Update app.py

app.py

@@ -27,6 +27,9 @@ from GoogleNews import GoogleNews
 from bs4 import BeautifulSoup
 import requests
 from urllib.parse import urlparse, urlunparse
+from stock_vector_db import StockVectorDB
+from datetime import datetime
+
 st.set_page_config(layout="wide")
 
 GOOGLE_API_KEY=os.environ['GOOGLE_API_KEY']
@@ -453,6 +456,26 @@ if activities == "Symbol Analysis":
             st.text(res["output_text"])
 elif activities=="News Sentiment":
 
+            # Initialize DB
+    db = StockVectorDB(index_path="vector_index", log_path="vector_log.json")
+    
+    log_df = pd.DataFrame(db.log_data)
+    if log_df.empty:
+        st.write("No log")
+    else:
+        log_df["date"] = pd.to_datetime(log_df["date"])
+        
+        with st.sidebar.expander("🕒 History Filters"):
+            selected_date = st.date_input("Pick a date", value=datetime.now().date())
+            filtered = log_df[log_df["date"] == pd.to_datetime(selected_date)]
+        
+            st.write(f"Found {len(filtered)} entries on {selected_date}")
+            for _, row in filtered.iterrows():
+                st.markdown(f"**{row['company']} ({row['ticker']})** — {row['sentiment']} → {row['action']}")
+                st.caption(f"Reason: {row['reason']}")
+
+
+        
 
     if st.button("Get Live Updates..."):
         url1 = f"https://economictimes.indiatimes.com/markets/stocks/news"
@@ -533,6 +556,9 @@ elif activities=="News Sentiment":
         parsed_data = json.loads(raw_text)
         top_picks = parsed_data.get("top_picks", [])
         
+        # Save LLM output to FAISS DB
+        today = datetime.now()
+        db.store_top_picks(top_picks, today)
         
         # Layout
         for stock in top_picks: