app.py

import streamlit as st
import pandas as pd
from datetime import datetime

import numpy as np
import pmdarima as pm
from pmdarima import auto_arima

import torch
from transformers import pipeline, TapasTokenizer, TapasForQuestionAnswering

st.set_page_config(
      page_title="Sales Forecasting System",
      page_icon="📈",
      layout="wide",
      initial_sidebar_state="expanded",
)

# Preprocessing
def merge(B, C, A):
  i = j = k = 0

  # Convert 'Date' columns to datetime.date objects
  B['Date'] = pd.to_datetime(B['Date']).dt.date
  C['Date'] = pd.to_datetime(C['Date']).dt.date
  A['Date'] = pd.to_datetime(A['Date']).dt.date

  while i < len(B) and j < len(C):
    if B['Date'].iloc[i] <= C['Date'].iloc[j]:
      A['Date'].iloc[k] = B['Date'].iloc[i]
      A['Sales'].iloc[k] = B['Sales'].iloc[i]
      i += 1
      
    else:
      A['Date'].iloc[k] = C['Date'].iloc[j]
      A['Sales'].iloc[k] = C['Sales'].iloc[j]
      j += 1
    k += 1

  while i < len(B):
    A['Date'].iloc[k] = B['Date'].iloc[i]
    A['Sales'].iloc[k] = B['Sales'].iloc[i]
    i += 1
    k += 1

  while j < len(C):
    A['Date'].iloc[k] = C['Date'].iloc[j]
    A['Sales'].iloc[k] = C['Sales'].iloc[j]
    j += 1
    k += 1

  return A

def merge_sort(dataframe):
  if len(dataframe) > 1:
      center = len(dataframe) // 2
      left = dataframe.iloc[:center]
      right = dataframe.iloc[center:]
      merge_sort(left)
      merge_sort(right)

      return merge(left, right, dataframe)

  else:
      return dataframe

def drop (dataframe):
  def get_columns_containing(dataframe, substrings):
    return [col for col in dataframe.columns if any(substring.lower() in col.lower() for substring in substrings)]

  columns_to_keep = get_columns_containing(dataframe, ["date", "sale"])
  dataframe = dataframe.drop(columns=dataframe.columns.difference(columns_to_keep))
  dataframe = dataframe.dropna()
    
  return dataframe

def date_format(dataframe):
  for i, d, s in dataframe.itertuples():
    dataframe['Date'][i] = dataframe['Date'][i].strip()

  for i, d, s in dataframe.itertuples():
    new_date = datetime.strptime(dataframe['Date'][i], "%m/%d/%Y").date()
    dataframe['Date'][i] = new_date

  return dataframe

def group_to_three(dataframe):
  dataframe['Date'] = pd.to_datetime(dataframe['Date'])
  dataframe = dataframe.groupby([pd.Grouper(key='Date', freq='3D')])['Sales'].mean().round(2)
  dataframe = dataframe.replace(0, np.nan).dropna()

  return dataframe

# SARIMAX Model
def train_test(dataframe, n):
  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)
  training_X = dataframe.iloc[:-n,1:]
  test_X = dataframe.iloc[-n:,1:]
  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,
                         max_p=3, max_q=3, m=12,
                         start_P=0, seasonal=True,
                         d=None, D=1, trace=True,
                         error_action='ignore',
                         suppress_warnings=True,
                         stepwise=True)
    model = futureModel
    return model

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,
                           max_p=3, max_q=3, m=12,
                           start_P=0, seasonal=True,
                           d=None, D=1, trace=True,
                           error_action='ignore',
                           suppress_warnings=True,
                           stepwise=True)
    model = trainTestModel
    return model

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
    corr = np.corrcoef(forecast, actual)[0,1]   # corr
    mins = np.amin(np.hstack([forecast[:,None],
                            actual[:,None]]), axis=1)
    maxs = np.amax(np.hstack([forecast[:,None],
                            actual[:,None]]), axis=1)
    minmax = 1 - np.mean(mins/maxs)             # minmax
    return({'mape':mape, 'rmse':rmse, 'corr':corr, 'min-max':minmax})

def sales_growth(dataframe, fittedValues):
    sales_growth = fittedValues.to_frame()
    sales_growth = sales_growth.reset_index()
    sales_growth.columns = ("Date", "Sales")
    sales_growth = sales_growth.set_index('Date')

    sales_growth['Sales'] = (sales_growth['Sales']).round(2)

    #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
    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)


    return sales_growth

# TAPAS Model
model_name = "google/tapas-large-finetuned-wtq"
tokenizer = TapasTokenizer.from_pretrained(model_name)
model = TapasForQuestionAnswering.from_pretrained(model_name, local_files_only=False)

def load_tapas_model(model, tokenizer):
  pipe = pipeline("table-question-answering", model=model, tokenizer=tokenizer)
  return pipe

pipe = load_tapas_model(model, tokenizer)

def get_answer(table, query):
    answers = pipe(table=table, query=query)
    return answers

def convert_answer(answer):
    if answer['aggregator'] == 'SUM':
      cells = answer['cells']
      converted = sum(float(value.replace(',', '')) for value in cells)
      return converted

    if answer['aggregator'] == 'AVERAGE':
      cells = answer['cells']
      values = [float(value.replace(',', '')) for value in cells]
      converted = sum(values) / len(values)
      return converted

    if answer['aggregator'] == 'COUNT':
      cells = answer['cells']
      converted = sum(int(value.replace(',', '')) for value in cells)
      return converted

    else:
      return answer

def get_converted_answer(table, query):
    converted_answer = convert_answer(get_answer(table, query))
    return converted_answer


# Web Application

st.title("Sales Forecasting Dashboard")
st.write("📈 Welcome User, start using the application by uploading your file in the sidebar!")

if 'uploaded' not in st.session_state:
  st.session_state.uploaded = False

# Sidebar Menu
with st.sidebar:
    uploaded_file = st.file_uploader("Upload your Store Data here (must atleast contain Date and Sale)", type=["csv"])
    err = 0
    if uploaded_file is not None:
      if uploaded_file.type != 'text/csv':
            err = 1
            st.info('Please upload in CSV format only...')
      else: 
        st.success("File uploaded successfully!")
        df = pd.read_csv(uploaded_file, parse_dates=True)
        st.write("Your uploaded data:")
        st.write(df)

        # Data pre-processing
        df = drop(df)
        df = date_format(df)
        merge_sort(df)
        df = group_to_three(df)
        st.session_state.uploaded = True

    with open('sample.csv', 'rb') as f:
       st.download_button("Download our sample CSV", f, file_name='sample.csv')

if (st.session_state.uploaded):
  st.line_chart(df)

  forecast_button = st.button(
    'Start Forecasting',
    key='forecast_button',
    type="primary",
  )

  if (forecast_button):
    # Create the eXogenous values
    df['Sales First Difference'] = df['Sales'] - df['Sales'].shift(1)
    df['Seasonal First Difference'] = df['Sales'] - df['Sales'].shift(12)

    auto_train_test = train_test(df, 20)
    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_test_model = test_fitting(df, training_X, training_y)

    # Forecast (testing)
    n_periods = 20
    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
    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)

    # Forecast (actual)
    n_periods = 36
    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
    future_fitted_series = pd.Series(future_fitted)
    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)

    auto_sales_growth = sales_growth(df, future_fitted_series)
    st.write("Forecasted sales in the next 3 months")
    st.write(auto_sales_growth)