Update app.py

app.py

@@ -61,7 +61,7 @@ def predict_stock_price(ticker, open_price, close_price):
         return str(e)
 
 # Function to predict next month's price
-def predict_next_month_price(ticker):
+def predict_next_month_price(ticker, close_price):
     try:
         start_date = '2010-01-01'
         end_date = datetime.now().strftime('%Y-%m-%d')
@@ -72,6 +72,11 @@ def predict_next_month_price(ticker):
         dataset = data.values
         scaled_data = scaler.transform(dataset)
 
+        # Append the user inputs as the last row in the data
+        user_input = np.array([[close_price]])
+        user_input_scaled = scaler.transform(user_input)
+        scaled_data = np.vstack([scaled_data, user_input_scaled])
+
         # Prepare the data for LSTM
         x_test_lstm = []
         for i in range(60, len(scaled_data)):
@@ -85,8 +90,8 @@ def predict_next_month_price(ticker):
         for _ in range(30):
             pred = lstm_model.predict(x_test_lstm[-1].reshape(1, 60, 1))
             predictions.append(pred[0])
-            x_test_lstm = np.append(x_test_lstm, pred, axis=0)
-            x_test_lstm = x_test_lstm[1:]
+            new_input = np.append(x_test_lstm[-1][1:], pred)
+            x_test_lstm = np.append(x_test_lstm, new_input.reshape(1, 60, 1), axis=0)
 
         predictions = np.array(predictions)
         next_month_predictions = scaler.inverse_transform(predictions)
@@ -122,9 +127,10 @@ with gr.Blocks() as app:
     with gr.Tab("Predict Next Month's Price"):
         gr.Markdown("## Predict Next Month's Price")
         next_month_ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
+        next_month_close_price = gr.Number(label="Close")
         next_month_predict_button = gr.Button("Predict")
         next_month_predict_output = gr.Textbox()
-        next_month_predict_button.click(predict_next_month_price, inputs=[next_month_ticker_input], outputs=next_month_predict_output)
+        next_month_predict_button.click(predict_next_month_price, inputs=[next_month_ticker_input, next_month_close_price], outputs=next_month_predict_output)
     
     with gr.Tab("View Historical Data"):
         gr.Markdown("## View Historical Data")