Update app.py

app.py

@@ -20,8 +20,12 @@ TICKERS = [
 
 def optimize_portfolio(years, target_return):
     try:
-        data = yf.download(TICKERS, period=f"{years}y", interval="1mo")['Adj Close']
-        returns = data.pct_change().dropna()
+        data = yf.download(TICKERS, period=f"{years}y", interval="1mo")
+        if "Adj Close" not in data:
+            return pd.DataFrame(), "Error: 'Adj Close' column missing.", "", ""
+
+        prices = data['Adj Close']
+        returns = prices.pct_change().dropna()
         mean_returns = returns.mean() * 12
         cov_matrix = returns.cov() * 12
 
@@ -47,7 +51,7 @@ def optimize_portfolio(years, target_return):
         )
 
         if not result.success:
-            return "Optimization failed. Try adjusting inputs.", None, None, None
+            return pd.DataFrame(), "Optimization failed. Try adjusting inputs.", "", ""
 
         weights = result.x
         port_return = weights @ mean_returns
@@ -60,14 +64,14 @@ def optimize_portfolio(years, target_return):
             "Weight (%)": np.round(weights * 100, 2)
         }).sort_values("Weight (%)", ascending=False).reset_index(drop=True)
 
-        return df, f"{port_return*100:.2f}%", f"{port_vol*100:.2f}%", f"{sharpe_ratio:.2f}"
+        return df, "", f"{port_return*100:.2f}%", f"{port_vol*100:.2f}%", f"{sharpe_ratio:.2f}"
 
     except Exception as e:
-        return f"Error: {str(e)}", None, None, None
+        return pd.DataFrame(), f"Error: {str(e)}", "", "", ""
 
 with gr.Blocks() as demo:
     gr.Markdown("# 📈 Modern Portfolio Optimizer (MPT)")
-    gr.Markdown("Select number of years of historical data and your target annual return. This app computes the **minimum risk portfolio** of 25 S&P 500 stocks.")
+    gr.Markdown("Optimize a portfolio of 25 S&P 500 stocks for **minimum risk** and a **5% annual return target**.")
 
     with gr.Row():
         years_slider = gr.Slider(1, 10, value=5, step=1, label="Years of Historical Data")
@@ -76,14 +80,15 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Optimize Portfolio")
 
     output_table = gr.Dataframe(headers=["Ticker", "Weight (%)"], label="Optimal Allocation")
-    ret_text = gr.Text(label="Expected Return")
-    vol_text = gr.Text(label="Expected Volatility")
-    sharpe_text = gr.Text(label="Sharpe Ratio")
+    error_box = gr.Textbox(label="Message", lines=1)
+    ret_text = gr.Textbox(label="Expected Return")
+    vol_text = gr.Textbox(label="Expected Volatility")
+    sharpe_text = gr.Textbox(label="Sharpe Ratio")
 
     run_button.click(
         fn=lambda years, target: optimize_portfolio(years, target / 100),
         inputs=[years_slider, return_slider],
-        outputs=[output_table, ret_text, vol_text, sharpe_text]
+        outputs=[output_table, error_box, ret_text, vol_text, sharpe_text]
     )
 
 demo.launch()