Update app

app.py

@@ -18,6 +18,7 @@ st.set_page_config(
 )
 
 # Preprocessing
+@st.cache(show_spinner=False)
 def merge(B, C, A):
   i = j = k = 0
 
@@ -93,6 +94,7 @@ def group_to_three(dataframe):
   return dataframe
 
 # SARIMAX Model
+@st.cache(show_spinner=False)
 def train_test(dataframe):
   n = round(len(dataframe) * 0.2)
   training_y = dataframe.iloc[:-n,0]
@@ -103,7 +105,7 @@ def train_test(dataframe):
   future_X = dataframe.iloc[0:,1:]
   return (training_y, test_y, test_y_series, training_X, test_X, future_X)
 
-
+@st.cache(show_spinner=False)
 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,
@@ -117,6 +119,7 @@ def model_fitting(dataframe, Exo):
     model = futureModel
     return model
 
+@st.cache(show_spinner=False)
 def test_fitting(dataframe, Exo, trainY):
     trainTestModel = auto_arima(X = Exo, y = trainY, start_p=1, start_q=1,
                            test='adf',min_p=1,min_q=1,
@@ -130,6 +133,7 @@ def test_fitting(dataframe, Exo, trainY):
     model = trainTestModel
     return model
 
+@st.cache(show_spinner=False)
 def forecast_accuracy(forecast, actual):
     mape = np.mean(np.abs(forecast - actual)/np.abs(actual)).round(4)  # MAPE
     rmse = (np.mean((forecast - actual)**2)**.5).round(2)  # RMSE
@@ -141,6 +145,7 @@ def forecast_accuracy(forecast, actual):
     minmax = 1 - np.mean(mins/maxs)             # minmax
     return({'mape':mape, 'rmse':rmse, 'corr':corr, 'min-max':minmax})
 
+@st.cache(show_spinner=False)
 def sales_growth(dataframe, fittedValues):
     sales_growth = fittedValues.to_frame()
     sales_growth = sales_growth.reset_index()
@@ -149,11 +154,11 @@ def sales_growth(dataframe, fittedValues):
 
     sales_growth['Sales'] = (sales_growth['Sales']).round(2)
 
-    #Calculate and create the column for sales difference and growth
+    # Calculate and create the column for sales difference and growth
     sales_growth['Forecasted Sales First Difference']=(sales_growth['Sales']-sales_growth['Sales'].shift(1)).round(2)
     sales_growth['Forecasted Sales Growth']=(((sales_growth['Sales']-sales_growth['Sales'].shift(1))/sales_growth['Sales'].shift(1))*100).round(2)
 
-    #Calculate and create the first row for sales difference and growth
+    # Calculate and create the first row for sales difference and growth
     sales_growth['Forecasted Sales First Difference'].iloc[0] = (dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2]).round(2)
     sales_growth['Forecasted Sales Growth'].iloc[0]=(((dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2])/dataframe['Sales'].iloc[-1])*100).round(2)
 
@@ -165,6 +170,7 @@ model_name = "google/tapas-large-finetuned-wtq"
 tokenizer = TapasTokenizer.from_pretrained(model_name)
 model = TapasForQuestionAnswering.from_pretrained(model_name, local_files_only=False)
 
+@st.cache(show_spinner=False)
 def load_tapas_model(model, tokenizer):
   pipe = pipeline("table-question-answering", model=model, tokenizer=tokenizer)
   return pipe
@@ -207,7 +213,13 @@ st.subheader("Welcome User, start using the application by uploading your file i
 
 # Session States
 if 'uploaded' not in st.session_state:
-  st.session_state.uploaded = False
+    st.session_state.uploaded = False
+
+if 'preprocessed_data' not in st.session_state:
+    st.session_state.preprocessed_data = None
+
+if 'fitted_models' not in st.session_state:
+    st.session_state.fitted_models = {}
 
 # Sidebar Menu
 with st.sidebar:
@@ -236,6 +248,7 @@ 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)