Update app.py

app.py

@@ -53,9 +53,11 @@ def fetch_data(tickers, start_date, end_date):
         raise ValueError(f"Failed to fetch data: {e}")
 
 def optimize_portfolio(tickers, start_date, end_date, initial_balance):
+    # Fetch real-time data
     data = fetch_data(tickers, start_date, end_date)
     returns = data.pct_change().dropna()
 
+    # Define the environment
     env = PortfolioEnv(returns, initial_balance=initial_balance)
     model = PPO("MlpPolicy", env, verbose=0)
     model.learn(total_timesteps=5000)
@@ -70,6 +72,7 @@ def optimize_portfolio(tickers, start_date, end_date, initial_balance):
         portfolio_weights = action / (np.sum(action) + 1e-8)
         portfolio_values.append(reward)
 
+    # Save chart
     plt.figure(figsize=(10, 6))
     plt.plot(portfolio_values, label="Portfolio Value")
     plt.title("Portfolio Value Over Time")
@@ -78,13 +81,14 @@ def optimize_portfolio(tickers, start_date, end_date, initial_balance):
     plt.legend()
     plt.grid()
 
-    buffer = io.BytesIO()
-    plt.savefig(buffer, format="png")
-    buffer.seek(0)
+    chart_path = "portfolio_chart.png"
+    plt.savefig(chart_path)
     plt.close()
 
+    # Prepare the output
     weights = {f"Asset_{i + 1} ({tickers.split(',')[i].strip()})": float(weight) for i, weight in enumerate(portfolio_weights)}
-    return weights, buffer
+    return weights, chart_path
+
 
 def run_optimization(tickers, start_date, end_date, initial_balance):
     if not tickers or not start_date or not end_date: