Circhastic commited on
Commit
0662fc3
1 Parent(s): 2b2d52f

Fix app graph colors and debug

Browse files
Files changed (1) hide show
  1. app.py +8 -2
app.py CHANGED
@@ -199,13 +199,17 @@ def interpret_mape(mape_score):
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  score = (mape_score * 100).round(2)
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  if score < 10:
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  interpretation = "Great"
 
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  elif score < 20:
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  interpretation = "Good"
 
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  elif score < 50:
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  interpretation = "Relatively good"
 
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  else:
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  interpretation = "Poor"
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- return score, interpretation
 
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  # TAPAS Model
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@@ -341,7 +345,7 @@ if (st.session_state.uploaded):
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  test_y, predictions = np.array(test_y), np.array(fitted)
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  score = forecast_accuracy(predictions, test_y)
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- mape, interpretation = interpret_mape(score['mape'])
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  merged_data = merge_forecast_data(df['Sales'], fitted_series, future_fitted_series)
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  merged_data_dates = merged_data.copy()
@@ -349,6 +353,7 @@ if (st.session_state.uploaded):
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  col_charts = st.columns(2)
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  print(merged_data_dates[merged_data.columns[0]]) # for debugging
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  min_date = merged_data_dates[merged_data_dates.columns[0]].min()
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  max_date = merged_data_dates[merged_data_dates.columns[0]].max()
@@ -369,6 +374,7 @@ if (st.session_state.uploaded):
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  fig_forecast = go.Figure()
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  fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Actual Sales'], mode='lines', name='Actual Sales'))
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  fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Forecasted Future Sales'], mode='lines', name='Forecasted Future Sales'))
 
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  fig_forecast.update_layout(title='Forecasted Sales Data', xaxis_title='Date', yaxis_title='Sales')
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  # fig_forecast.update_xaxes(range=[min_date, max_date])
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  fig_forecast.update_layout(
 
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  score = (mape_score * 100).round(2)
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  if score < 10:
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  interpretation = "Great"
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+ color = "green"
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  elif score < 20:
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  interpretation = "Good"
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+ color = "seagreen"
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  elif score < 50:
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  interpretation = "Relatively good"
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+ color = "orange"
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  else:
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  interpretation = "Poor"
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+ color = "red"
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+ return score, interpretation, color
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  # TAPAS Model
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  test_y, predictions = np.array(test_y), np.array(fitted)
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  score = forecast_accuracy(predictions, test_y)
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+ mape, interpretation, mape_color = interpret_mape(score['mape'])
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  merged_data = merge_forecast_data(df['Sales'], fitted_series, future_fitted_series)
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  merged_data_dates = merged_data.copy()
 
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  col_charts = st.columns(2)
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+ print(merged_data[merged_data.columns[0]]) # for debugging
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  print(merged_data_dates[merged_data.columns[0]]) # for debugging
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  min_date = merged_data_dates[merged_data_dates.columns[0]].min()
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  max_date = merged_data_dates[merged_data_dates.columns[0]].max()
 
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  fig_forecast = go.Figure()
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  fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Actual Sales'], mode='lines', name='Actual Sales'))
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  fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Forecasted Future Sales'], mode='lines', name='Forecasted Future Sales'))
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+ fig_forecast.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Forecasted Sales'], mode='lines', name='Forecasted Sales', line=dict(color=mape_color)))
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  fig_forecast.update_layout(title='Forecasted Sales Data', xaxis_title='Date', yaxis_title='Sales')
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  # fig_forecast.update_xaxes(range=[min_date, max_date])
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  fig_forecast.update_layout(