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,    
                br.name AS brand_name,    
                p.image AS product_image,    
                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 brands AS br ON p.brand_id = br.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", "brand_name", "product_image", "product_variation", 
                "variation_name", "sub_sku", "customer", "contact_id", 
                "transaction_id", "invoice_no", "transaction_date", "sell_qty", 
                "unit", "unit_price_inc_tax", "unit_price_before_discount"
          ]
          df = pd.DataFrame(results, columns=columns)
          return df,"done"

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