App is ready

app.py

@@ -2,10 +2,12 @@ import os
 import sys
 import tempfile
 import time
+import itertools
 import streamlit as st
 import pandas as pd
 import requests
 import openai
+from threading import Thread
 
 # Add 'src' to Python path
 sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
@@ -56,24 +58,40 @@ if submitted:
         spinner_box = st.empty()
         log_box = st.empty()
         logs = []
+        rotating = True
 
         def log(msg):
             logs.append(msg)
             log_box.code("\n".join(logs))
 
-        try:
-            spinner_box.markdown("⏳ Checking API keys...")
+        # === Rotating UI Messages ===
+        def rotating_messages():
+            messages = itertools.cycle([
+                "🔍 Searching financial news...",
+                "🧠 Running AI analysis...",
+                "📊 Evaluating sentiment...",
+                "📝 Generating report...",
+                "💹 Finalizing insights..."
+            ])
+            while rotating:
+                spinner_box.markdown(f"⏳ {next(messages)}")
+                time.sleep(1.5)
+
+        rotator_thread = Thread(target=rotating_messages)
+        rotator_thread.start()
 
-            # === Check OpenAI Key ===
+        try:
+            # Check API Keys
             try:
                 client = openai.OpenAI(api_key=openai_api_key)
                 client.models.list()
                 log("✅ OpenAI API key is valid.")
             except Exception as e:
                 log(f"❌ OpenAI API Key Error: {e}")
+                rotating = False
+                rotator_thread.join()
                 st.stop()
 
-            # === Check Tavily Key ===
             try:
                 response = requests.post(
                     "https://api.tavily.com/search",
@@ -84,50 +102,58 @@ if submitted:
                     log("✅ Tavily API key is valid.")
                 else:
                     log(f"❌ Tavily Key Error: {response.status_code} {response.text}")
+                    rotating = False
+                    rotator_thread.join()
                     st.stop()
             except Exception as e:
                 log(f"❌ Tavily API Key Error: {e}")
+                rotating = False
+                rotator_thread.join()
                 st.stop()
 
-            spinner_box.markdown("⏳ Running analysis pipeline...")
-            html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
+            with st.spinner("⏳ Running analysis..."):
+                html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
+
+            rotating = False
+            rotator_thread.join()
             spinner_box.success("✅ Analysis complete!")
 
             # === Report Tab ===
-
-
-            # === Articles Tab ===
             with tab_report:
+                st.subheader("📝 Latest Report")
                 if html_paths:
-                    # Add one button to download the latest report
                     latest_report = html_paths[-1]
                     with open(latest_report, 'r', encoding='utf-8') as f:
                         html_content = f.read()
 
+                    # Download button for HTML report
                     st.download_button(
-                        label=f"⬇️ Download {os.path.basename(latest_report)}",
+                        label="⬇️ Download Report (HTML)",
                         data=html_content,
                         file_name=os.path.basename(latest_report),
                         mime="text/html"
                     )
 
-                    # Display the report
                     st.components.v1.html(html_content, height=600, scrolling=True)
                 else:
                     st.error("❌ No reports were generated.")
-            # with tab_articles:
-            #     st.subheader("📋 Articles Table")
-            #     if not articles_df.empty:
-            #         st.dataframe(articles_df[["Title", "URL", "Summary", "Priority", "Date", "Sentiment", "Confidence", "Signal"]],
-            #                      use_container_width=True)
-            #         st.download_button(
-            #             label="⬇️ Download Articles CSV",
-            #             data=articles_df.to_csv(index=False).encode("utf-8"),
-            #             file_name="articles.csv",
-            #             mime="text/csv"
-            #         )
-            #     else:
-            #         st.info("No articles available.")
+
+            # === Articles Tab ===
+            with tab_articles:
+                st.subheader("📋 Articles Table")
+                if not articles_df.empty:
+                    st.dataframe(
+                        articles_df[["Title", "URL", "Priority", "Sentiment", "Confidence", "Signal", "Date"]],
+                        use_container_width=True
+                    )
+                    st.download_button(
+                        label="⬇️ Download Articles CSV",
+                        data=articles_df.to_csv(index=False).encode("utf-8"),
+                        file_name="articles.csv",
+                        mime="text/csv"
+                    )
+                else:
+                    st.info("No articles available.")
 
             # === Insights Tab ===
             with tab_insights:
@@ -144,6 +170,8 @@ if submitted:
                     st.info("No insights available.")
 
         except Exception as e:
+            rotating = False
+            rotator_thread.join()
             spinner_box.error("❌ Failed.")
             log_box.error(f"❌ Error: {e}")
 

src/main.py

@@ -45,8 +45,8 @@ def run_value_investing_analysis(csv_path, progress_callback=None):
     for _, row in current_df.iterrows():
         topic = row.get("topic")
         timespan = row.get("timespan_days", 7)
-        if progress_callback:
-            progress_callback(f"🔍 Processing topic: {topic} ({timespan} days)")
+        # if progress_callback:
+        #     progress_callback(f"🔍 Processing topic: {topic} ({timespan} days)")
         # try:
         #     news = fetch_deep_news(topic, timespan)
         #     if progress_callback: