Update app.py

app.py

@@ -46,23 +46,24 @@ def predict_future_prices(ticker, periods=1825):
     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))
 
-    # NeuralProphet 모델 생성 및 학습
+# NeuralProphet 모델 생성 및 학습
     model_np = NeuralProphet()
-    model_np.fit(data, freq='D')
+    metrics = model_np.fit(data, freq='D')
     future_np = model_np.make_future_dataframe(data, periods=periods)
     forecast_np = model_np.predict(future_np)
-    
-    # 예측 결과 그래프 생성
+
+# 예측 결과 그래프 생성
     forecast_prophet['ds'] = forecast_prophet['ds'].dt.strftime('%Y-%m-%d')
-    forecast_np['ds'] = forecast_np['ds'].dt.strftime('%Y-%m-%d')
+    forecast_np['ds'] = pd.to_datetime(forecast_np['ds']).dt.strftime('%Y-%m-%d')
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='Prophet Forecast (Blue)', line=dict(color='blue')))
     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=forecast_np['ds'], y=forecast_np['yhat1'], mode='lines', name='NeuralProphet Forecast (Green)', line=dict(color='green')))
     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']], forecast_np[['ds', 'yhat1']]
 
+
 # Gradio 인터페이스 설정 및 실행
 with gr.Blocks() as app:
     with gr.Row():