Women’s Health Symptom Tracker

A modern women’s health tracking web application designed to help users log symptoms, understand patterns in their health data, and visualize trends over time.

The project evolved from a backend prototype into a product-oriented MVP with a redesigned frontend inspired by consumer health applications such as Flo.

This repository demonstrates full-stack engineering, including:

• API design
• database modeling
• frontend product UX
• data visualization
• containerized infrastructure

Features

Today Page

The Today page serves as the main daily home screen and includes:

• cycle context placeholder
• quick actions
• weekly activity summary
• selected symptom overview

This page was designed to feel personal, calm, and product-oriented, rather than like a traditional admin dashboard.

Calendar / Cycle View

The Calendar page provides a month-based history view of logged health events.

Users can:

• navigate months
• select specific days
• view entries for the selected date

The goal is to give users a cycle-centered view of their health history.

Health Insights

The Insights page visualizes patterns extracted from logged health data.

It currently includes:

• entries in the last 7 days
• most frequent category
• highest severity category
• total entries tracked
• severity trend chart

The visualization layer uses Recharts to display time-based trends.

Daily Health Logging

Users can log how they feel using a structured daily check-in experience. The logging interface organizes health events into categories such as:

• Mood
• Symptoms
• Digestion
• Vaginal discharge
• Sex and sex drive
• Contraception and pills
• Pregnancy / ovulation tests
• Lifestyle factors

Users select items using interactive chips that create structured symptom entries.

Architecture

The application follows a full-stack web architecture.

React (Frontend)
       │
       │ REST API
       ▼
FastAPI (Backend)
       │
       │ ORM
       ▼
PostgreSQL Database

The project is fully containerized using Docker.

Tech Stack

Frontend

• React
• TypeScript
• Vite
• Recharts
• React Calendar

Backend

• FastAPI
• SQLAlchemy
• Pydantic
• JWT authentication

Database

PostgreSQL

Infrastructure

• Docker
• Docker Compose

Future infrastructure phases are planned for:

• AWS ECS
• Terraform
• CI/CD pipelines

Backend API

Key API endpoints include:

Authentication

POST /auth/register
POST /auth/login

Symptom Logging

POST /symptoms
GET /symptoms
GET /symptoms?category=...
GET /symptoms?severity=...

Insights

GET /insights/summary

Returns:

• total entries
• days tracked
• entries last 7 days
• most frequent category
• highest severity category

Data Export

GET /symptoms/export

Exports user symptom logs as a CSV file.

Running the Project Locally

1. Clone the repository

git clone https://github.com/your-username/whealth-tracker.git
cd whealth-tracker

2. Start backend services

docker compose up -d

This starts:

• FastAPI server
• PostgreSQL database

3. Verify backend health

curl http://localhost:8000/health

Expected response:

{"status":"ok"}

4. Start the frontend

cd frontend
npm install
npm run dev

The application will be available at:

http://localhost:5174

Database Model (Simplified)

The core data model stores symptom entries as structured records.

Example entry:

date_time: 2026-03-12T10:00:00Z
category: symptoms
severity: 9
notes: Strong pain
tags: ["cramps"]

Each log is stored with:

• timestamp
• category group
• severity
• tags
• notes

This design supports flexible health event tracking.

Project Structure

whealth-tracker
│
├── backend
│   ├── app
│   │   ├── api
│   │   ├── models
│   │   ├── schemas
│   │   └── services
│   │
│   └── main.py
│
├── frontend
│   ├── src
│   │   ├── components
│   │   ├── layout
│   │   ├── pages
│   │   ├── routes
│   │   └── styles
│
├── docs
│   └── screenshots
│
└── docker-compose.yml

Product Vision

The long-term goal of the project is to evolve into a personal women health companion that helps users understand patterns in their bodies.

Future capabilities may include:

• cycle prediction
• advanced symptom correlations
• machine learning insights
• doctor-friendly health reports
• personalized recommendations

Future Work

Planned improvements include:

Product

• richer cycle tracking
• pregnancy test tracking
• contraception tracking
• symptom severity visualization

Infrastructure

• production deployment
• AWS ECS container orchestration
• Terraform infrastructure
• CI/CD pipelines

Data Intelligence

• machine learning for symptom patterns
• personalized health insights
• anomaly detection

Compliance Note

This project is HIPAA-aware by design, meaning architectural decisions consider healthcare data sensitivity.

However:

The project is not HIPAA compliant or certified.

Compliance requires additional controls including:

• encrypted storage
• audit infrastructure
• formal policies
• production security reviews

These concerns may be addressed in later phases.