Update app.py

app.py

@@ -46,8 +46,8 @@ def predict_future_prices(ticker, periods=1825):
     model_lr.fit(X.values.reshape(-1, 1), y)
     
     # 미래 데이터 프레임 생성 및 예측
-    future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:]
-    future_lr = pd.DataFrame({'ds': future_dates})
+    future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:].strftime('%Y-%m-%d')    
+    future_lr = pd.DataFrame({'ds': future_dates, 'yhat': model_lr.predict(X_future.values.reshape(-1, 1))})
     future_lr['ds'] = future_lr['ds'].dt.strftime('%Y-%m-%d')
     X_future = pd.to_numeric(pd.Series(range(len(data), len(data) + len(future_lr))))
     future_lr['yhat'] = model_lr.predict(X_future.values.reshape(-1, 1))
@@ -85,8 +85,9 @@ def predict_future_prices(ticker, periods=1825):
         pred_lstm.append(pred[0, 0])
 
     pred_lstm = scaler.inverse_transform(np.array(pred_lstm).reshape(-1, 1))
-    future_lstm = pd.DataFrame({'ds': future_dates[:periods], 'yhat': pred_lstm.flatten()})
+    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.values.reshape(-1, 1), y)