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Update streamlit_app.py
Browse files- streamlit_app.py +6 -6
streamlit_app.py
CHANGED
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@@ -88,7 +88,7 @@ start_background_scheduler()
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st.set_page_config(page_title="PolicyPilot Intel", layout="wide")
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st.title("AI Policy and News Dashboard - ALPHA Version")
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#
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st.markdown("""
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Welcome to the **AI Policy and News Dashboard**, an automated platform tracking technology policy developments, legislative movement, and media coverage.
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@@ -109,7 +109,7 @@ To help you scan the chronological timeline quickly, entries are color-coded:
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To generate a high-level summary of the most recent data entries, click the **"Generate Briefing"** button below.
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""")
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#
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st.markdown("""
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---
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### Notes for Users
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@@ -117,7 +117,7 @@ st.markdown("""
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2. **Work in Progress:** This is an alpha version. Improvements in coverage and AI logic will be made regularly.
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""")
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#
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with st.expander("🛠️ Technical Details & Architecture"):
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st.markdown("""
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* **AI Engine:** Summaries and keyword extraction are powered by **Meta-Llama-3.1-8B-Instruct**.
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@@ -160,7 +160,7 @@ with st.sidebar:
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with data_lock: csv_data = active_df.to_csv(index=False).encode('utf-8')
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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)
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# ---
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if active_df is not None and not active_df.empty:
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st.subheader("Executive Intel Briefing")
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if 'exec_briefing' not in st.session_state:
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@@ -173,9 +173,9 @@ if active_df is not None and not active_df.empty:
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top_items = active_df.head(10)
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context = "\n".join([f"- {row['title']} (Source: {row['source']})" for _, row in top_items.iterrows()])
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messages = [
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{"role": "system", "content": "Summarize the following data into 3 concise,
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{"role": "user", "content": f"Summarize these updates:\n\n{context}"}
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]
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try:
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st.set_page_config(page_title="PolicyPilot Intel", layout="wide")
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st.title("AI Policy and News Dashboard - ALPHA Version")
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# Onboarding Text
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st.markdown("""
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Welcome to the **AI Policy and News Dashboard**, an automated platform tracking technology policy developments, legislative movement, and media coverage.
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To generate a high-level summary of the most recent data entries, click the **"Generate Briefing"** button below.
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""")
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# Warning Notes
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st.markdown("""
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---
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### Notes for Users
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2. **Work in Progress:** This is an alpha version. Improvements in coverage and AI logic will be made regularly.
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""")
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# Expanded Architecture Notes
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with st.expander("🛠️ Technical Details & Architecture"):
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st.markdown("""
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* **AI Engine:** Summaries and keyword extraction are powered by **Meta-Llama-3.1-8B-Instruct**.
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with data_lock: csv_data = active_df.to_csv(index=False).encode('utf-8')
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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)
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# --- BRIEFING ---
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if active_df is not None and not active_df.empty:
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st.subheader("Executive Intel Briefing")
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if 'exec_briefing' not in st.session_state:
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top_items = active_df.head(10)
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context = "\n".join([f"- {row['title']} (Source: {row['source']})" for _, row in top_items.iterrows()])
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# AI Prompt forcing policy prioritization
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messages = [
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{"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."},
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{"role": "user", "content": f"Summarize these updates:\n\n{context}"}
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]
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try:
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