Update app.py

app.py

@@ -1,11 +1,10 @@
 import gradio as gr
 import yfinance as yf
 from prophet import Prophet
-import plotly.express as px
+import pandas as pd
 from datetime import datetime
 import plotly.graph_objects as go
 
-
 def download_data(ticker, start_date='2010-01-01'):
     """ 데이터를 다운로드하고 적절히 포맷팅하는 함수 """
     data = yf.download(ticker, start=start_date)
@@ -20,38 +19,33 @@ def download_data(ticker, start_date='2010-01-01'):
     return data
 
 def predict_future_prices(ticker, periods=1825):  # 5년간의 데이터 예측
-    data = yf.download(ticker, start="2010-01-01")['Adj Close'].reset_index()
-    data.rename(columns={'Date': 'ds', 'Adj Close': 'y'}, inplace=True)
+    data = download_data(ticker)
     
+    # Prophet 모델 생성 및 훈련
     model = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True)
     model.fit(data)
     
+    # 미래 데이터프레임 생성 및 예측
     future = model.make_future_dataframe(periods=periods, freq='D')
     forecast = model.predict(future)
     
-    # Timestamp를 문자열로 변환
-    forecast['ds'] = forecast['ds'].dt.strftime('%Y-%m-%d')
-    
+    # 예측 결과를 그래프로 표현
     fig = go.Figure(data=[go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast')])
-    fig.update_layout(title=f'5-Year Future Price Forecast for {ticker}', xaxis_title='Date', yaxis_title='Predicted Price')
-
-    return fig, forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].to_dict(orient='records')
+    return fig, forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]  # Pydantic과 같은 엄격한 형식 검사 없이 DataFrame으로 직접 전달
 
-# 이제 이 함수를 Gradio 인터페이스와 연결하여 사용자가 시각화를 볼 수 있게 합니다.
-
-
-# Gradio 인터페이스 설정
+# Gradio 인터페이스 설정 및 실행
 with gr.Blocks() as app:
     with gr.Row():
-        ticker_input = gr.Textbox(value="AAPL", label="Enter Stock Ticker")
-        forecast_button = gr.Button("Generate 5-Year Forecast")
-    
-    forecast_chart = gr.Plot(label="Future Price Forecast")
-    forecast_data = gr.Dataframe(label="Forecast Data Table")
+        ticker_input = gr.Textbox(value="AAPL", label="Enter Stock Ticker for Forecast")
+        periods_input = gr.Number(value=1825, label="Forecast Period (days)")
+        forecast_button = gr.Button("Generate Forecast")
+        
+    forecast_chart = gr.Plot(label="Forecast Chart")
+    forecast_data = gr.Dataframe(label="Forecast Data")
     
     forecast_button.click(
         fn=predict_future_prices,
-        inputs=ticker_input,
+        inputs=[ticker_input, periods_input],
         outputs=[forecast_chart, forecast_data]
     )