Update app.py

app.py

@@ -5,9 +5,10 @@ import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
 import gradio as gr
 import matplotlib.pyplot as plt
+import os
 
 # Define stock tickers for the dropdown
-tickers = ['AAPL', 'MSFT', 'GOOGL', 'TSLA', 'AMZN', 'FB', 'NFLX', 'NVDA', 'INTC', 'IBM']
+tickers = ['AAPL', 'MSFT', 'GOOGL', 'TSLA', 'AMZN', 'META', 'NFLX', 'NVDA', 'INTC', 'IBM']
 
 # Function to fetch stock data and make predictions
 def stock_prediction_app(ticker, start_date, end_date):
@@ -38,7 +39,7 @@ def stock_prediction_app(ticker, start_date, end_date):
     
     # Define LSTM model
     lstm_model = tf.keras.Sequential([
-        tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(60, 1)),
+        tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)),
         tf.keras.layers.LSTM(50, return_sequences=False),
         tf.keras.layers.Dense(25),
         tf.keras.layers.Dense(1)
@@ -48,7 +49,7 @@ def stock_prediction_app(ticker, start_date, end_date):
     lstm_model.compile(optimizer='adam', loss='mean_squared_error')
     
     # Train the model
-    lstm_model.fit(X_train, y_train, batch_size=1, epochs=1)
+    lstm_model.fit(X_train, y_train, batch_size=1, epochs=1, verbose=0)
 
     # Predict on the same data (just for demonstration)
     predictions = lstm_model.predict(X_train)
@@ -56,18 +57,20 @@ def stock_prediction_app(ticker, start_date, end_date):
 
     # Create a plot to show predictions
     plt.figure(figsize=(10, 5))
-    plt.plot(df_close.values, label='Actual Stock Price')
-    plt.plot(predictions, label='Predicted Stock Price')
+    plt.plot(df_close.values, label='Actual Stock Price', color='blue')
+    plt.plot(predictions, label='Predicted Stock Price', color='orange')
     plt.title(f'{ticker} Stock Price Prediction')
     plt.xlabel('Days')
     plt.ylabel('Stock Price')
     plt.legend()
     
     # Save the plot to display in Gradio app
-    plt.savefig('stock_prediction_plot.png')
+    plot_path = 'stock_prediction_plot.png'
+    plt.savefig(plot_path)
+    plt.close()  # Close the plot to prevent display issues in some environments
 
     # Return a message and the path to the saved plot
-    return f"Prediction complete for {ticker} from {start_date} to {end_date}", 'stock_prediction_plot.png'
+    return f"Prediction complete for {ticker} from {start_date} to {end_date}", plot_path
 
 # Create the Gradio UI for the app
 app = gr.Blocks()
@@ -86,7 +89,7 @@ with app:
     predict_button = gr.Button("Predict")
     
     # Output fields for text and image
-    output_text = gr.Textbox(label="Prediction Result")
+    output_text = gr.Textbox(label="Prediction Result", interactive=False)
     output_image = gr.Image(label="Stock Price Graph")
     
     # Set up button click event to run the prediction function