Update app

app.py

@@ -4,6 +4,7 @@ from datetime import datetime
 
 import numpy as np
 import pmdarima as pm
+import matplotlib.pyplot as plt
 from pmdarima import auto_arima
 
 import torch
@@ -92,7 +93,8 @@ def group_to_three(dataframe):
   return dataframe
 
 # SARIMAX Model
-def train_test(dataframe, n):
+def train_test(dataframe):
+  n = round(len(dataframe) * 0.2)
   training_y = dataframe.iloc[:-n,0]
   test_y = dataframe.iloc[-n:,0]
   test_y_series = pd.Series(test_y, index=dataframe.iloc[-n:, 0].index)
@@ -101,6 +103,7 @@ def train_test(dataframe, n):
   future_X = dataframe.iloc[0:,1:]
   return (training_y, test_y, test_y_series, training_X, test_X, future_X)
 
+
 def model_fitting(dataframe, Exo):
     futureModel = pm.auto_arima(dataframe['Sales'], X=Exo, start_p=1, start_q=1,
                          test='adf',min_p=1,min_q=1,
@@ -200,7 +203,7 @@ def get_converted_answer(table, query):
 # Web Application
 
 st.title("Sales Forecasting Dashboard 📈")
-st.write("Welcome User, start using the application by uploading your file in the sidebar!")
+st.subheader("Welcome User, start using the application by uploading your file in the sidebar!")
 
 # Session States
 if 'uploaded' not in st.session_state:
@@ -233,6 +236,9 @@ with st.sidebar:
 if (st.session_state.uploaded):
   st.line_chart(df)
 
+  period = st.slider('How many days would you like to forecast?', min_value=30, max_value=90)
+  forecast_period = round(period / 3)
+
   forecast_button = st.button(
     'Start Forecasting',
     key='forecast_button',
@@ -259,7 +265,7 @@ if (st.session_state.uploaded):
     train_test_model = test_fitting(df, training_X, training_y)
 
     # Forecast (testing)
-    n_periods = 20
+    n_periods = round(len(df) * 0.2)
     fitted, confint = train_test_model.predict(X=test_X, n_periods=n_periods, return_conf_int=True)
     index_of_fc = test_y_series.index
 
@@ -272,8 +278,23 @@ if (st.session_state.uploaded):
     test_y, predictions = np.array(test_y), np.array(fitted)
     forecast_accuracy(predictions, test_y)
 
+    st.title("Forecasted Sales")
+
+    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()
+
     # Forecast (actual)
-    n_periods = 36
+    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)
@@ -284,12 +305,17 @@ 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.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)
 
-    # Streamlit plot
-    # st.title("Forecasted Sales")
-    # st.line_chart(df['Sales'][-50:])
-    # st.line_chart(future_fitted_series, use_container_width=True)
-    # st.area_chart(pd.concat([future_lower_series, future_upper_series], axis=1), color="#808080")
+    plt.title("SARIMA - Final Forecast of Retail Sales")
+    plt.show()
 
     auto_sales_growth = sales_growth(df, future_fitted_series)
     df = auto_sales_growth
@@ -300,10 +326,7 @@ if (st.session_state.uploaded):
     st.write("Forecasted sales in the next 3 months")
     st.write(df)
 
-    with st.form("question_form"):
-        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:
+    question = st.text_input('Ask a Question about the Forecasted Data', placeholder="What is the total sales in the month of December?")
+    if question:
         answer = get_converted_answer(df, question)
         st.write("The answer is:", answer)