Update app.py

app.py

@@ -91,15 +91,15 @@ def predict_future_prices(ticker, periods=1825):
     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)
-    model_svr.fit(X.reshape(-1, 1), y)
-    future_svr = pd.DataFrame({'ds': future_dates, 'yhat': model_svr.predict(X_future)})
+#    # SVR 모델 생성 및 학습
+#   model_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
+#   model_svr.fit(X.reshape(-1, 1), y)
+#   future_svr = pd.DataFrame({'ds': future_dates, 'yhat': model_svr.predict(X_future)})
     
-    # Bayesian Regression 모델 생성 및 학습
-    model_bayes = BayesianRidge()
-    model_bayes.fit(X.reshape(-1, 1), y)
-    future_bayes = pd.DataFrame({'ds': future_dates, 'yhat': model_bayes.predict(X_future)})
+#    # Bayesian Regression 모델 생성 및 학습
+#   model_bayes = BayesianRidge()
+#   model_bayes.fit(X.reshape(-1, 1), y)
+#   future_bayes = pd.DataFrame({'ds': future_dates, 'yhat': model_bayes.predict(X_future)})
     
 
     # 예측 결과 그래프 생성
@@ -110,8 +110,8 @@ def predict_future_prices(ticker, periods=1825):
     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_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=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')))
     
     return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']], future_arima[['ds', 'yhat']], future_lstm[['ds', 'yhat']], future_xgb[['ds', 'yhat']], future_svr[['ds', 'yhat']], future_bayes[['ds', 'yhat']]
@@ -130,13 +130,13 @@ with gr.Blocks() as app:
     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_svr = gr.Dataframe(label="SVR Forecast Data")
-    forecast_data_bayes = gr.Dataframe(label="Bayesian Regression 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()