Update app.py

app.py

@@ -151,3 +151,38 @@ def plot_stock_data(ticker, start_date, end_date):
     
     plt.show()
     return plt.gcf()  # Return the current figure object
+    import matplotlib.pyplot as plt
+import pandas as pd
+
+def plot_stock_data(ticker, start_date, end_date):
+    # Fetch stock data and make predictions...
+    stock_data = fetch_data(ticker, start_date, end_date)
+    
+    # Example future dates (change this according to your actual future date range logic)
+    future_dates = pd.date_range(end_date, periods=90, freq='D')  # Generates 90 future dates
+    future_prices = model_predict(stock_data)  # Generates 89 future prices (example)
+
+    # Ensure both future_dates and future_prices have the same length
+    min_length = min(len(future_dates), len(future_prices))
+    future_dates = future_dates[:min_length]
+    future_prices = future_prices[:min_length]
+
+    # Plot historical stock data
+    plt.figure(figsize=(10, 5))
+    plt.plot(stock_data.index, stock_data['Close'], label="Historical Prices")
+
+    # Plot future predicted prices
+    plt.plot(future_dates, future_prices, label="Predicted Future Prices", linestyle='--')
+
+    # Add labels and title
+    plt.xlabel("Date")
+    plt.ylabel("Stock Price")
+    plt.title(f"{ticker} Stock Price Prediction")
+    plt.legend()
+    plt.grid(True)
+
+    # Show the plot
+    plt.show()
+    
+    return plt.gcf()  # Return the current figure object for Gradio to display
+