Update app.py

app.py

@@ -4,7 +4,6 @@ import numpy as np
 import pandas as pd
 from scipy.optimize import minimize
 
-# Define stock tickers (25 from S&P 500)
 TICKERS = [
     'AAPL', 'MSFT', 'NVDA', 'AVGO', 'ADBE',
     'AMZN', 'TSLA', 'HD',
@@ -21,8 +20,8 @@ TICKERS = [
 def optimize_portfolio(years, target_return):
     try:
         data = yf.download(TICKERS, period=f"{years}y", interval="1mo")
-        if "Adj Close" not in data:
-            return pd.DataFrame(), "Error: 'Adj Close' column missing.", "", ""
+        if "Adj Close" not in data.columns:
+            return pd.DataFrame(), "Error: 'Adj Close' column missing.", "", "", ""
 
         prices = data['Adj Close']
         returns = prices.pct_change().dropna()
@@ -51,7 +50,7 @@ def optimize_portfolio(years, target_return):
         )
 
         if not result.success:
-            return pd.DataFrame(), "Optimization failed. Try adjusting inputs.", "", ""
+            return pd.DataFrame(), "Optimization failed. Try adjusting inputs.", "", "", ""
 
         weights = result.x
         port_return = weights @ mean_returns
@@ -71,7 +70,7 @@ def optimize_portfolio(years, target_return):
 
 with gr.Blocks() as demo:
     gr.Markdown("# 📈 Modern Portfolio Optimizer (MPT)")
-    gr.Markdown("Optimize a portfolio of 25 S&P 500 stocks for **minimum risk** and a **5% annual return target**.")
+    gr.Markdown("Optimize a portfolio of 25 S&P 500 stocks for **minimum risk** with a target return.")
 
     with gr.Row():
         years_slider = gr.Slider(1, 10, value=5, step=1, label="Years of Historical Data")