Feature/meal plan api

.gitignore

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 *.tar.gz
 2025-T1/Food-Image-Classifier/scripts/data/food-101/images/
-2025-T2/Multi-Image-Classifier/scripts/VFN/Images/
+2025-T2/Multi-Image-Classifier/scripts/VFN/Images/.Rhistory
+.python-version
+.Rhistory

2025-T3/Meal Generator/README.md

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+# NutriHelp-AI – SIT374 Capstone Team Project (A)
+
+## Project Overview
+The team-based capstone project NutriHelp-AI was created as a component of SIT374's Capstone Team Project (A). The project's main goal is to develop an AI-assisted nutrition assistance system that offers context-aware and safety-conscious meal suggestions based on user preferences, dietary requirements, and fundamental health considerations.
+
+This repository provides artifacts demonstrating my individual contribution to the team effort.
+
+---
+
+## My Role & Contributions
+My contribution in this project was on AI logic and rule-based meal recommendation design. I helped build the meal generating logic, structured food data, safety considerations, and related documentation. My work ensured that meal recommendations were consistent, understandable, and in line with project requirements.
+
+---
+
+## Key Files & Folders
+
+### `meal_generator.py`
+Contains the main rule-based logic that generates meal recommendations. This file explains how to use conditional logic, restrictions, and context-aware decision-making in a practical system.
+
+### `meal_library.json`
+Stores structured meal data that is used in the recommendation logic. This file explains how to use JSON to organize and manage food-related data in an understandable, reusable way.
+
+### `Documentation & Reports/`
+Includes written documentation and reports about system logic, context-aware recommendations, and design explanations. These documents enable understanding, reflection, and handover.
+
+### `Testing & Result/`
+Contains testing outputs and result files used to validate the behavior of the meal recommendation logic. This category contains examples of basic system testing, assessment, and verification processes.
+
+---
+
+## Tools & Technologies
+- Python (rule-based AI logic)
+- JSON (structured meal and rule data)
+- Git & GitHub (version control and collaboration)
+
+---
+
+## Notes
+This project was created cooperatively as part of a team. Model training, deployment, and dependency management were handled at the system level, whereas my contributions concentrated on AI logic, data structure, documentation, and testing.
+
+---
+
+## Academic Context
+This work was created for **SIT374 - Capstone Team Project (A)** and is meant for academic assessment and portfolio submission.