
import unittest
import user_code
import ast
import re   
import unittest
import os
import pandas as pd
import importlib
import user_code

class TestTask(unittest.TestCase):
    def setUp(self):
        # Create a temporary CSV with sample data
        self.input_csv = 'test_expenses_raw.csv'
        self.output_csv = 'test_expense_summary.csv'
        data = [
            {'Amount': 25.00, 'Description': 'Uber ride downtown'},
            {'Amount': 40.00, 'Description': 'Office stationery purchase'},
            {'Amount': 15.50, 'Description': 'Lunch at local cafe'},
            {'Amount': 120.00, 'Description': 'Hotel stay'},
            {'Amount': 10.00, 'Description': 'Monthly software subscription'},
            {'Amount': 30.00, 'Description': 'Electric utilities payment'},
            {'Amount': 17.00, 'Description': 'Fuel for company car'},
            {'Amount': 9.99, 'Description': 'Water utilities'},
            {'Amount': 50.00, 'Description': 'Flight to conference'},
            {'Amount': 20.00, 'Description': 'Dining with client'},
            {'Amount': None, 'Description': 'Taxi to airport'},  # Should be excluded
            {'Amount': 5.00, 'Description': None},               # Should be excluded
            {'Amount': 8.00, 'Description': 'Book purchase'}     # Should be "Other"
        ]
        df = pd.DataFrame(data)
        df.to_csv(self.input_csv, index=False)

    def tearDown(self):
        if os.path.exists(self.input_csv):
            os.remove(self.input_csv)
        if os.path.exists(self.output_csv):
            os.remove(self.output_csv)

    def test_categorize_expense_keywords(self):
        # Check that categorize_expense returns correct category for various descriptions
        func = getattr(user_code, 'categorize_expense', None)
        self.assertIsNotNone(func, "categorize_expense function is missing")
        test_cases = [
            ("Uber ride downtown", "Travel"),
            ("Office stationery purchase", "Office Supplies"),
            ("Lunch at local cafe", "Meals & Entertainment"),
            ("Hotel stay", "Lodging"),
            ("Flight to conference", "Travel"),
            ("Monthly software subscription", "Software & Subscriptions"),
            ("Electric utilities payment", "Utilities"),
            ("Fuel for company car", "Automobile"),
            ("Water utilities", "Utilities"),
            ("Dining with client", "Meals & Entertainment"),
            ("Book purchase", "Other"),
        ]
        for desc, expected in test_cases:
            result = func(desc)
            _dynamic_test(
                self,
                result == expected,
                f"Description '{desc}' correctly categorized as '{expected}'",
                f"Description '{desc}' incorrectly categorized as '{result}', expected '{expected}'"
            )

    def test_dropna_excludes_incomplete_rows(self):
        # Run the main function and check that rows with missing Amount or Description are excluded
        importlib.reload(user_code)
        summary = user_code.automate_expense_categorization(self.input_csv, self.output_csv)
        df = pd.read_csv(self.input_csv)
        filtered = df.dropna(subset=["Amount", "Description"])
        # There are 13 rows, but 2 have missing data, so should be 11 processed
        _dynamic_test(
            self,
            len(filtered) == 11,
            "Rows with both Amount and Description present are processed",
            f"Expected 11 rows after dropna, got {len(filtered)}"
        )

    def test_aggregation_totals_by_category(self):
        importlib.reload(user_code)
        summary = user_code.automate_expense_categorization(self.input_csv, self.output_csv)
        # Manually build expected sums
        expected = {
            "Travel": 25.00 + 50.00,
            "Office Supplies": 40.00,
            "Meals & Entertainment": 15.50 + 20.00,
            "Lodging": 120.00,
            "Software & Subscriptions": 10.00,
            "Utilities": 30.00 + 9.99,
            "Automobile": 17.00,
            "Other": 8.00,
        }
        # Convert summary to dict
        actual = dict(zip(summary['Category'], summary['Amount']))
        for cat, amt in expected.items():
            _dynamic_test(
                self,
                cat in actual and abs(actual[cat] - amt) < 1e-2,
                f"Category '{cat}' has correct total {amt}",
                f"Category '{cat}' has incorrect total {actual.get(cat, None)}, expected {amt}"
            )

    def test_summary_csv_exported(self):
        importlib.reload(user_code)
        user_code.automate_expense_categorization(self.input_csv, self.output_csv)
        # Check that output file exists and has correct columns
        exists = os.path.exists(self.output_csv)
        _dynamic_test(
            self,
            exists,
            "Summary CSV file is created",
            "Summary CSV file was not created"
        )
        if exists:
            df = pd.read_csv(self.output_csv)
            _dynamic_test(
                self,
                set(df.columns) == {"Category", "Amount"},
                "Summary CSV file has correct columns",
                f"Summary CSV columns incorrect: {df.columns}"
            )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

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Challenge: Automate Expense Categorization

Solução

Challenge: Automate Expense Categorization

Solução