Update app.py

app.py

@@ -8,9 +8,10 @@ import plotly.graph_objects as go
 
 def predict_future_prices(ticker, periods=1825):
 data = download_data(ticker)
-
 def download_data(ticker, start_date='2010-01-01'):
-    """ 주식 데이터를 다운로드하고 포맷을 조정하는 함수 """
+    """
+    주식 데이터를 다운로드하고 포맷을 조정하는 함수
+    """
     data = yf.download(ticker, start=start_date)
     if data.empty:
         raise ValueError(f"No data returned for {ticker}")
@@ -24,31 +25,37 @@ def download_data(ticker, start_date='2010-01-01'):
 
 def predict_future_prices(ticker, periods=1825):
     data = download_data(ticker)
+    
     # Prophet 모델 생성 및 학습
     model_prophet = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True)
     model_prophet.fit(data)
+    
     # 미래 데이터 프레임 생성 및 예측
     future = model_prophet.make_future_dataframe(periods=periods, freq='D')
     forecast_prophet = model_prophet.predict(future)
+    
     # Linear Regression 모델 생성 및 학습
     model_lr = LinearRegression()
     X = pd.to_numeric(pd.Series(range(len(data))))
     y = data['y'].values
     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_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))
+    
     # 예측 결과 그래프 생성
     forecast_prophet['ds'] = forecast_prophet['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)'))
     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=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']]
-
+    
 css = """footer { visibility: hidden; }"""
 
 with gr.Blocks(css=css) as app: