Healthcare Form Platform

A simple fullstack application for cardiovascular health assessment with automated grammar improvement.

Project Structure

├── backend/          # Python FastAPI backend with async job processing
├── frontend/         # React frontend application
└── README.md        # This file

Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Redis server

One-Command Setup & Run

1. Start Redis (if not already running):

   # macOS: brew services start redis
   # Linux: sudo systemctl start redis  
   # Docker: docker run -d -p 6379:6379 redis:alpine

2. Start everything with hot reload:

   ./start_dev.sh

3. Access the Application:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs

4. Stop everything:

   # Press Ctrl+C in the terminal running start_dev.sh
   # The script will automatically stop all services

Hot Reload Features ✨

• FastAPI Backend: Auto-reloads on Python file changes
• React Frontend: Auto-reloads on JavaScript/CSS changes
• arq Worker: Auto-restarts on Python file changes (using watchmedo)
• Data Storage: JSON file automatically created on first run

Manual Setup (Alternative)

If you prefer to run services individually in separate terminals:

# Terminal 1 - Backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

# Terminal 2 - Worker (with hot reload)
cd backend
watchmedo auto-restart --recursive -- arq worker.WorkerSettings

# Terminal 3 - Frontend  
cd frontend
npm install
npm start

Technology Stack

Backend

• FastAPI: Modern Python web framework
• JSON File Storage: Simple local file persistence (no database setup required)
• arq: Redis-based job queue for async processing
• Redis: In-memory data store for job queue
• OpenAI + OpenRouter: LLM integration for grammar improvement using Llama 4 Maverick

Frontend

• React: JavaScript library for building user interfaces
• Axios: HTTP client for API communication
• CSS: Modern styling with responsive design

How It Works

Data Flow

1. Frontend Form: User fills out 5 cardiovascular health questions
2. JSON Storage: Form data is saved to backend/health_form_data.json
3. Async Processing: When user triggers grammar improvement, a background job is queued
4. LLM Analysis: OpenAI + OpenRouter (Llama 4 Maverick) analyzes the health responses for grammatical issues
5. Smart Improvements: The LLM makes minimal changes to improve grammar while preserving meaning
6. Real-time Updates: Frontend polls job status and displays results

API Endpoints

• GET /form - Retrieve current health form data
• PUT /form - Update health form data
• POST /form/improve-grammar - Trigger LLM grammar improvement (async)
• GET /jobs/{job_id}/status - Check background job status