Update app.py

app.py

@@ -6,8 +6,6 @@ import yfinance as yf
 from datetime import datetime
 from tensorflow.keras.models import load_model
 from joblib import load
-import io
-from PIL import Image
 
 # Load the saved LSTM model and scaler
 lstm_model = load_model('lstm_model.h5')
@@ -57,27 +55,12 @@ 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 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('Close Price USD ($)')
-        plt.legend()
-
-        # 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, buf
+        return result
     except Exception as e:
         return str(e)
 
@@ -109,7 +92,7 @@ iface = gr.Interface(
         gr.Number(label="Adj Close"),
         gr.Number(label="Volume")
     ],
-    outputs=[gr.Textbox(), gr.Image(type="file")],
+    outputs=gr.Textbox(),
     title="Stock Price Predictor",
     description="Select the stock ticker and input the last recorded values to predict the closing price using the LSTM model."
 )