Revise README for QuickBite branding and features

README.md

@@ -1,31 +1,141 @@
-# QuickBite — Food Delivery App
+# QuickBite: AI-Powered Food Delivery Platform
 
-![GitHub stars](https://img.shields.io/github/stars/bobbleteeb/Team-Sleep?style=social)
-![GitHub forks](https://img.shields.io/github/forks/bobbleteeb/Team-Sleep?style=social)
-![GitHub issues](https://img.shields.io/github/issues/bobbleteeb/Team-Sleep)
-![Next.js](https://img.shields.io/badge/Next.js-16.1-black?logo=next.js)
+![Next.js](https://img.shields.io/badge/Next.js-16.1.x-black?logo=next.js)
+![React](https://img.shields.io/badge/React-19.2.x-149eca?logo=react)
 ![TypeScript](https://img.shields.io/badge/TypeScript-5.x-blue?logo=typescript)
-![Tailwind CSS](https://img.shields.io/badge/Tailwind-v4-38bdf8?logo=tailwind-css)
+![Supabase](https://img.shields.io/badge/Supabase-PostgreSQL-3ecf8e?logo=supabase)
+![OpenAI](https://img.shields.io/badge/OpenAI-Integrated-10a37f?logo=openai)
 
-A full-stack food delivery app built with Next.js, Supabase, and OpenAI. Supports two roles — **customers** can browse nearby restaurants, place orders, and chat with drivers; **drivers** can accept deliveries, track earnings, and use an AI copilot to message customers.
+QuickBite is a full-stack food delivery app where AI drives real product behavior.
 
-> **Status:** Work in progress
+Customers order with natural language, discover nearby food with location-aware recommendations, and place orders in a chat-first flow. Drivers use an AI copilot to generate clear, professional messages while managing live deliveries.
 
----
+## What This App Does
 
-## Running with Docker
+- Customer and driver experiences in one platform
+- AI-powered ordering assistant that can perform structured actions
+- AI driver message copilot for ETA, delays, arrival, and contact issues
+- Geolocation-based restaurant discovery with fallback data sources
+- Real-time order messaging between customers and drivers
+- Cart, checkout, promo discounts, and order history
 
-### 1. Set up environment variables
+## Why It Matters (Recruiter View)
+
+- End-to-end ownership: product UX, API design, data modeling, and AI integration
+- Practical LLM orchestration: prompt design, JSON action extraction, and safe fallbacks
+- Real-world architecture: dual-role workflows, status transitions, and resilient backend behavior
+- Built with a modern TypeScript stack and production-minded patterns
+
+## AI Usage (Core of the Product)
+
+### 1) AI Customer Ordering Assistant
+
+Customers can type requests like:
+- "Find sushi near me"
+- "Add 2 burgers from The Vortex Tucker"
+- "Place my order to 120 Main St"
+
+The assistant:
+1. Reads live restaurant menus and current cart context
+2. Understands user intent from natural language
+3. Returns structured actions when needed:
+   - `add_to_cart`
+   - `place_order`
+4. Falls back to conversational guidance when clarification is needed
+
+### 2) AI Location-Aware Food Fallbacks
+
+If an item is not available in nearby QuickBite menus, the assistant can re-check nearby food options via Google Places and suggest closest matches with distance in miles.
+
+### 3) AI Driver Copilot
+
+Drivers choose an intent and get ready-to-send customer messages:
+- ETA update
+- Arrival message
+- Delay notice
+- Cannot reach customer
+- Custom prompt
+
+This reduces friction and keeps communication concise and professional.
+
+## How It Works (End-to-End Flow)
+
+### Customer Flow
+
+1. Customer logs in
+2. App detects location (with fallback + cache)
+3. Nearby restaurants are fetched and sorted by distance (miles)
+4. Customer orders using AI chat
+5. AI emits structured cart/order actions
+6. Order is placed and tracked live
+7. Customer can message driver in real time
+
+### Driver Flow
+
+1. Driver logs in and goes online
+2. Driver views and accepts pending deliveries
+3. Driver updates order status through delivery lifecycle
+4. Driver uses AI copilot for customer messages
+5. Completed orders are tracked in history and stats
+
+## Architecture Overview
+
+### Frontend
+- Next.js App Router + React 19 + TypeScript
+- Role-based UI for customers and drivers
+- Context-based state management for auth and cart
+
+### Backend
+- Next.js route handlers for auth, chat, orders, carts, restaurants, and driver operations
+- API-driven business logic and validation
+
+### Database
+- Supabase (PostgreSQL)
+- Tables for users, customers, drivers, restaurants, menus, carts, orders, and order messages
+- Order status lifecycle and relational links between actors and orders
+
+### AI Layer
+- OpenAI chat completions for:
+  - customer ordering assistant
+  - driver message copilot
+- Prompted for structured, operational outputs where appropriate
+
+### Geolocation and Discovery
+- Browser geolocation with timeout and cache
+- Multi-source restaurant pipeline:
+  - Supabase
+  - Google Places
+  - Overpass fallback
+- Distances computed in miles via Haversine
+
+## Tech Stack
+
+- Next.js 16
+- React 19
+- TypeScript 5
+- Tailwind CSS 4
+- Supabase
+- OpenAI API
+- Google Maps Places API
+
+## Local Setup
+
+### 1) Install dependencies
+
+```bash
+npm install
+```
+
+### 2) Configure environment
 
 ```bash
 cp .env.example .env.local
 ```
 
-Fill in `.env.local` with your keys:
+Add required values to `.env.local`:
 
 ```env
 OPENAI_API_KEY=sk-proj-...
-OPENAI_MODEL=gpt-4
 
 GOOGLE_MAPS_API_KEY=...
 
@@ -34,32 +144,66 @@ NEXT_PUBLIC_SUPABASE_ANON_KEY=...
 SUPABASE_SERVICE_ROLE_KEY=...
 ```
 
-### 2. Build the image
+> **Optional:** `OPENAI_MODEL` defaults to `gpt-4.1` when unset. Override it if you want to use a different model (e.g. `OPENAI_MODEL=gpt-4o`).
+
+### 3) Initialize database
+
+Run the SQL in `supabase.sql` using your Supabase SQL Editor.
+
+### 4) Seed demo data
+
+```bash
+npm run seed
+```
+
+Optional: seed sample pending orders and test accounts for full delivery demos:
+
+```bash
+npm run seed:orders
+```
+
+Test accounts created by `seed:orders`:
+- Driver: `dummy.driver@quickbite.test` / `password123`
+- Customer: `dummy.customer@quickbite.test` / `password123`
+
+### 5) Start app
+
+```bash
+npm run dev
+```
+
+Open http://localhost:3000
+
+## Docker (Optional)
 
 ```bash
 docker build \
   --build-arg NEXT_PUBLIC_SUPABASE_URL=your_supabase_url \
   --build-arg NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key \
   -t quickbite .
-```
-
-### 3. Run the container
 
-```bash
 docker run -p 3000:3000 \
   -e OPENAI_API_KEY=your_key \
   -e GOOGLE_MAPS_API_KEY=your_key \
   -e SUPABASE_SERVICE_ROLE_KEY=your_key \
   quickbite
 ```
 
-Open [http://localhost:3000](http://localhost:3000).
+## Key Engineering Highlights
 
----
+- LLM-integrated product actions, not just chat responses
+- Fallback-aware architecture for location and restaurant discovery
+- Dual-role domain modeling and delivery lifecycle states
+- Type-safe full-stack implementation with modern React and Next.js
 
-## Running locally (without Docker)
+## Project Docs
 
-```bash
-npm install
-npm run dev
-```
+- `AI_IMAGE_SEARCH_SETUP.md`
+- `GEOLOCATION_FEATURE.md`
+- `DISTANCE_AND_IMAGES_UPDATE.md`
+- `PRODUCTION_DEMO_SETUP.md`
+- `TEAM_SETUP.md`
+
+## Status
+
+Actively evolving and demo-ready, with AI-first product functionality already implemented across customer and driver workflows.