CLI To-Do Application

Project Structure

Organize your code into the following structure:

todo-app/
├── todo.py              # Main entry point
├── task.py              # Task class definition
├── task_manager.py      # TaskManager class for CRUD operations
├── storage.py           # JSON file handling
├── analytics.py         # Statistics and reporting
├── utils.py             # Helper functions (validation, formatting)
├── data/
│   ├── tasks.json       # User tasks storage (auto-created)
│   ├── sample_tasks.csv # Sample data for import testing
│   └── productivity_data.csv
├── tests/
│   ├── test_task.py
│   └── test_manager.py
├── requirements.txt     # Dependencies (if any)
└── README.md            # Project documentation

Task Class (OOP)

Create a Task class with the following:

• Attributes: id, title, description, category, priority, status, due_date, created_date, completed_date
• Methods: mark_complete(), mark_pending(), to_dict(), from_dict()
• Properties: is_overdue (computed), days_until_due (computed)
• Validation: Validate category and priority values on assignment
• Representation: Implement __str__ and __repr__ methods

class Task:
    CATEGORIES = ['work', 'personal', 'study', 'health']
    PRIORITIES = ['high', 'medium', 'low']
    
    def __init__(self, title, category='personal', priority='medium', 
                 due_date=None, description=''):
        self.id = None  # Set by TaskManager
        self.title = title
        self.category = category
        self.priority = priority
        self.status = 'pending'
        self.due_date = due_date
        self.created_date = datetime.now().isoformat()
        self.completed_date = None
        self.description = description
    
    @property
    def is_overdue(self):
        # Return True if past due date and not completed
        pass
    
    def mark_complete(self):
        self.status = 'completed'
        self.completed_date = datetime.now().isoformat()

TaskManager Class

Implement CRUD operations:

• add_task(task): Add new task, assign ID, save to storage
• get_task(id): Retrieve task by ID
• get_all_tasks(): Return all tasks
• update_task(id, **kwargs): Update task attributes
• delete_task(id): Remove task from storage
• filter_tasks(category=None, priority=None, status=None): Filter tasks
• search_tasks(query): Search by title or description

Storage Module

Handle JSON file operations:

• load_tasks(filepath): Load tasks from JSON file
• save_tasks(tasks, filepath): Save tasks to JSON file
• import_csv(filepath): Import tasks from CSV file
• export_csv(tasks, filepath): Export tasks to CSV
• backup_data(): Create timestamped backup

import json
import csv
from datetime import datetime

def load_tasks(filepath='data/tasks.json'):
    """Load tasks from JSON file. Create if not exists."""
    try:
        with open(filepath, 'r') as f:
            data = json.load(f)
            return data.get('tasks', [])
    except FileNotFoundError:
        return []

def save_tasks(tasks, filepath='data/tasks.json'):
    """Save tasks to JSON file with metadata."""
    data = {
        'tasks': [task.to_dict() for task in tasks],
        'metadata': {
            'total_tasks': len(tasks),
            'last_updated': datetime.now().isoformat()
        }
    }
    with open(filepath, 'w') as f:
        json.dump(data, f, indent=2)

Analytics Module

Implement productivity statistics:

• get_completion_rate(): Percentage of completed tasks
• get_category_breakdown(): Tasks per category
• get_priority_breakdown(): Tasks per priority level
• get_overdue_tasks(): List of overdue tasks
• get_productivity_summary(): Overall statistics
• analyze_csv_data(filepath): Analyze imported dataset

CLI Interface

Interactive menu-driven interface:

• Main menu with numbered options
• Clear screen and header for each view
• Input validation with helpful error messages
• Confirmation prompts for destructive actions
• Color-coded output (optional but recommended)
• Graceful exit with save confirmation