created excel parser

excel_parser

@@ -0,0 +1,80 @@
+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