Meal Prep Planner for Schools

Table of Contents

• Context and Problem
  • Key Operational Requirements
• Value Proposition
• Objective
• Key Features
• ETL Pipeline
  • Usage
  • Datasets
  • Frontend and Dashboard
• Future Roadmap & Expansion
• Author, Affiliation and Contact
• References

Context and Problem

Between 2020 and 2024, the combined prevalence of overweight and obesity in Mexico reached 36.6% in school-age children and 40.1% in adolescents. These figures indicate a significant deterioration in the health and nutritional status of minors, driven largely by the recurrent consumption of ultra-processed products and sugary drinks, particularly in urban environments (Gaona-Pineda et al., 2025).

Data shows that the caloric contribution of ultra-processed products in daily diets ranges from 16.8% in adults to 26.4% in school-aged children, making this a critical public health priority (Gaona-Pineda et al., 2025).

In response, the Mexican government has implemented public policies such as the "Sistema de Etiquetado Frontal de Advertencia Mexicano" (NOM-051) to increase consumer awareness regarding high-calorie density and harmful additives (Secretaría de Economía, Secretaría de Salud, 2020).

Key Operational Requirements

Following the guidelines of "La Escuela es Nuestra" (Programas para el Bienestar, 2026), this system is designed to meet the following standards:

• Balanced Nutrition: Providing food and beverages that foster a varied and healthy diet.
• Economic Accessibility: Maintaining a suggested cost of up to $15.00 MXN per student.
• Cyclic Menu Planning: Designing monthly menu cycles that satisfy specific nutritional requirements through recipe diversity.
• Strategic Logistics: Planning ingredient acquisition and storage management to guarantee safety and optimal usage conditions.

Value Proposition

While commercial platforms exist, they often lack full nutritional metadata or hide planning tools behind premium paywalls. This project bridges that gap by providing:

• Data-Driven Menu Planning: Consolidating structured and unstructured data (instructions, nutritional facts, and costs) to design menus based on specific nutritional targets.
• Inventory Generation: Automatically calculating required supplies based on student enrollment.
• Nutritional Analytics: Processing structured and unstructured data to provide insights into caloric and bromatological contributions.

Objective

Develop a digital repository of nutritionally balanced recipes that streamlines weekly menu planning in compliance with the operational guidelines of the "La Escuela es Nuestra" food service program (Programas para el Bienestar, 2026).

Key Features

• Implement Robust ETL Pipelines: Design and deploy automated data flows for the extraction, transformation, and loading (ETL) of nutritional metadata and market costs from heterogeneous web sources.
• Engineered Knowledge Ingestion: Build a specialized pipeline for the ingestion and processing of technical literature and regulatory standards regarding food safety and nutritional requirements.
• Develop Multi-Objective Optimization Models: Deploy an AI-driven algorithm designed to balance high nutritional density with cost-efficiency, providing intelligent meal recommendations.
• Design Real-Time UI/UX Interfaces: Develop an intuitive and accessible dashboard that provides synchronous feedback on nutritional goal attainment and per-student budget constraints.
• Automate Supply Chain Management: Integrate a logistics module for the automated generation of ingredient lists and inventory requirements based on planned menu cycles.
• Establish Foundation for Autonomous Agents: Integrate technical documentation and embeddings to facilitate future development of a RAG-based autonomous assistant for intelligent menu generation.

ETL Pipeline

For a detailed technical overview of the recipe, ingredient and technical documents data ingestion flows, please refer to the ETL Pipeline documentation.

Usage

Follow these steps to properly use the pipeline:

1. Configure Credentials: Create a Kiwilimón account. Copy the example credential file and update it with your username and password:

cp ./ETL_Pipeline/CREDENTIAL_EXAMPLE.json ./ETL_Pipeline/CREDENTIAL.json

2. Install Dependencies: It is recommended to use a Python virtual environment. Install the required libraries with:

pip install -r ./ETL_Pipeline/requirements.txt

3. Environment Variables: For production or custom deployments, configure your environment variables by copying the example file:

cp .env.example .env

4. Start Services: Launch the Docker services for the database:

docker compose up -d

5. Run the Pipeline: Execute the ETL process with the following command:

python -m ETL_Pipeline

6. Run the Frontend: Execute the Streamlit script to show the planner:

streamlit run Frontend

Note: Ensure the environment variables are exported to your system so the ETL script can access them during execution.

Datasets

Upon successful execution of the pipeline, all processed data (available in CSV, JSON, PDF, and Markdown formats) is organized and stored in the Datasets folder. Additionally, this directory includes specific CSV files designed for database reconstruction, facilitating seamless migrations to other systems or a complete database reset if required.

Frontend and Dashboard

For a detailed technical overview of the frontend and dashboard, please refer to the Frontend documentation.

Future Roadmap & Expansion

This project is designed for continuous evolution, with several key expansion axes to enhance systemic quality:

• Database Scaling: Increasing the repository's volume and quality by integrating diverse international recipes and real-time data sources for market costs and nutritional metrics.
• End-to-End Automation: Transitioning into a comprehensive service model to simplify planning through intelligent processes. This reduces operational overhead by automating supplier prospecting and recipe selection.
• Agentic AI Integration: Implementing autonomous agents to manage the entire planning lifecycle-optimizing for nutritional requirements, remaining inventory, and automated procurement without human intervention.

Author, Affiliation and Contact

Alexis Aguilar [Student of Bachelor's Degree in "Tecnologías para la Información en Ciencias" at Universidad Nacional Autónoma de México UNAM]: alexis.uaguilaru@gmail.com

Project developed for the subject "Digital Repositories" taught in semestre 2026-2 and it is not affiliated to a political party.

References

• Gaona-Pineda, E. B., Arango-Angarita, A., Valenzuela-Bravo, D. G., Medina-Zacarías, M. C., Martinez-Tapia, B., Rodríguez-Ramírez, S., & Hernández-Carapia, N. (2025). Contribución energética de alimentos mínimamente procesados, ultraprocesados y factores sociodemográficos asociados. Salud Pública de México, 67 (6 (nov-dic)), 587-597. https://doi.org/10.21149/16998
• Secretaría de Economía, Secretaría de Salud. (2020, 24 de enero). NOM-051-SCFI/SSA1-2010, Especificaciones generales de etiquetado para alimentos y bebidas no alcohólicas preenvasados. https://www.dof.gob.mx/normasOficiales/8150/seeco11_C/seeco11_C.html
• Programas para el Bienestar. (2026, ene.). Guías LEEN 2026. https://laescuelaesnuestra.sep.gob.mx/