data_collector.py

import mysql.connector
from decimal import Decimal
import pandas as pd
from prophet import Prophet
import math
# Define the connection parameters
host = "159.138.104.192"
user = "storemate_ml"
password = "bTgZd77VpD^o4Ai6Dw9xs9"
database = "lite_version"



def forecast(monthly_sales):
  # Prepare the data for Prophet
  monthly_sales.rename(columns={'transaction_date': 'ds', 'sell_qty': 'y'}, inplace=True)

  # Initialize and fit the Prophet model
  model = Prophet()
  model.fit(monthly_sales)

  # Make a future dataframe for the next month
  future = model.make_future_dataframe(periods=1, freq='M')
  forecast = model.predict(future)

  # Extract the forecasted sales for the next month
  forecasted_sales = forecast[['ds', 'yhat']].tail(2)

  # Combine historical and forecasted data
  combined_sales = pd.concat([monthly_sales, forecasted_sales[-1:]], ignore_index=True)
  original_forecasted_value = combined_sales.tail(1)
  rounded_value = combined_sales.tail(1)

  rounded_value['yhat'] = rounded_value['yhat'].apply(lambda x: max(0, math.ceil(x)))

  return combined_sales,original_forecasted_value,rounded_value

def get_data(b_id,product_name):
  # Create a connection to the MySQL server
  try:
      # Create a connection to the MySQL server
      connection = mysql.connector.connect(
          host=host,
          user=user,
          password=password,
          database=database
      )

      if connection.is_connected():
          print("Connected to MySQL database")

          # Create a cursor object for executing SQL queries
          cursor = connection.cursor()

          # Define the SQL SELECT query
          sql_query = f"""
  SELECT
    `b`.`id` AS `business_id`,
    `b`.`name` AS `business_name`,
    `p`.`name` AS `product_name`,
    `p`.`type` AS `product_type`,
    `c1`.`name` AS `category_name`,
    `pv`.`name` AS `product_variation`,
    `v`.`name` AS `variation_name`,
    `v`.`sub_sku`,
    `c`.`name` AS `customer`,
    `c`.`contact_id`,
    `t`.`id` AS `transaction_id`,
    `t`.`invoice_no`,
    `t`.`transaction_date` AS `transaction_date`,
    (transaction_sell_lines.quantity - transaction_sell_lines.quantity_returned) AS sell_qty,
    `u`.`short_name` AS `unit`,
    transaction_sell_lines.unit_price_inc_tax,
    transaction_sell_lines.unit_price_before_discount
  FROM `transaction_sell_lines`
    INNER JOIN `transactions` AS `t`
      ON `transaction_sell_lines`.`transaction_id` = `t`.`id`
    INNER JOIN `variations` AS `v`
      ON `transaction_sell_lines`.`variation_id` = `v`.`id`
    LEFT JOIN `transaction_sell_lines_purchase_lines` AS `tspl`
      ON `transaction_sell_lines`.`id` = `tspl`.`sell_line_id`
    LEFT JOIN `purchase_lines` AS `pl`
      ON `tspl`.`purchase_line_id` = `pl`.`id`
    INNER JOIN `product_variations` AS `pv`
      ON `v`.`product_variation_id` = `pv`.`id`
    INNER JOIN `contacts` AS `c`
      ON `t`.`contact_id` = `c`.`id`
    INNER JOIN `products` AS `p`
      ON `pv`.`product_id` = `p`.`id`
    LEFT JOIN `business` AS `b`
      ON `p`.`business_id` = `b`.`id`
    LEFT JOIN `categories` AS `c1`
      ON `p`.`category_id` = `c1`.`id`
    LEFT JOIN `tax_rates`
      ON `transaction_sell_lines`.`tax_id` = `tax_rates`.`id`
    LEFT JOIN `units` AS `u`
      ON `p`.`unit_id` = `u`.`id`
    LEFT JOIN `transaction_payments` AS `tp`
      ON `tp`.`transaction_id` = `t`.`id`
    LEFT JOIN `transaction_sell_lines` AS `tsl`
      ON `transaction_sell_lines`.`parent_sell_line_id` = `tsl`.`id`
  WHERE `t`.`type` = 'sell'
  AND `t`.`status` = 'final'
  AND `t`.`business_id` = {b_id}
  GROUP BY `b`.`id`,
          `transaction_sell_lines`.`id`;
  """

          # Execute the SQL query
          cursor.execute(sql_query)

          # Fetch all the rows as a list of tuples
          results = cursor.fetchall()
          results = [tuple(
              float(val) if isinstance(val, Decimal) else val for val in row
          ) for row in results]

          #print(results)

          # Display the results
          #for row in results:
              #print(row)  # You can process the results as needed

          # Close the cursor and connection
          cursor.close()
          connection.close()

          # Create a DataFrame
          columns = ["business_id","business_name","product_name","product_type","category_name","product_variation","variation_name","sub_sku","customer","contact_id","transaction_id","invoice_no","transaction_date","sell_qty","unit","cost_price","selling_price"]
          df = pd.DataFrame(results, columns=columns)
          return df,"done"

  except mysql.connector.Error as e:
      return e,"error"