Update app

app.py

@@ -112,8 +112,7 @@ def series_to_df_exogenous(series):
   return dataframe
 
 @st.cache_data
-def series_to_df_dates(series):
-  dataframe = series.to_frame()
+def dates_df(dataframe):
   dataframe = dataframe.reset_index()
   dataframe['Date'] = dataframe['Date'].dt.strftime('%B %d, %Y')
   dataframe[df.columns] = dataframe[df.columns].astype(str)
@@ -263,7 +262,6 @@ with st.sidebar:
         st.write("Your uploaded data:")
         st.write(df)
 
-        # Data pre-processing
         df = drop(df)
         df = date_format(df)
         merge_sort(df)
@@ -274,7 +272,7 @@ with st.sidebar:
        st.download_button("Download our sample CSV", f, file_name='sample.csv')
 
 if (st.session_state.uploaded):
-  st.line_chart(series)
+  st.line_chart(series) 
   
   MIN_DAYS = 30
   MAX_DAYS = 90
@@ -289,73 +287,37 @@ if (st.session_state.uploaded):
   if (forecast_button):
     df = series_to_df_exogenous(series)
 
-    auto_train_test = train_test(df)
-    training_y, test_y, test_y_series, training_X, test_X, future_X = auto_train_test
-
-    # Auto_arima to fit the model to forecast future sales
-    future_model = model_fitting(df, future_X)
-    # Auto_arima to check the accuracy of the train test split
+    train = train_test(df)
+    training_y, test_y, test_y_series, training_X, test_X, future_X = train
     train_test_model = test_fitting(df, training_X, training_y)
 
-    # Forecast (testing)
     n_periods = round(len(df) * 0.2)
+    future_n_periods = forecast_period + n_periods
+
     fitted, confint = train_test_model.predict(X=test_X, n_periods=n_periods, return_conf_int=True)
+
     index_of_fc = test_y_series.index
 
     # make series for plotting purpose
     fitted_series = pd.Series(fitted)
-    fitted_series.index = index_of_fc
+    fitted_series.index=index_of_fc
     lower_series = pd.Series(confint[:, 0], index=index_of_fc)
     upper_series = pd.Series(confint[:, 1], index=index_of_fc)
 
-    test_y, predictions = np.array(test_y), np.array(fitted)
-    forecast_accuracy(predictions, test_y)
-
-    # Plot
-    # plt.figure(figsize=(12,8))
-    # plt.plot(training_y[-80:])
-    # plt.plot(test_y, color = 'red', label = 'Actual Sales')
-    # plt.plot(fitted_series, color='darkgreen', label = 'Predicted Sales')
-    # plt.fill_between(lower_series.index,
-    #                  lower_series,
-    #                  upper_series,
-    #                  color='k', alpha=.15)
-
-    # plt.title("SARIMAX - Forecast of Retail Sales VS Actual Sales")
-    # plt.legend(loc='upper left', fontsize=8)
-    # plt.show()
+    #Future predictions
+    frequency = '3D'
+    future_fitted, confint = train_test_model.predict(X=df.iloc[-future_n_periods:,1:], n_periods=future_n_periods, return_conf_int=True, freq=frequency)
+    future_index_of_fc = pd.date_range(df['Sales'].index[-1], periods = future_n_periods, freq=frequency)
 
-    # Combine traces and create layout
-
-    # Forecast (actual)
-    n_periods = forecast_period
-    freq='3D'
-    future_fitted, confint = future_model.predict(X=df.iloc[-n_periods:,1:], n_periods=n_periods, return_conf_int=True, freq=freq)
-    future_index_of_fc = pd.date_range(df['Sales'].index[-1], periods = n_periods, freq=freq)
-
-    # make series for plotting purpose
+    # make series for future plotting purpose
     future_fitted_series = pd.Series(future_fitted)
-    future_fitted_series.index=future_index_of_fc
+    future_fitted_series.index = future_index_of_fc
     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.figure(figsize=(12,8))
-    # plt.plot(df['Sales'][-50:])
-    # plt.plot(future_fitted_series, color='darkgreen')
-    # plt.fill_between(future_lower_series.index,
-    #                  future_lower_series,
-    #                  future_upper_series,
-    #                  color='k', alpha=.15)
-
-    # plt.title("SARIMA - Final Forecast of Retail Sales")
-    # plt.show()
-
-    # Create traces for each line and fill_between
-
-    auto_sales_growth = sales_growth(df, future_fitted_series)
+    future_sales_growth = sales_growth(df, future_fitted_series)
 
-    df = series_to_df_dates(auto_sales_growth)
+    df = dates_df(future_sales_growth)
     st.write("Forecasted sales in the next 3 months")
     st.write(df)
 
@@ -363,6 +325,6 @@ if (st.session_state.uploaded):
         question = st.text_input('Ask a Question about the Forecasted Data', placeholder="What is the total sales in the month of December?")
         query_button = st.form_submit_button(label='Generate Answer')
 
-    if query_button:
+    if query_button or question:
         answer = get_converted_answer(df, question)
         st.write("The answer is:", answer)