if active_df is not None and not active_df.empty:
     st.subheader("Executive Intel Briefing")
     if 'exec_briefing' not in st.session_state:
@@ -173,7 +176,7 @@ if active_df is not None and not active_df.empty:
                 top_items = active_df.head(10)
                 context = "\n".join([f"- {row['title']} (Source: {row['source']})" for _, row in top_items.iterrows()])
                 
-              # AI Prompt forcing policy prioritization
+                # UPDATED: Objective Prompt with Legislation/Action Priority
                 messages = [
                     {"role": "system", "content": "You are an objective AI intelligence analyst. Summarize the following data into 3 concise, actionable bullets. Prioritize legislation and federal/executive actions over general news. Cite sources inline."},
                     {"role": "user", "content": f"Summarize these updates:\n\n{context}"}
@@ -192,13 +195,14 @@ def render_event_cards(display_df):
         st.info("No items match these filters.")
         return
     
-    # Visual Mapping for rapid scanning
+    # Visual Mapping for rapid scanning (Updated with Megaphone)
     type_icons = {
         "Legislation": "🟣",
         "Federal/Exec Action": "🟢",
         "News/Media": "🔵",
         "Schedule/Hearing": "🟠",
-        "Hearing/Markup": "🟠"
+        "Hearing/Markup": "🟠",
+        "Legislative Office Press Release": "📣"
     }
     
     color_map = {
@@ -206,7 +210,8 @@ def render_event_cards(display_df):
         "Federal/Exec Action": "green", 
         "Legislation": "violet", 
         "Schedule/Hearing": "orange", 
-        "Hearing/Markup": "orange"
+        "Hearing/Markup": "orange",
+        "Legislative Office Press Release": "red"
     }
     
     for _, row in display_df.iterrows():
@@ -221,7 +226,7 @@ def render_event_cards(display_df):
         raw_title = str(row['title'])
         display_title = raw_title[:75] + ("..." if len(raw_title) > 75 else "")
         
-        # UPDATED: Injected {source} directly into the UI header
+        # Injected {source} directly into the UI header
         with st.expander(f"{icon} {dt_str} | {card_type} | {source} | {display_title}"):
             col1, col2 = st.columns([3, 1])
             with col1: