🍽️ FoodChain

AI-Powered Raw Material Sourcing Platform for Street Food Vendors

A perfect combination of AIML and WebDev

SmartStreetSupply is a full-stack web application designed to help street food vendors find trusted nearby raw material sellers while maintaining high food quality and minimizing daily food wastage. The platform integrates AI and Machine Learning to provide personalized quantity estimation, nearest seller discovery, and review sentiment analysis, enabling smarter and more sustainable food operations.

Features

• Review System: Buyers can write reviews for sellers with sentiment analysis
• Color-coded Seller Status: Sellers are displayed with green/red borders based on review sentiment
• Hugging Face Integration: Automatic sentiment analysis of reviews using AI
• Real-time Review Updates: Reviews are immediately reflected in seller status

Environment Setup

Create a .env.local file in the root directory with the following variables:

# Hugging Face API Key for sentiment analysis
# Get your API key from: https://huggingface.co/settings/tokens
HUGGING_FACE_API_KEY
JWT_SECRET
UPSTASH_REDIS_REST_URL
UPSTASH_REDIS_REST_TOKEN
GROQ_API_KEY
MONGODB_URI

Getting Started

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🚀 Features

👨‍🍳 Street Food Vendors

• Find nearby raw material sellers
• Get AI-based raw material quantity estimation
• Reduce daily food wastage
• View seller ratings based on sentiment analysis

🏪 Raw Material Sellers

• List available raw materials
• Reach nearby vendors easily
• Build trust through transparent reviews

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🧠 AI & Machine Learning

• KNN Algorithm to find the nearest raw material sellers
• Personalized AI Model for quantity estimation using daily wastage data
• Sentiment Analysis (NLP) to classify seller reviews
• Redis caching for faster ML model responses

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🏗️ Tech Stack

Frontend

• Next.js
• Tailwind CSS

Backend

• Node.js
• Express.js

Database

• MongoDB

Caching

• Redis

Machine Learning

• K-Nearest Neighbors (KNN)
• Sentiment Analysis (NLP)

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⚙️ How It Works

1. Vendor enters daily sales and food wastage data
2. AI estimates the exact raw material quantity required
3. KNN model identifies nearest trusted sellers
4. Sentiment analysis validates seller reviews
5. Redis caches results for faster future access
6. Vendor places order with confidence

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🚀 Getting Started

Follow these steps to run the project locally.

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📦 Prerequisites

Make sure you have the following installed:

• Node.js (v18 or above recommended)
• npm
• MongoDB
• Redis
• Docker compose

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🔽 Clone the Repository

git clone https://github.com/saburi004/TutedudeHackathon.git
cd TutedudeHackathon
npm install

Create a .env file in the root directory

docker compose up -d 
npm run dev