Update app.py

app.py

@@ -100,15 +100,40 @@ def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
     ratios_table = pd.DataFrame(ratios.items(), columns=["Metric", "Value"])
     financial_health = ratios_table[ratios_table["Metric"].isin(["Debt/Equity Ratio", "Return on Equity (%)", "Free Cash Flow ($)", "Beta (Volatility)"])]
 
+    # New Recommendation Section
     recommendation = "Hold"
     if ratios['P/E Ratio'] < 15 and ratios['Debt/Equity Ratio'] < 1.0 and ratios['Return on Equity (%)'] > 10 and ratios['Beta (Volatility)'] < 1.2:
         recommendation = "Buy"
     elif ratios['P/E Ratio'] > 30 or ratios['Debt/Equity Ratio'] > 2.0 or ratios['Return on Equity (%)'] < 5:
         recommendation = "Sell"
 
-    summary = f"Recommendation: {recommendation} based on financial health and valuation ratios."
+    # Detailed report for summarization
+    report = (
+        f"Company Overview:\n"
+        f"Name: {info['Name']}\n"
+        f"Industry: {info['Industry']}\n"
+        f"Sector: {info['Sector']}\n"
+        f"Market Cap: ${info['Market Cap']:,.2f}\n\n"
+        f"Financial Metrics:\n"
+        f"P/E Ratio: {ratios['P/E Ratio']}\n"
+        f"P/S Ratio: {ratios['P/S Ratio']}\n"
+        f"P/B Ratio: {ratios['P/B Ratio']}\n"
+        f"PEG Ratio: {ratios['PEG Ratio']}\n"
+        f"Dividend Yield: {ratios['Dividend Yield']}%\n"
+        f"Debt/Equity Ratio: {ratios['Debt/Equity Ratio']}\n"
+        f"Return on Equity: {ratios['Return on Equity (%)']}%\n"
+        f"Free Cash Flow: ${ratios['Free Cash Flow ($)']:,.2f}\n"
+        f"Beta (Volatility): {ratios['Beta (Volatility)']}\n"
+    )
+
+    # Summarize via Mistral
+    summary_prompt = f\"Summarize this financial report clearly and briefly:\n\n{report}\"
+    ai_summary = query_mistral(summary_prompt)
+
+    # Add recommendation inside financial health
+    financial_health = financial_health.append({\"Metric\": \"Recommendation\", \"Value\": recommendation}, ignore_index=True)
 
-    return summary, info_table, ratios_table, financial_health, sector_comp, fig
+    return ai_summary, info_table, ratios_table, financial_health, sector_comp, fig
 
 # Theme Selection
 selected_theme = os.getenv("APP_THEME", "light")