Gourmet Reference Manual

Under Construction

Introduction
Features
System Architecture
User Interface
Meal Planning
Recipe Management
Inventory Management
Nutrition Analysis
Shopping and Ordering
Special Diets and Restrictions
AI and Machine Learning
Data Sources and Integration
Privacy and Security
Customization and Extensibility
Troubleshooting
Future Work
Contributing
License

1. Introduction

Purpose

Gourmet is an AI-powered meal planning system designed to optimize nutrition, reduce costs, and simplify meal preparation for individuals and families. It aims to democratize access to advanced nutritional planning tools and promote healthier eating habits.

User Base

Individuals seeking to improve their diet
Families looking to streamline meal planning and preparation
People with specific dietary needs or restrictions
Budget-conscious consumers
Anyone interested in exploring new recipes and cuisines

Sample Use Cases

Generate a week's worth of balanced, budget-friendly meals
Plan meals around specific health goals (e.g., weight loss, muscle gain)
Accommodate multiple dietary restrictions within a household
Optimize grocery shopping to reduce food waste
Learn new cooking techniques and explore diverse cuisines

Status

Gourmet is actively developed as part of the Free Life Planner (FLP) project. While many core features are operational, development is ongoing to expand capabilities and improve user experience.

2. Features

Completed Features

✅ Comprehensive food databases integration (Food Data Central, Nutritionix, SOAR Recipe Archive)
✅ Barcode scanning for quick product information lookup
✅ Basic meal plan generation based on nutritional requirements
✅ PGourmet system
✅ Door sensor alerts on fridge and freezer for portion control
✅ Integration with WordNet and OpenCyc for food ontology

In Progress Features

[66%] PDDL-based meal planner
[50%] Gourmet-Formalog-Standalone system
[50%] Interactive cooking assistant
[40%] Seamless integration with FLP's generalized action planning systems
[40%] Self-discipline coach software
[33%] Food ontology development
[25%] Interactive cooking assistant
[20%] Logging and planning of macros
[20%] Follow-up dialog regarding intent when fridge and freezer accessed

Planned Features

Meal plan optimization for long-term health outcomes
Collaborative meal planning for households and groups
Integration with personal health data for tailored nutrition advice
Seasonal and local food prioritization
Freezer cooking support
Advanced food safety management
Emergency food and water provision management

3. System Architecture

Gourmet is built on a modular architecture that allows for easy expansion and integration with other systems. Key components include:

Core Planning Engine: Prolog-based PDDL planner for meal plan generation
Data Layer: Manages interactions with various food and recipe databases
User Interface: Web-based frontend for user interactions
AI Module: Handles machine learning tasks for personalization and recipe analysis
Integration Layer: Manages connections with external services (e.g., grocery stores, FLP modules)

Meal Planning Resources

Multiple meal planning and preparation systems
Gourmet-Formalog-Standalone (using OpenFoodTox, Basket)
PDDL-based meal planner
Interactive cooking assistant
PGourmet
Gourmet
st0opkid's MealSolver

4. User Interface

Web Interface

Responsive design for desktop and mobile use
Dashboard with meal plan overview, shopping lists, and quick actions
Recipe browser with filtering and search capabilities
Inventory management interface
Settings and preference configuration

Mobile App

🚧 Native mobile app for iOS and Android (in development)
Barcode scanning functionality
Quick meal logging and inventory updates
Shopping list management

Voice Assistant Integration

[50%] Basic Alexa skill and Rhasspy interface for querying meal plans and adding items to shopping list
[25%] Enhanced voice interactions for hands-free cooking assistance

5. Meal Planning

Plan Generation

[66%] PDDL-based meal planner for comprehensive meal contingent plan generation
[40%] Seamless integration with FLP's generalized action planning systems
[20%] Consideration of leftover ingredients and batch cooking opportunities

Customization

[50%] Fine-tuning of nutritional targets and preferences
Food style and preference learning
Estimating when users might tire of specific recipes or ingredients

Optimization

✅ Basic cost optimization
Freezer cooking support (reduces cost and prep time by ≥4X)
🚧 Advanced multi-objective optimization (nutrition, cost, preparation time, variety)

Nutrition Lookup

✅ Using FDC CSVs converted to Prolog KB (Food Data Central)
✅ Using Nutritionix (working but offline due to air-gapping development server)

6. Recipe Management

Recipe Database

✅ Integration of 300,000+ recipes (150,000 from SOAR Recipe Archive, additional 150,000 planned)
🚧 User ability to add custom recipes
[10%] Automatic nutritional analysis of custom recipes

Recipe Recommendations

🚧 Recommender system / collaborative filtering over recipes, ingredients, etc.

Recipe Parsing and Conversion

[50%] Automatic conversion of recipes to Behavior Trees (BTs)
- Using CURD (Carnegie Mellon University Recipe Database)
- Open-SESAME parser
Parser for converting natural language recipes into planning problems

7. Inventory Management

Pantry Tracking

✅ Manual inventory input
✅ Barcode scanning for quick item addition
🚧 Automatic inventory deduction based on meal plan usage

Expiration Management

[10%] Tracking of product expiration dates
[10%] Smart notifications for items nearing expiration
Planning tool to avoid food spoilage and expiration in various storage conditions

Fridge/Freezer Monitoring

✅ Temperature and door sensors for fridges and freezers
✅ Tracking pantry inventory changes
✅ Ensuring food safety
[20%] Follow-up dialog regarding intent when fridge and freezer accessed

Shopping List Generation

✅ Manual addition and removal of items
🚧 Automatic list creation based on meal plans and inventory

Receipt Tracking

[50%] Receipt tracker
[25%] Bill splitter for shared groceries

8. Nutrition Analysis

Nutritional Calculations

✅ Calculation of macronutrients (protein, carbs, fats) and calories
[20%] Logging and planning of macros
🚧 Tracking of essential vitamins and minerals
🚧 Consideration of bioavailability and nutrient interactions

Reporting and Visualization

✅ Basic nutritional overview of meal plans
[0%] Detailed nutritional reports and trend analysis
🚧 Visual representations of nutritional balance

User Modeling and Health Tracking

[40%] Self-discipline coach for hitting macros and understanding food psychology
Checking for common symptoms known to affect the user (e.g., GERD, lactose intolerance)
Inference of food sensitivities
Nutritional temporal records with privacy preservation
Integration with health temporal records for empirical analysis
Detection and allowance for genetic factors affecting diet and health

9. Shopping and Ordering

Grocery Store Integration

[10%] Integration with major online grocery retailers
🚧 Price comparison across multiple stores
🚧 Support for local and specialty stores

Order Optimization

🚧 Basic order consolidation to minimize delivery fees
🚧 Advanced order splitting for cost optimization across stores

Delivery Tracking

✅ Basic order status tracking
🚧 Integration with delivery services for real-time updates

10. Special Diets and Restrictions

Food Ontology

[33%] Comprehensive food ontology development
- Food properties encoded into Prolog factbase
- Medical properties such as allergies
- Food storage duration knowledge
- Food similarity using word embeddings
- Food substitutions (changing, adding, or canceling)
- Normalization / mapping NL descriptions to entities in a KB

Supported Diets

🚧 Vegetarian and vegan
🚧 Gluten-free
🚧 Ketogenic
🚧 Paleo
🚧 Low-FODMAP
Support for ethical diets (e.g., vegan, pescatarian)
Support for medical diets (e.g., diabetic, gall bladder issues)

Allergy Management

🚧 Common allergen avoidance (e.g., nuts, dairy, shellfish)
🚧 Customizable allergen profiles
🚧 Cross-contamination risk assessment

Medical Diet Support

[10%] Diabetic-friendly meal planning
[10%] Low-sodium diet planning
🚧 Renal diet support

11. AI and Machine Learning

Personalization

🚧 Basic preference learning from user interactions
🚧 Advanced taste profile modeling
🚧 Predictive modeling of user satisfaction

Natural Language Processing

[50%] Recipe parsing and normalization
[30%] Ingredient entity recognition and linking

Computer Vision

✅ Barcode recognition for product identification
🚧 Image-based food recognition and logging

12. Data Sources and Integration

Food Databases

✅ USDA Food Data Central
✅ Open Food Facts
✅ Nutritionix
🚧 Specialized databases for international cuisines

Recipe Sources

✅ SOAR Recipe Archive (150,000 recipes)
🚧 Integration with popular recipe websites
🚧 User-generated recipe repository

External Integrations

[0%] Fitness tracker data for personalized energy requirements
[0%] Weather data for seasonal recipe suggestions
Integration with Hetionet for empirical analysis of nutritional factors associated with medical conditions

13. Privacy and Security

Data Protection

🚧 Encryption of user data at rest and in transit
Privacy-preserving nutritional temporal records

User Controls

🚧 Granular privacy settings for data sharing
🚧 Data export and deletion capabilities

Compliance

🚧 GDPR compliance for EU users
🚧 CCPA compliance for California residents

14. Customization and Extensibility

API Access

🚧 RESTful API for third-party integrations
🚧 WebHooks for real-time data updates

Plugin System

🚧 Framework for community-developed plugins

15. Troubleshooting

Common Issues

Barcode scanning failures
Recipe import errors
Synchronization issues with external services

Logging and Diagnostics

✅ Detailed application logs
🚧 User-accessible diagnostic tools

Support Channels

✅ Community forums for peer support

16. Future Work

Advanced nutritional modeling for long-term health outcome prediction
Expansion of the food ontology to cover global cuisines and ingredients
Development of a companion mobile app for real-time meal tracking and adjustments
Implementation of a gamification system to encourage healthy eating habits
Semi-automatic Rapid Knowledge Formation (RKF) of textual knowledge bases regarding food safety
Integration with transportation and building hours APIs for complex food provision scenarios (e.g., homelessness, lack of transportation)
Automatic detection of inventory items which have been recalled or linked to disease outbreaks
Proper handling of food items lacking barcodes
Emergency food and water provision management
- Stocking enough provisions to survive n-months
- Rotating emergency food and water provisions
Food pantry patronage support

17. Contributing

Gourmet is an open-source project and welcomes contributions from the community. Ways to contribute include:

Code contributions (see CONTRIBUTING.md for guidelines)
Recipe submissions and curation
Translation and localization efforts
Documentation improvements
Bug reports and feature suggestions

18. License

Gourmet is released under the GNU General Public License v3.0. See the LICENSE file for full details.

This reference manual provides a comprehensive overview of the Gourmet system, its features, and ongoing development efforts. As the project evolves, this manual will be updated to reflect new capabilities and improvements.

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