Update app.py

app.py

@@ -55,23 +55,25 @@ def predict_stock_price(ticker, open_price, high_price, low_price, close_price,
         lstm_predictions = scaler.inverse_transform(lstm_predictions)
         next_day_lstm_price = lstm_predictions[-1][0]
         
-        # Plot the historical data and the predicted future price
-        plt.figure(figsize=(12, 6))
-        plt.plot(data.index, data['Close'], label='Historical Data')
-        plt.axhline(y=next_day_lstm_price, color='g', linestyle='--', label='Predicted Future Price')
-        plt.title(f'Historical Stock Price and Predicted Future Price for {ticker}')
+        # Plot the data
+        plt.figure(figsize=(10, 6))
+        plt.plot(data.index, data['Close'], label='Historical Close Prices')
+        plt.axvline(x=data.index[-1], color='r', linestyle='--', label='Prediction Date')
+        plt.plot([data.index[-1], data.index[-1] + pd.DateOffset(1)], [data['Close'].iloc[-1], next_day_lstm_price], 'go-', label='Predicted Price')
+        plt.title(f'Predicted Closing Price for {ticker}')
         plt.xlabel('Date')
-        plt.ylabel('Price (USD)')
+        plt.ylabel('Close Price USD ($)')
         plt.legend()
-        
-        # Save the plot to a file
-        plot_filename = 'stock_price_prediction.png'
-        plt.savefig(plot_filename)
+
+        # Save the plot to a buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
         plt.close()
         
         result = f"Predicted future price for {ticker}: ${next_day_lstm_price:.2f}"
 
-        return result, plot_filename
+        return result,buf
     except Exception as e:
         return str(e)
 
@@ -103,7 +105,7 @@ iface = gr.Interface(
         gr.Number(label="Adj Close"),
         gr.Number(label="Volume")
     ],
-    outputs=[gr.Textbox(),gr.Image()],
+    outputs=[gr.Textbox(), gr.Image(type="plot")],
     title="Stock Price Predictor",
     description="Select the stock ticker and input the last recorded values to predict the closing price using the LSTM model."
 )