🏋️ TrackFitt API

FastAPI backend for TrackFitt — AI-powered workout recommendations, real-time tracking, token gamification.

Frontend → TrackFitt

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Architecture

graph TD
    A[React Client] -->|REST API| B[FastAPI]
    A -->|WebSocket| C[WS Manager]
    B --> D{AI Layer}
    D -->|Exercises| E[DeepSeek API]
    D -->|Suggestions| F[Google AI Studio]
    B --> G[(PostgreSQL)]
    G --> H[17 Tables<br/>SQLAlchemy + Alembic]
    B --> I[S3<br/>Profile Images]

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What's Inside

57 REST Endpoints + WebSocket

AI Features:

• Exercise recommendations based on fitness level and goals
• Smart workout plan generation
• AI-powered exercise categorization

Core API:

• Full auth flow (JWT + role-based access)
• Exercise CRUD with 200+ pre-loaded exercises (CSV seed data)
• Workout creation, scheduling, and active tracking
• Workout history with detailed set/rep/weight logging
• Token economy (earn → request → distribute)
• User profiles with fitness/health/goal data
• Real-time WebSocket notifications

Admin API:

• User management (roles, activation)
• Exercise library management
• Token request approval/rejection

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Data Models (17 Tables)

Model	Purpose
User	Auth + roles (user/admin)
UserProfile	Physical metrics, fitness level, goals
Exercise	200+ exercises with categories
ExerciseCategory	Cardio, HIIT, Pilates, Flexibility, Strength
Workout	Custom workout templates
WorkoutExercise + Set	Exercises within workouts with sets
ScheduledWorkout	Planned future workouts
WorkoutHistory	Completed workout logs
Token + TokenRequest	Gamification currency
Notification	Real-time alerts
AITracking	AI interaction logs

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Quick Start

Docker (recommended)

git clone https://github.com/abhiFSD/TrackFitt-API.git
cd TrackFitt-API
cp .env.example .env    # Add your API keys
docker-compose up -d    # Starts API + PostgreSQL

API available at http://localhost:8000

Manual

pip install -r requirements.txt

# Setup database
alembic upgrade head
python init-db.py

# Run
uvicorn app.main:app --reload --port 8000

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Tech Stack

Component	Technology
Framework	FastAPI
Database	PostgreSQL 15
ORM	SQLAlchemy 2.0
Migrations	Alembic
AI	DeepSeek API + Google AI Studio
Auth	JWT (python-jose) + bcrypt (passlib)
Storage	AWS S3 (boto3)
Real-time	WebSockets
Deployment	Docker + Docker Compose
Data	CSV seed files (200+ exercises)

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Project Structure

├── app/
│   ├── main.py                # FastAPI app + CORS + WebSocket
│   ├── api/
│   │   ├── endpoints.py       # 57 route handlers
│   │   └── auth.py            # JWT auth + password hashing
│   ├── models/models.py       # 17 SQLAlchemy models
│   ├── schemas/schemas.py     # Pydantic request/response schemas
│   ├── services/
│   │   ├── websocket_service  # Connection manager
│   │   └── notification_*     # Push notification system
│   └── db/database.py         # DB session + engine
├── dataCsv/                   # Exercise seed data
│   ├── cardio_exercises.csv
│   ├── hiit_exercises.csv
│   ├── pilates_exercises.csv
│   └── flexibility_exercises.csv
├── migrations/                # Alembic migrations
├── docker-compose.yml         # API + PostgreSQL
├── Dockerfile
└── requirements.txt

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Environment Variables

cp .env.example .env

Variable	Required	Description
AWS_REGION	Yes	AWS region for S3
AWS_ACCESS_KEY_ID	Yes	S3 access key
AWS_SECRET_ACCESS_KEY	Yes	S3 secret
S3_BUCKET	Yes	S3 bucket for uploads
DEEPSEEK_API_KEY	Yes	DeepSeek AI key
GOOGLE_AI_STUDIO_KEY	Optional	Google AI key

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License

MIT