Version 1 hotfix #29 for merge

app.py

@@ -196,15 +196,15 @@ def merge_forecast_data(actual, predicted, future):
 
 @st.cache_data
 def interpret_mape(mape_score):
-  score = mape_score * 100
+  score = (mape_score * 100).round(2)
   if score < 10:
-    interpretation = "Highly Accurate"
+    interpretation = "Great"
   elif score < 20:
-    interpretation = "Good Accuracy"
+    interpretation = "Good"
   elif score < 50:
-    interpretation = "Reasonable Accuracy"
+    interpretation = "Relatively good"
   else:
-    interpretation = "Inaccurate"
+    interpretation = "Poor"
   return score, interpretation
 
 # TAPAS Model
@@ -336,22 +336,6 @@ if (st.session_state.uploaded):
     future_lower_series = pd.Series(confint[:, 0], index=future_index_of_fc)
     future_upper_series = pd.Series(confint[:, 1], index=future_index_of_fc)
 
-    # Plot
-    # plt.plot(df['Sales'], color='b', label = 'Actual Sales')
-    # plt.plot(test_y, color='b')
-    # plt.plot(fitted_series, color='r', label = 'Predicted Sales')
-    # plt.title("SARIMAX - Forecast of Auto Business Retail Sales VS Actual Sales")
-    # plt.legend(loc='upper left', fontsize=8)
-    # plt.plot(future_fitted_series, color='darkgreen', label ='Future Forecasted Sales')
-    # plt.fill_between(future_lower_series.index,
-    #                  future_lower_series,
-    #                  future_upper_series,
-    #                  color='k', alpha=.15)
-    # plt.fill_between(lower_series.index,
-    #                  lower_series,
-    #                  upper_series,
-    #                  color='k', alpha=.15)
-
     future_sales_growth = sales_growth(df, future_fitted_series)
     future_sales_growth = future_sales_growth.iloc[n_periods:]
     df = dates_df(future_sales_growth)
@@ -365,11 +349,11 @@ if (st.session_state.uploaded):
       merged_data = merge_forecast_data(df['Sales'], fitted_series, future_fitted_series)
       
       fig = go.Figure()
-      fig.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Actual Sales'], mode='lines', name='Actual Sales'))
+      fig.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=df['Sales'].to_frame(), mode='lines', name='Actual Sales'))
       fig.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Predicted Sales'], mode='lines', name='Predicted Sales'))
       fig.add_trace(go.Scatter(x=merged_data[merged_data.columns[0]], y=merged_data['Forecasted Future Sales'], mode='lines', name='Forecasted Future Sales'))
       fig.update_layout(title='Forecasted Sales Data', xaxis_title='Date', yaxis_title='Sales')
-      fig.update_xaxes(range=['2018-01-01', '2026-01-01'])
+      fig.update_xaxes(range=['2020-01-01', '2025-01-01']) # TODO: Change this to be adaptive
       col[0].plotly_chart(fig)
       col[0].write(f"MAPE score: {mape}% - {interpretation}")
     with col[1]: