feat: spinner while HMM extract results

& refactor: unify model output format
#48 #44

streamlit/views/prediction.py

@@ -124,7 +124,7 @@ def get_stock_data(ticker, period, interval):
 
  st.plotly_chart(fig, use_container_width=True)
 
- # Non-DL model matching 시각화
+ # AR model prediction 시각화
  def generate_stock_prediction(stock_ticker, period, interval):
  # Try to generate the predictions
  try:
@@ -176,8 +176,8 @@ def generate_stock_prediction(stock_ticker, period, interval):
  # Check if the data is not None
  if train_df is not None and (forecast >= 0).all() and (predictions >= 0).all():
  # Add a title to the stock prediction graph
- st.markdown("## **Non DL Stock Prediction**")
-
+ layout = go.Layout(title='AR Prediction', xaxis=dict(title='Date'), yaxis=dict(title='Stock Price'))
+ 
  # Create a plot for the stock prediction
  fig = go.Figure(
  data=[
@@ -209,7 +209,7 @@ def generate_stock_prediction(stock_ticker, period, interval):
  mode="lines",
  line=dict(color="green"),
  ),
- ]
+ ], layout=layout
  )
 
  # Customize the stock prediction graph
@@ -218,20 +218,16 @@ def generate_stock_prediction(stock_ticker, period, interval):
  # Use the native streamlit theme.
  st.plotly_chart(fig, use_container_width=True)
 
- # If the data is None
- else:
- # Add a title to the stock prediction graph
- st.markdown("## **Stock Prediction**")
-
- # Add a message to the stock prediction graph
- st.markdown("### **No data available for the selected stock**") 
- if st.button("Start Non-DL model matching"): 
- hmm = HMM(data)
- predicted_close_prices = hmm.test_predictions()
- fig = hmm.visualize_hmm(predicted_close_prices)
- st.plotly_chart(fig, use_container_width=True) 
+ # HMM prediction 시각화
+ if st.button("Start HMM prediction"): 
+ with st.spinner('Wait for model output...'): 
+ hmm = HMM(data)
+ predicted_close_prices = hmm.test_predictions()
+ fig = hmm.visualize_hmm(predicted_close_prices)
+ st.plotly_chart(fig, use_container_width=True) 
 
- # DL model matching 시각화
+
+ # DL model prediction 시각화
  data_close = data[["Close"]]
  data_close.fillna(method='pad')
 
@@ -268,12 +264,12 @@ def generate_stock_prediction(stock_ticker, period, interval):
  predicted_price_trace = go.Scatter(x=data_close[len(data_close)-len(y_test):].index, y=y_test_pred.flatten(), mode='lines', line=dict(color="green"), name='Predicted Stock Price')
  forecast_price_trace = go.Scatter(x=forecast_dates, y=forecast, mode='lines', line=dict(color="red"), name='Forecasted Stock Price')
 
- layout = go.Layout(title='LSTM Stock Price Prediction', xaxis=dict(title='Date'), yaxis=dict(title='Stock Price'))
+ layout = go.Layout(title='LSTM Prediction', xaxis=dict(title='Date'), yaxis=dict(title='Stock Price'))
  fig2 = go.Figure(data=[real_price_trace, predicted_price_trace, forecast_price_trace], layout=layout)
 
  st.plotly_chart(fig2)
 
- # Image-based CNN model matching 시각화
+ # Image-based CNN model prediction 시각화
  cnn_model_inference( company, ticker, period, interval)