debugging for analysis tables

app.py

@@ -34,7 +34,7 @@ with st.form("topics_form"):
     submitted = st.form_submit_button("Run Analysis")
 
 # === Tabs Setup ===
-tab_report, tab_articles, tab_insights = st.tabs(["📝 Report", "📋 Articles", "📊 Insights"])
+tab_report, tab_articles, tab_insights, tab_debug = st.tabs(["📝 Report", "📋 Articles", "📊 Insights", "🛠 Debug"])
 
 if submitted:
     if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]):
@@ -137,10 +137,16 @@ if submitted:
                     )
                 else:
                     st.info("No insights available.")
+            with tab_debug:
+                st.subheader("🛠 Debug Log")
+                st.code("\n".join(logs) if logs else "No logs yet.")
 
         except Exception as e:
             spinner_box.error("❌ Failed.")
             log_box.error(f"❌ Error: {e}")
+                        # === Debug Tab ===
+
+
 
 # import os
 # import sys

src/main.py

@@ -38,7 +38,15 @@ def run_value_investing_analysis(csv_path, progress_callback=None):
         if progress_callback:
             progress_callback(f"🔍 Processing topic: {topic} ({timespan} days)")
 
-        news = fetch_deep_news(topic, timespan)
+        try:
+            news = fetch_deep_news(topic, timespan)
+            if progress_callback:
+                progress_callback(f"[DEBUG] fetch_deep_news returned {len(news) if news else 0} articles.")
+        except Exception as e:
+            if progress_callback:
+                progress_callback(f"[ERROR] fetch_deep_news failed: {e}")
+            continue
+
         if not news:
             if progress_callback:
                 progress_callback(f"⚠️ No news found for topic: {topic}")
@@ -50,7 +58,6 @@ def run_value_investing_analysis(csv_path, progress_callback=None):
             url = article.get("url", "")
             date = article.get("date", datetime.now().strftime("%Y-%m-%d"))
 
-            # === Sentiment Analysis ===
             try:
                 result = analyze_article(summary)
                 sentiment = result.get("sentiment", "Neutral")
@@ -75,13 +82,12 @@ def run_value_investing_analysis(csv_path, progress_callback=None):
             })
 
             company_data.append({
-                "Company": topic,  # Use topic as placeholder for company detection
+                "Company": topic,
                 "Sentiment": sentiment,
                 "Confidence": confidence,
                 "Summary": summary,
             })
 
-        # Save markdown report
         try:
             report_body = generate_value_investor_report(topic, news)
             filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}.md"
@@ -113,16 +119,13 @@ def build_company_insights(company_data):
             "Confidence": avg_confidence,
             "Highlights": highlights
         })
-
-    insights_df = pd.DataFrame(insights)
-    return insights_df.sort_values(by="Confidence", ascending=False).head(5)
+    return pd.DataFrame(insights).sort_values(by="Confidence", ascending=False).head(5)
 
 # === Pipeline ===
 def run_pipeline(csv_path, tavily_api_key, progress_callback=None):
     os.environ["TAVILY_API_KEY"] = tavily_api_key
     all_articles, company_data = run_value_investing_analysis(csv_path, progress_callback)
 
-    # Convert markdown to HTML
     html_paths = []
     for md_file in os.listdir(DATA_DIR):
         if md_file.endswith(".md"):
@@ -133,7 +136,6 @@ def run_pipeline(csv_path, tavily_api_key, progress_callback=None):
     insights_df = build_company_insights(company_data)
     return html_paths, articles_df, insights_df
 
-
 # import os
 # import pandas as pd
 # from datetime import datetime