Update app.py

app.py

@@ -5,7 +5,7 @@ from sklearn.linear_model import LinearRegression, BayesianRidge
 from sklearn.svm import SVR
 from sklearn.preprocessing import MinMaxScaler
 from statsmodels.tsa.arima.model import ARIMA
-from xgboost import XGBRegressor
+#from xgboost import XGBRegressor
 import pandas as pd
 import numpy as np
 from datetime import datetime
@@ -86,10 +86,10 @@ def predict_future_prices(ticker, periods=1825):
     pred_lstm = scaler.inverse_transform(np.array(pred_lstm).reshape(-1, 1))
     future_lstm = pd.DataFrame({'ds': future_dates[:len(pred_lstm)], 'yhat': pred_lstm.flatten()})
     
-  # XGBoost 모델 생성 및 학습
-    model_xgb = XGBRegressor(n_estimators=100, learning_rate=0.1)
-    model_xgb.fit(X.reshape(-1, 1), y)
-    future_xgb = pd.DataFrame({'ds': future_dates, 'yhat': model_xgb.predict(X_future)})
+#  # XGBoost 모델 생성 및 학습
+#   model_xgb = XGBRegressor(n_estimators=100, learning_rate=0.1)
+#   model_xgb.fit(X.reshape(-1, 1), y)
+#   future_xgb = pd.DataFrame({'ds': future_dates, 'yhat': model_xgb.predict(X_future)})
     
 #    # SVR 모델 생성 및 학습
 #   model_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
@@ -109,7 +109,7 @@ def predict_future_prices(ticker, periods=1825):
     fig.add_trace(go.Scatter(x=future_lr['ds'], y=future_lr['yhat'], mode='lines', name='Linear Regression Forecast (Red)', line=dict(color='red')))
     fig.add_trace(go.Scatter(x=future_arima['ds'], y=future_arima['yhat'], mode='lines', name='ARIMA Forecast (Green)', line=dict(color='green')))
     fig.add_trace(go.Scatter(x=future_lstm['ds'], y=future_lstm['yhat'], mode='lines', name='LSTM Forecast (Orange)', line=dict(color='orange')))
-    fig.add_trace(go.Scatter(x=future_xgb['ds'], y=future_xgb['yhat'], mode='lines', name='XGBoost Forecast (Purple)', line=dict(color='purple')))
+#   fig.add_trace(go.Scatter(x=future_xgb['ds'], y=future_xgb['yhat'], mode='lines', name='XGBoost Forecast (Purple)', line=dict(color='purple')))
 #   fig.add_trace(go.Scatter(x=future_svr['ds'], y=future_svr['yhat'], mode='lines', name='SVR Forecast (Brown)', line=dict(color='brown')))
 #   fig.add_trace(go.Scatter(x=future_bayes['ds'], y=future_bayes['yhat'], mode='lines', name='Bayesian Regression Forecast (Pink)', line=dict(color='pink')))
     fig.add_trace(go.Scatter(x=data['ds'], y=data['y'], mode='lines', name='Actual (Black)', line=dict(color='black')))
@@ -129,14 +129,14 @@ with gr.Blocks() as app:
     forecast_data_lr = gr.Dataframe(label="Linear Regression Forecast Data")
     forecast_data_arima = gr.Dataframe(label="ARIMA Forecast Data")
     forecast_data_lstm = gr.Dataframe(label="LSTM Forecast Data")
-    forecast_data_xgb = gr.Dataframe(label="XGBoost Forecast Data")
+#   forecast_data_xgb = gr.Dataframe(label="XGBoost Forecast Data")
 #   forecast_data_svr = gr.Dataframe(label="SVR Forecast Data")
 #   forecast_data_bayes = gr.Dataframe(label="Bayesian Regression Forecast Data")
     
     forecast_button.click(
         fn=predict_future_prices,
         inputs=[ticker_input, periods_input],
-        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr, forecast_data_arima, forecast_data_lstm, forecast_data_xgb] #forecast_data_svr, forecast_data_bayes]
+        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr, forecast_data_arima, forecast_data_lstm] #,forecast_data_xgb, forecast_data_svr, forecast_data_bayes]
     )
 
 app.launch()