| import re |
|
|
|
|
| CATEGORY_RULES = [ |
| { |
| "key": "income", |
| "name": "Income", |
| "patterns": [ |
| "salary", |
| "payroll", |
| "paycheck", |
| "credited", |
| "deposit", |
| "interest", |
| "refund", |
| ], |
| "positive_only": True, |
| }, |
| { |
| "key": "dining", |
| "name": "Food & Dining", |
| "patterns": [ |
| "zomato", |
| "swiggy", |
| "mcdonald", |
| "domino", |
| "pizza", |
| "restaurant", |
| "cafe", |
| "burger", |
| "kfc", |
| "subway", |
| "dining", |
| ], |
| }, |
| { |
| "key": "coffee", |
| "name": "Coffee & Cafes", |
| "patterns": ["starbucks", "coffee", "ccd", "barista"], |
| }, |
| { |
| "key": "groceries", |
| "name": "Groceries", |
| "patterns": [ |
| "grocery", |
| "groceries", |
| "dmart", |
| "bigbasket", |
| "blinkit", |
| "zepto", |
| "walmart", |
| "target", |
| "whole foods", |
| "supermarket", |
| ], |
| }, |
| { |
| "key": "transportation", |
| "name": "Transportation", |
| "patterns": [ |
| "uber", |
| "ola", |
| "rapido", |
| "metro", |
| "fuel", |
| "petrol", |
| "gas", |
| "shell", |
| "parking", |
| "toll", |
| ], |
| }, |
| { |
| "key": "entertainment", |
| "name": "Entertainment", |
| "patterns": [ |
| "netflix", |
| "spotify", |
| "prime", |
| "hotstar", |
| "bookmyshow", |
| "cinema", |
| "movie", |
| "youtube", |
| ], |
| }, |
| { |
| "key": "shopping", |
| "name": "Shopping", |
| "patterns": [ |
| "amazon", |
| "flipkart", |
| "myntra", |
| "ajio", |
| "nykaa", |
| "shopping", |
| "store", |
| "mall", |
| ], |
| }, |
| { |
| "key": "healthcare", |
| "name": "Healthcare", |
| "patterns": [ |
| "pharmacy", |
| "medical", |
| "hospital", |
| "clinic", |
| "doctor", |
| "apollo", |
| "1mg", |
| "pharmeasy", |
| ], |
| }, |
| { |
| "key": "education", |
| "name": "Education", |
| "patterns": [ |
| "school", |
| "college", |
| "tuition", |
| "course", |
| "udemy", |
| "coursera", |
| "book", |
| "exam", |
| ], |
| }, |
| { |
| "key": "housing", |
| "name": "Housing", |
| "patterns": [ |
| "rent", |
| "maintenance", |
| "electricity", |
| "water bill", |
| "gas bill", |
| "mortgage", |
| "society", |
| ], |
| }, |
| ] |
|
|
|
|
| FALLBACK_CATEGORY = {"key": "miscellaneous", "name": "Miscellaneous"} |
|
|
|
|
| def normalize_text(*values): |
| text = " ".join(str(value or "") for value in values).lower() |
| return re.sub(r"\s+", " ", text).strip() |
|
|
|
|
| def categorize_transaction(merchant, note="", amount=None): |
| text = normalize_text(merchant, note) |
| numeric_amount = None |
|
|
| try: |
| numeric_amount = float(amount) if amount is not None else None |
| except (TypeError, ValueError): |
| numeric_amount = None |
|
|
| for rule in CATEGORY_RULES: |
| if rule.get("positive_only") and numeric_amount is not None and numeric_amount <= 0: |
| continue |
|
|
| if any(pattern in text for pattern in rule["patterns"]): |
| return { |
| "category": rule["name"], |
| "category_key": rule["key"], |
| "confidence": "high", |
| "matched_rule": rule["key"], |
| } |
|
|
| if numeric_amount is not None and numeric_amount > 0: |
| return { |
| "category": "Income", |
| "category_key": "income", |
| "confidence": "medium", |
| "matched_rule": "positive_amount", |
| } |
|
|
| return { |
| "category": FALLBACK_CATEGORY["name"], |
| "category_key": FALLBACK_CATEGORY["key"], |
| "confidence": "low", |
| "matched_rule": None, |
| } |
|
|
|
|
| def should_auto_categorize(category=None, category_key=None): |
| generic_values = {"", None, "other", "misc", "miscellaneous", "uncategorized"} |
| normalized_category = category.lower() if isinstance(category, str) else category |
| normalized_key = category_key.lower() if isinstance(category_key, str) else category_key |
| return normalized_key in generic_values or normalized_category in generic_values |
|
|
