A ready-to-run web app with a lightweight frontend and a small Flask backend: a rules-based career advisor and a curated dataset of skills & career paths (500+).

• 🪟 Glassmorphism login with smooth animations
• 💬 Career advisor chat powered by a rules engine and searchable dataset
• 📚 500+ skills & careers with bullets, learning paths, and links
• 🌗 Dark / Light theme toggle with animated control
• ✨ Micro-interactions, hover-to-flip cards, and responsive layout

1. Ensure Python 3.10+ is installed.
2. Create and activate a virtual environment (recommended):

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Start the app (development):

python app.py

# or use gunicorn for production-like serving
# gunicorn app:app -b 0.0.0.0:5000 --workers 2

5. Open http://localhost:5000 in your browser.

This repo includes a Dockerfile and a render.yaml so you can deploy easily to Render using Docker. Vercel's Docker builder may not be available in some environments — for Vercel we can convert the app to use serverless functions or publish an image to a container registry.

Quick options:

• Deploy to Render (Docker): connect your GitHub repo in Render and it will use render.yaml and your Dockerfile.
• Deploy to Vercel: convert to a Vercel-friendly setup (serverless) or publish a container image and configure Vercel to use it.

Test locally with Docker (if Docker is installed):

docker build -t personapath:local .
docker run -p 5000:5000 personapath:local
# then open http://localhost:5000

Notes:

• Set environment variables in your host's project settings (Render / Vercel / Heroku).
• The app is served by gunicorn in the Dockerfile for production.

PERSONAPATH/
  app.py
  Procfile
  README.md
  requirements.txt
  trim_links.py
  data/
    skills_careers.json        # Add/edit skills & careers here
  static/
    styles.css
    theme.js
    explore.js
    dashboard.js
    chat.js
  templates/
    base.html
    index.html
    login.html
    dashboard.html
    chat.html
    explore.html

• The chat uses deterministic rules + search to return exact data where possible.
• Edit data/skills_careers.json to update the dataset.
• You can deploy to any Python-friendly host (Render, Railway, Heroku, etc.).