Personalized Fitness App

This is a full-stack web application that generates personalized fitness plans for users based on their age, goals, and experience level. The backend is built with Flask and the frontend is a React application.

Application Architecture

This diagram illustrates the high-level architecture of the fitness application, showing the interaction between the frontend, backend services, AI/LLM components, and the database.

Features

• User profile creation and updates

• Dynamic fitness options based on user's age

• Personalized workout plan generation

• Interactive dashboard to view workout plans

• AI-Powered Fitness Options: The Fitness Options API leverages an intelligent agent to dynamically generate personalized fitness options (goals, equipment, workout types, experience levels) based on the user's age and previous selections. This ensures a highly customized onboarding experience.

• Dynamic Workout Generation: Once a user's profile is set up, the Workout Generation API utilizes another intelligent agent to create a tailored 3-week workout plan. This plan is dynamically generated based on the user's specific fitness goals, available equipment, preferred workout types, and experience level, providing a truly personalized fitness journey.

• Interactive Frontend: A responsive React frontend allows for easy profile setup and management.

• RESTful API: A robust Flask backend provides a clear and scalable API for all application services.

AI/LLM Integration

This application leverages cutting-edge AI and Large Language Models (LLMs) to provide highly personalized experiences:

• Google Vertex AI: The backend agents are deployed and managed on Google Vertex AI, ensuring scalable and robust AI inference.
• Gemini 2.5: Utilized for complex reasoning and generation tasks, such as creating detailed workout plans and nuanced fitness options.
• Gemini 2.0 Flash Lite: Employed for tasks requiring low-latency responses, ensuring a snappy and responsive user experience, especially during interactive fitness option selections.

Tech Stack

• Frontend: React, Vite, react-router-dom, Tailwind CSS
• Backend: Python, Flask, SQLAlchemy, Alembic, Pydantic, LangChain, Google GenAI

Project Structure

/fitness-app
├── backend/         # Flask API and business logic
│   ├── app/         # Core application files, including routes and agents
│   ├── migrations/  # Database migration scripts
│   ├── run.py       # Application entry point
│   └── requirements.txt
├── src/             # React frontend source code
│   ├── components/  # Reusable React components
│   ├── App.jsx      # Main application component with routing
│   └── main.jsx     # Frontend entry point
├── package.json
└── README.md

Getting Started

Prerequisites

• Node.js & npm
• Python 3.x & pip

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables: Copy the example .env.example to .env and fill in the required values.

5. Run database migrations:

   flask db upgrade

6. Start the backend server:

   python run.py

Frontend Setup

1. Navigate to the root directory:

   cd ..

2. Install dependencies:

   npm install

3. Start the frontend development server:

   npm run dev

The application should now be running and accessible in your browser.

API Endpoints

The backend exposes the following RESTful endpoints:

• POST /api/profiles: Create a new user profile.
• GET /api/profiles/<uuid>: Retrieve a user profile by ID.
• PUT /api/profiles/<uuid>: Update an existing user profile.
• POST /api/profiles/<uuid>/workout-plan: Generate a new workout plan for a user.
• GET /api/profiles/<uuid>/workout-plan: Retrieve the latest workout plan for a user.