NutriNest

Features:

• Recipe Management:

  • Add, Edit, Delete, and View recipes.

  • Store recipe name, category, ingredients, servigns, and favorite status.

  • Automatic nutrition calculation when adding/editing recipes.

• Nutrition Database:

  • Separate Management for ingredients with their nutritional facts.

  • Add, Edit, Delete, and Search Nutrition Data.

• Search and Sort Capabilities:

  • Search recipes by name (supports partial or muliti-word matching)

  • Search recipes by ingredient

  • View recipes alphabetically

  • Filter recipes by category

• Favorites System:

  • Mark recipes as favorites and view them separately.

• File Handling:

  • Load and Save data using CSV files (recipe and nutrition csv files).

  • Option to use custom files or use defaults.

  • Automatic file creation with proper headers.

• Modular Architecture

Concepts Used:

• Modular Programming:

  • Multiple Python files with specific responsibilities.

• File Input/Output and CSV Handling:

  • Reading and writing structured data with csv.DictReader/Writer.

• Data Structures:

  • Lists of dictionaries to represent recipes and nutrition data.

• String Processing:

  • Parsing ingredients, searching with case-insensitve matching.

• Search Algorithms:

  • Multi-word partial matching for recipes and ingredients.

• Function Organization:

  • Clear separation between UI, logic, and data handling.

• Error Handling:

  • Input validation and graceful fallbacks.

• Code Reuseability:

  • Shared helper functions across modules.

How It Works:

1. Startup:

   • User choose whether to use default CSV files or provide their own.

2. Main Menu:

   • User navigates between:

     1. Recipe Management

     2. Nutrition Management

     3. Search and Sort

     4. Meal Planing Tools

     5. Favorites

     6. Save and Exit

3. Adding a Recipe:

   • Users enter details and the program automatically looks up each ingredient in the nutrition database and calculates totals.

4. Searching:

   • Supports flexible searching and finds relevate recipes.

5. Data Persistence

   • All changes are saved to the CSV files.

The program is designed with scalability in mind to easily add additional features.

What I Learned:

The project enhanced my skills in building a complete, multi-module Python application.

By structuring the program into separate handlers (fileHandler, recipeHandler, nutritionHandler, inputHandler), I learned the importance of modular design and separation of concerns, making the code much more maintainable and easier to debug.

I gained significant experience working with CSV files for data persistence, including proper header management and using dictionaries for structured data.  The automatic nutrition calculation feature taught me how to parse ingredient strings and match them against a database.

Additioanlly, implementing flexible search functionality (by name and ingredient) helped me udnerstand search algorithms and user-friendly matching techniques.  I also improved my ability to manage complex user input flows and menu systems.

Code: