import pandas as pd
import openpyxl
from typing import Dict, List, Any

class ExcelParser:
    def __init__(self):
        pass
    
    def read_excel_file(self, file_path: str, sheet_name: str = None) -> pd.DataFrame:
        """Read Excel file and return DataFrame"""
        try:
            if sheet_name:
                df = pd.read_excel(file_path, sheet_name=sheet_name)
            else:
                df = pd.read_excel(file_path)
            return df
        except Exception as e:
            print(f"Error reading Excel file: {e}")
            return None
    
    def get_sheet_names(self, file_path: str) -> List[str]:
        """Get all sheet names from Excel file"""
        try:
            wb = openpyxl.load_workbook(file_path)
            return wb.sheetnames
        except Exception as e:
            print(f"Error getting sheet names: {e}")
            return []
    
    def analyze_sales_data(self, file_path: str) -> Dict[str, Any]:
        """Analyze sales data from Excel file"""
        df = self.read_excel_file(file_path)
        if df is None:
            return {}
        
        results = {}
        
        # Look for common column patterns
        food_keywords = ['food', 'burger', 'sandwich', 'fries', 'pizza', 'chicken']
        drink_keywords = ['drink', 'soda', 'coffee', 'juice', 'water', 'tea']
        
        # Try to identify food vs drink items
        if 'category' in df.columns.str.lower():
            category_col = [col for col in df.columns if 'category' in col.lower()][0]
            food_items = df[~df[category_col].str.lower().str.contains('|'.join(drink_keywords), na=False)]
        else:
            # Try to identify by item name
            item_col = [col for col in df.columns if any(word in col.lower() for word in ['item', 'product', 'name'])][0]
            food_items = df[~df[item_col].str.lower().str.contains('|'.join(drink_keywords), na=False)]
        
        # Find sales/price column
        sales_cols = [col for col in df.columns if any(word in col.lower() for word in ['sales', 'price', 'total', 'amount'])]
        
        if sales_cols:
            sales_col = sales_cols[0]
            total_food_sales = food_items[sales_col].sum()
            results['total_food_sales'] = f"${total_food_sales:,.2f}"
        
        return results
    
    def calculate_totals(self, df: pd.DataFrame, column: str) -> float:
        """Calculate total for a specific column"""
        try:
            return df[column].sum()
        except Exception as e:
            print(f"Error calculating totals: {e}")
            return 0.0
    
    def filter_data(self, df: pd.DataFrame, filters: Dict[str, Any]) -> pd.DataFrame:
        """Filter DataFrame based on criteria"""
        filtered_df = df.copy()
        
        for column, value in filters.items():
            if column in filtered_df.columns:
                if isinstance(value, list):
                    filtered_df = filtered_df[filtered_df[column].isin(value)]
                else:
                    filtered_df = filtered_df[filtered_df[column] == value]
        
        return filtered_df