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import pandas as pd
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import re
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from datetime import datetime
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import numpy as np
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import sqlite3
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import os
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import streamlit as st
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class SalesDataProcessor:
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def __init__(self, db):
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self.db = db
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self.setup_product_mapping()
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self.setup_location_mapping()
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self.setup_database_tables()
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def setup_database_tables(self):
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"""Initialize database tables if they don't exist"""
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conn = self.db.get_connection()
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cursor = conn.cursor()
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS sales (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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source_sheet TEXT,
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sr_no TEXT,
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customer_name TEXT,
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village TEXT,
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taluka TEXT,
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district TEXT,
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invoice_no TEXT UNIQUE,
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reference TEXT,
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dispatch_date TEXT,
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product_type TEXT,
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quantity INTEGER,
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rate_per_unit REAL,
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amount REAL,
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final_amount REAL,
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total_liters REAL,
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payment_date TEXT,
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gpay_amount REAL,
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cash_amount REAL,
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cheque_amount REAL,
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rrn_number TEXT,
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sold_by TEXT,
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sale_type TEXT,
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payment_status TEXT,
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payment_method TEXT,
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processed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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source_file TEXT
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)
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''')
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS customers (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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customer_name TEXT,
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village TEXT,
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taluka TEXT,
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district TEXT,
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total_purchases REAL DEFAULT 0,
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total_orders INTEGER DEFAULT 0,
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last_order_date TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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''')
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conn.commit()
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conn.close()
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def setup_product_mapping(self):
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"""Standard product mapping for all packaging types"""
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self.PRODUCT_MAPPING = {
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'1 LTR PLASTIC JAR': '1L_PLASTIC_JAR',
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'2 LTR PLASTIC JAR': '2L_PLASTIC_JAR',
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'5 LTR PLASTIC JAR': '5L_PLASTIC_JAR',
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'10 LTR PLASTIC JAR': '10L_PLASTIC_JAR',
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'5 LTR STEEL BARNI': '5L_STEEL_BARNI',
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'10 LTR STEEL BARNI': '10L_STEEL_BARNI',
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'20 LTR STEEL BARNI': '20L_STEEL_BARNI',
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'20 LTR PLASTIC CAN': '20L_PLASTIC_CAN',
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'1 LTR PET BOTTLE': '1L_PET_BOTTLE',
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'20 LTR CARBO': '20L_CARBO'
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}
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def setup_location_mapping(self):
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"""Gujarati location name standardization"""
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self.GUJARATI_LOCALITIES = {
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'રામપુરા': 'RAMPURA',
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'શેખડી': 'SHEKHADI',
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'સિંહોલ': 'SINHOL',
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'વનાદરા': 'VANADARA',
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'માવલી': 'MAVLI',
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'સિમરડા': 'SIMRADA',
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'બિલપડ': 'BILPAD',
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'વઘોડિયા': 'VAGHODIA',
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'સાકરિયા': 'SAKARIYA'
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}
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def safe_float(self, value):
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"""Safely convert to float, handle errors"""
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if pd.isna(value) or value in ['', 'NOT_AVAILABLE', None, '_']:
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return 0.0
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try:
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return float(value)
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except (ValueError, TypeError):
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return 0.0
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def safe_int(self, value):
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"""Safely convert to integer"""
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return int(self.safe_float(value))
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def parse_date(self, date_str):
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"""Handle all date formats intelligently"""
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if pd.isna(date_str) or date_str in ['', 'NOT_AVAILABLE', None, '_']:
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return 'NOT_AVAILABLE'
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if isinstance(date_str, (int, float)):
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try:
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return (datetime(1899, 12, 30) + pd.Timedelta(days=date_str)).strftime('%Y-%m-%d')
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except:
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return 'INVALID_DATE'
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date_str = str(date_str).strip()
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date_formats = [
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'%Y-%m-%d %H:%M:%S',
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'%d/%m/%Y',
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'%Y-%m-%d',
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'%d-%m-%Y',
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'%d/%m/%Y %H:%M:%S'
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]
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for fmt in date_formats:
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try:
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return datetime.strptime(date_str, fmt).strftime('%Y-%m-%d')
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except ValueError:
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continue
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return 'INVALID_DATE'
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def clean_name(self, name):
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"""Handle names, duplicates, variations"""
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if pd.isna(name) or name in ['', '-', '_', None]:
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return 'NOT_AVAILABLE'
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name = ' '.join(str(name).strip().split())
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return name
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def standardize_location(self, location):
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"""Handle Gujarati location names"""
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if pd.isna(location) or location in ['', 'NOT_AVAILABLE', None]:
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return 'NOT_AVAILABLE'
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location_str = str(location).strip()
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if isinstance(location_str, str):
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for guj_name, eng_name in self.GUJARATI_LOCALITIES.items():
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if guj_name in location_str:
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return eng_name
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return location_str.upper()
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def standardize_product(self, product_name):
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"""Convert any product name to standard format"""
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if pd.isna(product_name) or product_name in ['', 'NOT_AVAILABLE', None]:
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return 'UNKNOWN_PRODUCT'
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product_str = str(product_name).strip()
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product_upper = product_str.upper()
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for key, value in self.PRODUCT_MAPPING.items():
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if key in product_upper:
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return value
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if '1 LTR' in product_upper or '1L' in product_upper:
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if 'PLASTIC' in product_upper or 'JAR' in product_upper:
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return '1L_PLASTIC_JAR'
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elif 'PET' in product_upper or 'BOTTLE' in product_upper:
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return '1L_PET_BOTTLE'
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elif '2 LTR' in product_upper or '2L' in product_upper:
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return '2L_PLASTIC_JAR'
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elif '5 LTR' in product_upper or '5L' in product_upper:
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if 'STEEL' in product_upper or 'BARNI' in product_upper:
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return '5L_STEEL_BARNI'
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else:
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return '5L_PLASTIC_JAR'
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elif '10 LTR' in product_upper or '10L' in product_upper:
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if 'STEEL' in product_upper or 'BARNI' in product_upper:
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return '10L_STEEL_BARNI'
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else:
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return '10L_PLASTIC_JAR'
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elif '20 LTR' in product_upper or '20L' in product_upper:
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if 'STEEL' in product_upper or 'BARNI' in product_upper:
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return '20L_STEEL_BARNI'
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elif 'PLASTIC' in product_upper or 'CAN' in product_upper:
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return '20L_PLASTIC_CAN'
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elif 'CARBO' in product_upper:
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return '20L_CARBO'
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return f"UNKNOWN_{product_upper.replace(' ', '_')}"
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def detect_sale_type(self, row):
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"""Detect if it's demo sale (single unit) or bulk sale"""
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quantity = self.safe_int(row.get('QTN', 0))
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reference = str(row.get('REF.', '')).upper()
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if reference == 'DEMO' or quantity == 1:
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return 'DEMO_SALE'
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else:
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return 'BULK_SALE'
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def calculate_payment_status(self, row):
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"""Determine payment status intelligently"""
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final_amt = self.safe_float(row.get('FINAL AMT', 0))
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gpay = self.safe_float(row.get('G-PAY', 0))
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cash = self.safe_float(row.get('CASH', 0))
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cheque = self.safe_float(row.get('CHQ', 0))
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paid_amt = gpay + cash + cheque
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if paid_amt >= final_amt:
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return 'PAID'
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elif paid_amt > 0:
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return 'PARTIAL_PAID'
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elif self.parse_date(row.get('PAYMENT DATE')) not in ['NOT_AVAILABLE', 'INVALID_DATE']:
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return 'PENDING'
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else:
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return 'UNPAID'
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def detect_payment_method(self, row):
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"""Intelligently detect payment method"""
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gpay = self.safe_float(row.get('G-PAY', 0))
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cash = self.safe_float(row.get('CASH', 0))
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cheque = self.safe_float(row.get('CHQ', 0))
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if gpay > 0:
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return 'GPAY'
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elif cash > 0:
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return 'CASH'
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elif cheque > 0:
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return 'CHEQUE'
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else:
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return 'NOT_PAID'
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def process_dataframe(self, df, sheet_name, source_file):
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"""Process entire dataframe and standardize all records"""
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standardized_records = []
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for idx, row in df.iterrows():
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if (pd.isna(row.get('NAME', '')) and
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pd.isna(row.get('PACKING', '')) and
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pd.isna(row.get('INV NO', ''))):
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continue
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try:
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standardized_record = self.standardize_record(row, sheet_name, source_file)
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standardized_records.append(standardized_record)
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except Exception as e:
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st.error(f"⚠️ Error processing row {idx}: {e}")
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continue
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return standardized_records
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def standardize_record(self, row, sheet_name, source_file):
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"""Standardize a single record"""
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record = {
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'source_sheet': sheet_name,
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'sr_no': self.clean_name(row.get('SR NO.', 'NOT_AVAILABLE')),
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'customer_name': self.clean_name(row.get('NAME', 'NOT_AVAILABLE')),
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'village': self.standardize_location(row.get('VILLAGE', 'NOT_AVAILABLE')),
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'taluka': self.standardize_location(row.get('TALUKA', 'NOT_AVAILABLE')),
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'district': self.standardize_location(row.get('DISTRICT', 'NOT_AVAILABLE')),
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'invoice_no': self.clean_name(row.get('INV NO', 'NOT_AVAILABLE')),
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'reference': self.clean_name(row.get('REF.', 'NOT_AVAILABLE')),
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'dispatch_date': self.parse_date(row.get('DISPATCH DATE')),
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'product_type': self.standardize_product(row.get('PACKING', 'NOT_AVAILABLE')),
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'quantity': self.safe_int(row.get('QTN', 0)),
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'rate_per_unit': self.safe_float(row.get('RATE', 0)),
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'amount': self.safe_float(row.get('AMT', 0)),
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'final_amount': self.safe_float(row.get('FINAL AMT', 0)),
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'total_liters': self.safe_float(row.get('TOTAL LTR', 0)),
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'payment_date': self.parse_date(row.get('PAYMENT DATE')),
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'gpay_amount': self.safe_float(row.get('G-PAY', 0)),
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'cash_amount': self.safe_float(row.get('CASH', 0)),
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'cheque_amount': self.safe_float(row.get('CHQ', 0)),
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'rrn_number': self.clean_name(row.get('RRN', 'NOT_AVAILABLE')),
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'sold_by': self.clean_name(row.get('BY', 'NOT_AVAILABLE')),
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'sale_type': self.detect_sale_type(row),
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'payment_status': self.calculate_payment_status(row),
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'payment_method': self.detect_payment_method(row),
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'source_file': os.path.basename(source_file)
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}
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if record['amount'] == 0 and record['quantity'] > 0 and record['rate_per_unit'] > 0:
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record['amount'] = record['quantity'] * record['rate_per_unit']
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if record['final_amount'] == 0 and record['amount'] > 0:
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record['final_amount'] = record['amount']
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return record
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def insert_into_database(self, records):
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"""Insert processed records into database"""
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conn = self.db.get_connection()
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cursor = conn.cursor()
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inserted_count = 0
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updated_count = 0
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for record in records:
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try:
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cursor.execute('SELECT id FROM sales WHERE invoice_no = ?', (record['invoice_no'],))
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existing = cursor.fetchone()
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if existing:
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update_query = '''
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UPDATE sales SET
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source_sheet=?, sr_no=?, customer_name=?, village=?, taluka=?, district=?,
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reference=?, dispatch_date=?, product_type=?, quantity=?, rate_per_unit=?,
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amount=?, final_amount=?, total_liters=?, payment_date=?, gpay_amount=?,
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cash_amount=?, cheque_amount=?, rrn_number=?, sold_by=?, sale_type=?,
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payment_status=?, payment_method=?, source_file=?
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WHERE invoice_no=?
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'''
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cursor.execute(update_query, (
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record['source_sheet'], record['sr_no'], record['customer_name'],
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record['village'], record['taluka'], record['district'],
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record['reference'], record['dispatch_date'], record['product_type'],
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record['quantity'], record['rate_per_unit'], record['amount'],
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record['final_amount'], record['total_liters'], record['payment_date'],
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record['gpay_amount'], record['cash_amount'], record['cheque_amount'],
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record['rrn_number'], record['sold_by'], record['sale_type'],
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record['payment_status'], record['payment_method'], record['source_file'],
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record['invoice_no']
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))
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updated_count += 1
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else:
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insert_query = '''
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INSERT INTO sales (
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source_sheet, sr_no, customer_name, village, taluka, district,
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invoice_no, reference, dispatch_date, product_type, quantity,
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rate_per_unit, amount, final_amount, total_liters, payment_date,
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gpay_amount, cash_amount, cheque_amount, rrn_number, sold_by,
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sale_type, payment_status, payment_method, source_file
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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'''
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cursor.execute(insert_query, (
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record['source_sheet'], record['sr_no'], record['customer_name'],
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record['village'], record['taluka'], record['district'],
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record['invoice_no'], record['reference'], record['dispatch_date'],
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record['product_type'], record['quantity'], record['rate_per_unit'],
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record['amount'], record['final_amount'], record['total_liters'],
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record['payment_date'], record['gpay_amount'], record['cash_amount'],
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record['cheque_amount'], record['rrn_number'], record['sold_by'],
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record['sale_type'], record['payment_status'], record['payment_method'],
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record['source_file']
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))
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inserted_count += 1
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except Exception as e:
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st.error(f"❌ Database error for invoice {record['invoice_no']}: {e}")
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continue
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conn.commit()
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self.update_customers_table(conn)
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conn.close()
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return inserted_count, updated_count
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def update_customers_table(self, conn):
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"""Update customers table from sales data"""
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cursor = conn.cursor()
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cursor.execute('DELETE FROM customers')
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cursor.execute('''
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INSERT INTO customers (customer_name, village, taluka, district, total_purchases, total_orders, last_order_date)
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SELECT
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customer_name,
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village,
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taluka,
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district,
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SUM(final_amount) as total_purchases,
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COUNT(*) as total_orders,
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MAX(dispatch_date) as last_order_date
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FROM sales
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WHERE customer_name != 'NOT_AVAILABLE'
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GROUP BY customer_name, village, taluka, district
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''')
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conn.commit()
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def process_excel_file(self, file_path):
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"""Main method to process Excel file - called from Streamlit"""
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try:
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st.info(f"🔄 Processing: {os.path.basename(file_path)}")
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xl = pd.ExcelFile(file_path)
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all_records = []
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for sheet_name in xl.sheet_names:
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with st.spinner(f"Processing sheet: {sheet_name}..."):
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df = pd.read_excel(file_path, sheet_name=sheet_name)
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standardized_records = self.process_dataframe(df, sheet_name, file_path)
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all_records.extend(standardized_records)
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if not all_records:
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st.warning("⚠️ No valid records found in the file")
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return False
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with st.spinner("Inserting into database..."):
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inserted, updated = self.insert_into_database(all_records)
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if inserted > 0 or updated > 0:
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st.success(f"✅ Processed {len(all_records)} records from {os.path.basename(file_path)}")
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st.success(f"📊 New: {inserted}, Updated: {updated}")
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self.show_import_summary(all_records)
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return True
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else:
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st.warning("⚠️ No records were inserted or updated")
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return False
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except Exception as e:
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st.error(f"❌ Error processing file: {e}")
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return False
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def show_import_summary(self, records):
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"""Show summary of imported data"""
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if not records:
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return
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df = pd.DataFrame(records)
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Total Records", len(records))
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with col2:
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demo_sales = len(df[df['sale_type'] == 'DEMO_SALE'])
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st.metric("Demo Sales", demo_sales)
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with col3:
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bulk_sales = len(df[df['sale_type'] == 'BULK_SALE'])
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st.metric("Bulk Sales", bulk_sales)
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with col4:
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total_amount = df['final_amount'].sum()
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st.metric("Total Amount", f"₹{total_amount:,.2f}")
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st.subheader("📦 Products Imported")
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product_summary = df['product_type'].value_counts().head(5)
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for product, count in product_summary.items():
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st.write(f"- {product}: {count} records")
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def get_import_stats(self):
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"""Get import statistics for dashboard"""
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conn = self.db.get_connection()
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try:
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total_records = pd.read_sql('SELECT COUNT(*) as count FROM sales', conn)['count'].iloc[0]
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files_processed = pd.read_sql('SELECT COUNT(DISTINCT source_file) as count FROM sales', conn)['count'].iloc[0]
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recent_imports = pd.read_sql('''
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SELECT source_file, COUNT(*) as records, MAX(processed_at) as last_import
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FROM sales
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GROUP BY source_file
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ORDER BY last_import DESC
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LIMIT 5
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''', conn)
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return {
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'total_records': total_records,
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'files_processed': files_processed,
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'recent_imports': recent_imports.to_dict('records')
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}
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finally:
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conn.close() |
