Trendscope AI (skeleton)

This repository provides a lightweight starter implementation for the AI Product Trend Forecasting app described in Jobsss.txt. It includes:

• Node.js + Express mock API with watchlist limits, product search, and subscription tiers.
• Python Flask ML microservice that scores products using rank velocity, price momentum, and review growth.
• React Native (Expo) UI scaffolding with shared components for product display and trending dashboard.

Backend (Node.js)

cd backend
npm install
# Copy environment defaults
cp .env.example .env
npm run dev
# Run config tests
npm test
# Verify the health endpoint
curl http://localhost:4000/health

The API exposes endpoints for health checks, mock authentication, subscription tiers, watchlist management, and product search/detail requests. Environment variables live in .env.example.

Key configuration flags:

• PORT: Port used by the API (default 4000).
• ALLOWED_ORIGINS: Comma-separated domains allowed by CORS. Leave blank to allow all during local development.
• WATCHLIST_*_LIMIT: Watchlist caps per subscription tier.
• MOCK_PRODUCT_COUNT: Number of mock products generated for demo search responses.

ML Service (Python)

cd ml_service
python -m venv .venv --system-site-packages
source .venv/bin/activate
pip install -r requirements.txt  # optional if dependencies are already available on the host
pytest
python app.py

The /trend-score endpoint returns a computed score and label. /health reports service status.

Mobile (React Native / Expo)

cd mobile
npm install
npm start

The Expo app renders a dashboard of trending products using the ProductCard and TrendBadge components.

Notes

This codebase is a foundation and does not yet include production integrations (Stripe, Firebase, Redis, PostgreSQL, PA-API) or full scheduling. It is meant to illustrate the architecture, key endpoints, and UI building blocks for the full feature set in Jobsss.txt.

Windows 10+ quick installer

Run the bundled PowerShell script from an elevated (Administrator) terminal to install prerequisites with winget and set up each project workspace:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
./scripts/windows-installer.ps1

Add -SkipMobile if you do not want to install the React Native / Expo dependencies on the first pass. After the script finishes you can start services with the standard commands shown in the output (e.g., cd backend && npm run dev, cd ml_service && .\.venv\Scripts\activate && python app.py).

If you want to validate the script from a non-Windows host or without making changes, use the dry-run mode:

./scripts/windows-installer.ps1 -DryRun -SkipMobile