debugging for analysis tables

app.py

@@ -1,11 +1,11 @@
 import os
 import sys
 import tempfile
+import time
 import streamlit as st
 import pandas as pd
 import requests
 import openai
-import pdfkit
 
 # Add 'src' to Python path
 sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
@@ -34,12 +34,13 @@ with st.form("topics_form"):
     submitted = st.form_submit_button("Run Analysis")
 
 # === Tabs Setup ===
-tab_report, tab_articles, tab_insights, tab_debug = st.tabs(["📝 Report", "📋 Articles", "📊 Insights", "🛠 Debug"])
+tab_report, tab_articles, tab_insights = st.tabs(["📝 Report", "📋 Articles", "📊 Insights"])
 
 if submitted:
     if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]):
         st.warning("Please fill in all fields.")
     else:
+        # Reset old results
         articles_df = pd.DataFrame()
         insights_df = pd.DataFrame()
         html_paths = []
@@ -52,6 +53,7 @@ if submitted:
             df.to_csv(tmp_csv.name, index=False)
             csv_path = tmp_csv.name
 
+        spinner_box = st.empty()
         log_box = st.empty()
         logs = []
 
@@ -60,8 +62,9 @@ if submitted:
             log_box.code("\n".join(logs))
 
         try:
-            # === Check API Keys ===
-            st.info("**Checking Credentials...**")
+            spinner_box.markdown("⏳ Checking API keys...")
+
+            # === Check OpenAI Key ===
             try:
                 client = openai.OpenAI(api_key=openai_api_key)
                 client.models.list()
@@ -70,6 +73,7 @@ if submitted:
                 log(f"❌ OpenAI API Key Error: {e}")
                 st.stop()
 
+            # === Check Tavily Key ===
             try:
                 response = requests.post(
                     "https://api.tavily.com/search",
@@ -85,56 +89,49 @@ if submitted:
                 log(f"❌ Tavily API Key Error: {e}")
                 st.stop()
 
-            # === Run Analysis with Spinner ===
-            with st.spinner("⏳ **Analyzing topics, please wait...**"):
-                html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
-
-            st.success("✅ Analysis complete!")
+            spinner_box.markdown("⏳ Running analysis pipeline...")
+            html_paths, articles_df, insights_df = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
+            spinner_box.success("✅ Analysis complete!")
 
             # === Report Tab ===
+
+
+            # === Articles Tab ===
             with tab_report:
                 if html_paths:
-                    for path in html_paths:
-                        with open(path, 'r', encoding='utf-8') as f:
-                            html_content = f.read()
-                            st.components.v1.html(html_content, height=600, scrolling=True)
-
-                            # Generate PDF from HTML
-                            pdf_path = path.replace(".html", ".pdf")
-                            try:
-                                pdfkit.from_string(html_content, pdf_path)
-                                with open(pdf_path, "rb") as pdf_file:
-                                    st.download_button(
-                                        label="⬇️ Download Report (PDF)",
-                                        data=pdf_file,
-                                        file_name=os.path.basename(pdf_path),
-                                        mime="application/pdf"
-                                    )
-                            except Exception as e:
-                                st.error(f"PDF generation failed: {e}")
-                else:
-                    st.error("❌ No reports were generated.")
+                    # 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()
 
-            # === Articles Tab ===
-            with tab_articles:
-                st.subheader("📋 Articles Table")
-                if not articles_df.empty:
-                    st.dataframe(
-                        articles_df[["Title", "URL", "Summary", "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"
+                        label=f"⬇️ Download {os.path.basename(latest_report)}",
+                        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.info("No articles available.")
+                    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.")
 
             # === Insights Tab ===
             with tab_insights:
-                st.subheader("📊 Top Investment Insights")
+                st.subheader("📊 Investment Insights")
                 if not insights_df.empty:
                     st.dataframe(insights_df, use_container_width=True)
                     st.download_button(
@@ -146,13 +143,8 @@ if submitted:
                 else:
                     st.info("No insights available.")
 
-            # === Debug Tab ===
-            with tab_debug:
-                st.subheader("🛠 Debug Log")
-                st.code("\n".join(logs) if logs else "No logs yet.")
-
         except Exception as e:
-            st.error("❌ Failed.")
+            spinner_box.error("❌ Failed.")
             log_box.error(f"❌ Error: {e}")
 
 

src/main.py

@@ -26,6 +26,16 @@ def derive_priority(sentiment, confidence):
         return "Medium"
     return "Low"
 
+# === Metrics Box ===
+def build_metrics_box(topic, num_articles):
+    now = datetime.now().strftime("%Y-%m-%d %H:%M")
+    return f"""
+> **Topic:** `{topic}`
+> **Articles Collected:** `{num_articles}`
+> **Generated:** `{now}`
+---
+"""
+
 # === Main Analysis ===
 def run_value_investing_analysis(csv_path, progress_callback=None):
     current_df = pd.read_csv(csv_path)
@@ -99,10 +109,12 @@ def run_value_investing_analysis(csv_path, progress_callback=None):
 
         try:
             report_body = generate_value_investor_report(topic, news)
+            metrics_md = build_metrics_box(topic, len(news))
+            full_md = metrics_md + report_body
             filename = f"{topic.replace(' ', '_').lower()}_{datetime.now().strftime('%Y-%m-%d')}.md"
             filepath = os.path.join(DATA_DIR, filename)
             with open(filepath, "w", encoding="utf-8") as f:
-                f.write(report_body)
+                f.write(full_md)
         except Exception as e:
             if progress_callback:
                 progress_callback(f"[REPORT ERROR] {e}")

src/news_analysis.py

@@ -298,7 +298,7 @@ Your goal is to uncover:
 
 Write a markdown memo with:
 1. **Key Value Signals**
-2. **Stocks or Startups to Watch**
+2. **Stocks or Startups to Watch** -- Add on each of those the Price-to-Earnings Ratio · Price-to-Book Ratio · Debt-to-Equity Ratio · Free Cash Flow · PEG Ratio ·  fore each, this is very important
 3. **What Smart Money Might Be Acting On**
 4. **References**
 5. **Investment Hypothesis**