GymPulse Backend

FastAPI backend for the GymPulse AI fitness tracker.

What It Does

• Authentication and profile APIs.
• Food logs, saved foods, saved meals, summaries, and streaks.
• Gym sessions, sleep logs, supplements, settings, and daily check-ins.
• Body photo and body analysis workflows.
• Notifications, email, FCM, AI analysis, MinIO, and OpenFoodFacts integrations.

Stack

• FastAPI.
• MongoDB through Motor/PyMongo.
• Pydantic.
• Uvicorn.
• Python service modules under routers/, models/, and services/.

Local Development

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Project Structure

main.py        # FastAPI entrypoint
auth.py        # Auth helpers
database.py    # MongoDB connection
models/        # Data models
routers/       # API route modules
services/      # Email, FCM, AI, MinIO, OpenFoodFacts, TDEE
scripts/       # Migration/maintenance scripts

Environment

Start from .env.example and keep real values local/server-side only.

Typical settings include:

MONGODB_URI=replace-with-mongodb-uri
JWT_SECRET=replace-with-secure-secret
EMAIL_USERNAME=replace-with-email
EMAIL_PASSWORD=replace-with-app-password
FIREBASE_CREDENTIALS=replace-with-path-or-secret
MINIO_ENDPOINT=replace-with-minio-endpoint

Verify

python3 -m compileall .

Add focused tests for changed business logic.

Safety Rules

• Do not commit .env*, Firebase credentials, API keys, or database credentials.
• Keep API response changes synchronized with ../app.
• Avoid logging sensitive health, body, or nutrition request data.