Personal Health Assistant Chatbot (Frontend Demo)

Team: Codecrafters
Team Leader: Udit Sharma (2400351530032)

Personal Health Assistant Chatbot — a frontend-only demo that provides personalized diet suggestions, meal timings and sleep recommendations based on user-entered health profiles. All data (profiles & history) is stored locally in the browser using localStorage — no backend required for the demo.

Features

• Save user profile (name, age, weight, height, goal)
• Generate rule-based recommendations: daily calories, sample meals, sleep/wake times, notes
• Chat-like interface to ask for recommendations
• Upload simple profile files (JSON / text) to prefill data
• History of previous recommendations stored in browser (localStorage)
• Quick demo-ready: open index.html to run

How to run (Demo / Local)

Option A — Open directly (no server)

1. Clone the repository:

   git clone <your-repo-url>
   

2. Open the project folder and double-click index.html (or drag it into your browser).

Option B — Serve with a simple HTTP server (recommended)

Using Python:

python3 -m http.server 8000
# then open http://localhost:8000/index.html

Using VSCode Live Server:

• Install Live Server extension, open index.html, and click "Open with Live Server".

Usage (demo script)

1. Fill the Profile form (left panel): name, age, weight (kg), height (cm), goal (maintain/lose/gain).
2. Click Save Profile.
3. Click Get Recommendation or ask in chat: "What should I eat today?"
4. View recommendation (calories, meals, sleep, notes).
5. Check History to view saved recommendations.

Folder structure

• index.html — main demo page (chat UI + profile form)
• styles.css — styling
• script.js — client-side logic, recommendation engine, localStorage
• README.md — project description & instructions

Deployment (GitHub Pages)

1. Push the repo to GitHub.
2. In repository Settings → Pages, select branch main and folder / (root).
3. Save — the site will be available at https://<your-username>.github.io/<repo-name>/

Notes & Limitations

• Data stored only in browser localStorage — clearing browser data removes profiles & history.
• No authentication or server-side storage. For production, integrate a backend and DB.
• Recommendation engine is rule-based and simple — extendable.

Team & Contributions

• Team: Codecrafters
• Team Leader: Udit Sharma — 2400351530032 (Project lead, Backend & Integration lead, final demo assembly)
• Tarun Gautam — 2400351530031 (Frontend & UI)
• Vivek Prashad — 2400350100052 (Chatbot logic & suggestion engine)
• Vikhyat Shrivastava — 2400351530034 (Database/Integration, deployment, docs & testing)

License

MIT