Version 1 hotfix #27 for merge

app.py

@@ -195,16 +195,17 @@ def merge_forecast_data(actual, predicted, future):
     return merged_dataframe
 
 @st.cache_data
-def interpret_mape(acc):
-  acc = acc * 100
-  if acc['mape'] < 10:
-    return "Great"
-  elif acc['mape'] < 20:
-    return "Good"
-  elif acc['mape'] < 50:
-    return "Relatively Good"
+def interpret_mape(mape):
+  mape = mape * 100
+  if mape < 10:
+    interpretation = "Highly Accurate"
+  elif mape < 20:
+    interpretation = "Good Accuracy"
+  elif mape < 50:
+    interpretation = "Reasonable Accuracy"
   else:
-    return "Poor"
+    interpretation = "Inaccurate"
+  return mape, interpretation
 
 # TAPAS Model
 
@@ -356,8 +357,8 @@ if (st.session_state.uploaded):
     df = dates_df(future_sales_growth)
 
     test_y, predictions = np.array(test_y), np.array(fitted)
-    acc = forecast_accuracy(predictions, test_y)
-    interpretation = interpret_mape(acc)
+    score = forecast_accuracy(predictions, test_y)
+    mape, interpretation = interpret_mape(score)
 
     col = st.columns(2)
     with col[0]:
@@ -370,7 +371,7 @@ if (st.session_state.uploaded):
       fig.update_layout(title='Forecasted Sales Data', xaxis_title='Date', yaxis_title='Sales')
       fig.update_xaxes(range=['2018-01-01', '2026-01-01'])
       col[0].plotly_chart(fig)
-      col[0].write(f"MAPE score: {acc['mape'] * 100}% - {interpretation}")
+      col[0].write(f"MAPE score: {mape}% - {interpretation}")
     with col[1]:
       col[1].subheader(f"Forecasted sales in the next {period} days")
       col[1].write(df)