Project Info

WorkFit

This project did not submit a demo video on Devpost.

Inspiration

WorkFit was inspired by the desire to help individuals with varying job types and lifestyles improve their activity levels. I believe that everyone deserves the opportunity to lead longer, more enjoyable, and productive lives. This project aims to tailor fitness solutions that fit seamlessly into users' schedules, considering their unique energy levels and work commitments. What I Learned Throughout this project, I learned about integrating user feedback into fitness applications and the importance of personalized approaches to health. I also gained hands-on experience with Fetch.ai agents, scheduling algorithms, and user interface design. How I Built the Project I developed WorkFit using HTML, CSS, and JavaScript within VS Code. I utilized Fetch.ai for implementing agents that help track user schedules and provide personalized workout recommendations based on energy levels and job types. The project involved defining clear user requirements and designing an intuitive interface for a better user experience. Challenges Faced One of the primary challenges was ensuring that the agent accurately assessed user energy levels and job demands. I had to experiment with different algorithms to optimize the scheduling process. Additionally, integrating the various components of the app and ensuring a seamless user experience required significant testing and iteration. Built With Languages: HTML, CSS, JavaScript Frameworks: Fetch.ai Tools: VS Code

WorkFit: Personalized Fitness Platform for Every Lifestyle

WorkFit is a health and fitness platform designed to create customized workout schedules that fit seamlessly into users' daily routines. Whether you're working a desk job or an active physical role, WorkFit tailors workout plans based on your job type, activity levels, and personal fitness goals.

The platform helps users stay active and healthy by providing personalized exercise routines that can be integrated into their busy schedules. It also uses advanced AI technologies, such as Fetch.AI’s uAgents, to optimize workout plans and track performance.

Key Features

Personalized Workout Schedule: Automatically adjusts workout plans to fit into your daily routine, based on your job type (sedentary or physical) and preferred time slots.
Tailored Exercises: Offers workouts suited to your lifestyle, whether you're looking to build strength, improve endurance, or maintain overall fitness.
AI-Powered Automation: Utilizes Fetch.AI’s uAgents to automatically optimize workout schedules and exercise routines for better performance.
Fitness Goal Tracking: Tracks progress toward your fitness goals, adjusting plans to ensure continuous improvement.

Future Goals

Location-Based Fitness: Integration with location services to suggest nearby parks and gyms for convenient exercise options.
Cost-Effective Plans: Personalization based on user budget, providing customized, affordable workout plans and resources.

Technology Stack

Backend: Node.js, Express
AI Integration: Fetch.AI uAgents
Frontend: React
Database: MongoDB
Deployment: Heroku

Installation & Setup

Clone the repository:
`git clone https://github.com/Zak-Dev21/Physical-Health-App
Navigate to the project directory:
cd Physical-Health-App
Install dependencies:
npm install
Start the development server:
npm start
Open your browser and go to:
http://localhost:3000

Contributing

We welcome contributions! If you'd like

Analysis

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Metric

Zak
1

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CSSIn code
HTMLIn code
JavaScriptIn code
PythonIn code

4 of 4 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

14 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

Feature verification

2 Verified2 Code-supported4 Claimed only

Free time slot schedulingVerified
Personalized workout schedule automatically adjusts to fit into your daily routine / preferred time slots
Claimed on readmehigh confidence
- schedule_agent.py:25 — find_free_slots() computes gaps between existing tasks within an 08:00-22:00 day window
- api_server.py:27 — /api/schedule endpoint calls add_schedule then find_free_slots and returns free_slots to caller
Personalized workout suggestions based on job type, activity level, goalsVerified
Automatically adjusts workout plans and offers tailored exercises based on job type (sedentary/physical), activity levels, and goals
Claimed on readmehigh confidence
- script.js:44 — generateWorkoutSuggestions() branches on activityLevel and goals to build a suggestion list, wired to form submit handler
- script.js:68 — generateTimingSuggestions() produces timing text based on activityLevel and goals
Energy-level based workout timingCode-supported
Adapts workout schedules based on your energy levels; agent accurately assesses user energy levels and job demands
Claimed on Devpostlow confidence
- workoutiming_agent.py:8 — suggest_best_time() branches on energy_level (high/moderate/low) to pick a slot
- api_server.py:45 — energy_level is hardcoded to "moderate" with a comment 'This could be dynamic based on more user info', so no real energy assessment feeds this logic
Fetch.ai uAgents for scheduling/timing automationCode-supported
Utilizes Fetch.AI's uAgents to automatically optimize workout schedules and exercise routines; agents track user schedules and provide recommendations
Claimed on readmemedium confidence
- schedule_agent.py:4 — SchedulerAgent subclasses uagents.Agent
- lifestyle_agent.py:3 — LifestyleAgent subclasses uagents.Agent to store user data
- workoutiming_agent.py:4 — TimingAgent subclasses uagents.Agent with suggest_best_time logic
- api_server.py:11 — Agents are instantiated and their methods called directly in-process via FastAPI routes rather than through actual uAgents messaging/protocols between independent agents
Cost-effective / budget-based personalizationClaimed only
Personalization based on user budget, providing customized, affordable workout plans and resources
Claimed on readmehigh confidence
Fitness goal tracking / progress trackingClaimed only
Tracks progress toward your fitness goals, adjusting plans to ensure continuous improvement
Claimed on readmehigh confidence
Location-based fitness (nearby parks/gyms)Claimed only
Integration with location services to suggest nearby parks and gyms
Claimed on readmehigh confidence
Node.js/Express/React/MongoDB/Heroku stackClaimed only
Technology stack listed as Backend Node.js/Express, Frontend React, Database MongoDB, Deployment Heroku
Claimed on readmehigh confidence

An AI agent derived these features from the project’s Devpost page and readme, then searched the code for each one. Verified features are backed by cited code; claimed-only features had no supporting code, which is not by itself proof a feature is missing.