FoodieAI
Link to open source: https://github.com/Saqib-develops/gdgc_hackathon
Project Description
FoodieAI is an AI-powered restaurant companion designed to improve the dining experience for both customers and restaurant owners.
It works through a QR-based conversational ordering system, where customers simply scan a QR code to open a friendly chat assistant that suggests dishes based on taste, diet, and budget — just like asking a friend for recommendations.
For restaurants, FoodieAI provides a smart Admin Dashboard to manage the menu, auto-generate dish descriptions using AI, and analyze customer feedback to learn which dishes are loved and which need improvement.
The system continuously learns from real customer preferences using a like/dislike feedback loop — helping restaurants improve their menu and highlight best-performing dishes.
💡 Problem Statement
Customers are often confused when choosing from large menus, and many restaurants struggle to describe dishes attractively. Staff recommendations are inconsistent, and menu decisions are made without data.
✅ Our Solution
| Feature | Benefit |
|---|---|
| QR-based AI Food Recommendation Chatbot | Helps customers quickly decide what to eat, based on taste & budget. |
| AI Menu Generator (Admin Dashboard) | Automatically creates appealing descriptions, promos & pairings. |
| Real-Time Dish Feedback System | Tracks what customers like/dislike to improve menu curation. |
| Menu Performance Analytics | Helps restaurants identify trending & weak-performing dishes. |
🚀 How it Works
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Restaurant prints a QR code and places it on tables.
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Customer scans it → Opens chat UI in browser.
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Customer types preferences (ex: “veg spicy under ₹120”).
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AI suggests 3 best-fit dishes from the restaurant's real menu.
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Customer can 👍 Like / 👎 Dislike recommendations.
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Admin sees analytics and updates the menu accordingly.
🔧 Tech Stack
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Frontend: React + Vite + Netlify Deployment
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Backend: Node.js + Express + Render Deployment
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Database: MongoDB Atlas
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AI Model: Gemini (Google AI API)
🎯 Impact
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Faster ordering & better customer satisfaction.
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Personalized recommendations tailored to taste.
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Restaurants get data-driven insights — not guesswork.
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Reduces dependence on staff training.
This build was uploaded as a hackathon project
