Update app.py

app.py

@@ -116,4 +116,38 @@ with gr.Blocks() as demo:
         outputs=[output_text, output_plot]
     )
 
-demo.launch()
+demo.launch()
+import matplotlib.pyplot as plt
+
+def plot_stock_data(ticker, start_date, end_date):
+    # Existing code to fetch stock data and make predictions...
+    stock_data = fetch_data(ticker, start_date, end_date)
+    
+    # Assuming you have future_dates and future_prices from your model
+    future_dates = pd.date_range(end_date, periods=90, freq='D')  # Example for future dates
+    future_prices = model_predict(stock_data)  # Example for predicted prices
+    
+    # Check lengths of future_dates and future_prices
+    print(f"Length of future_dates: {len(future_dates)}")
+    print(f"Length of future_prices: {len(future_prices)}")
+
+    # Ensure they are 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='--')
+    
+    plt.xlabel("Date")
+    plt.ylabel("Stock Price")
+    plt.title(f"{ticker} Stock Price Prediction")
+    plt.legend()
+    plt.grid(True)
+    
+    plt.show()
+    return plt.gcf()  # Return the current figure object