Flipkart Search System

A full-stack search system inspired by Flipkart, featuring autosuggest, typo-tolerance, and semantic search over a real product dataset. Built with FastAPI (backend) and Next.js (frontend).

Live Demo

Frontend: https://grid-front-deploy.vercel.app/
Backend: https://grid-backend-20320365612.asia-south1.run.app/health

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Features

1. Intelligent Autosuggestion with Typo-Tolerance

Our advanced autosuggestion system handles spelling errors, phonetic matching, and fuzzy search to provide accurate suggestions even when users make typos.

2. Context-Based Search

The system remembers your search patterns and prioritizes relevant categories based on your previous searches. When you search for "shirt", the system saves the category path (Apparel→Men→Shirts→Casual Shirts) in local storage.

Later, when you type "pan", pants-related products appear at the top of suggestions based on your browsing context.

3. Time-Based Smart Suggestions

Our system adapts suggestions based on the time of day:

• Early morning: Typing "d" shows diya, dal, etc. (daily essentials)
• Night time: Typing "d" suggests daaru, wine, etc. (evening items)

4. Delivery Days Filter

A unique feature missing in many e-commerce sites - sort products by number of delivery days to find the fastest shipping options.

5. Additional Features

• Semantic product search using sentence transformers
• Trending keywords and multi-language support (Hindi-English ready)
• Real Flipkart product data (from Kaggle)
• Modern, responsive frontend with product grid, filters, and cart functionality

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Project Structure

flipkartSearchSystem/
├── backend/                # FastAPI backend
│   ├── data/
│   │   └── products.json   # Product database (JSON)
│   ├── main.py            # Main API server
│   └── requirements.txt   # Backend dependencies
├── frontend/              # Next.js frontend
│   ├── components/
│   ├── pages/
│   └── ...
└── dataset.csv           # (Optional) Raw product dataset

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Technical Documentation

For more detailed technical requirements and implementation details, please refer to our Technical Documentation.

Setup Instructions

Quick Start (Recommended)

Don't want to set up locally? Use our deployed links:

• Frontend: https://grid-front-deploy.vercel.app/
• Backend: https://grid-backend-20320365612.asia-south1.run.app/health

Local Development Setup

1. Clone the Repository

git clone https://github.com/prasaaaad98/Grid7.0.git
cd Grid7.0

2. Backend Setup

a. Create Virtual Environment (Recommended)

cd backend
python -m venv venv

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

b. Install Dependencies

pip install -r requirements.txt
python -m spacy download en_core_web_sm

c. Run Backend Server

uvicorn main:app --reload

The API will be available at http://localhost:8000

3. Frontend Setup

cd ../frontend
npm install
npm run dev

The frontend will be available at http://localhost:3000

Note: If the frontend .env file contains NEXT_PUBLIC_BACKEND_URL, that URL will be used; otherwise, it defaults to http://localhost:8000

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Usage

1. Open http://localhost:3000 in your browser
2. Use the search bar to test:
   • Autosuggest functionality
   • Typo-tolerance features
   • Semantic product search
3. Explore features:
   • Add products to cart
   • Apply filters
   • Sort by delivery days
   • Experience context-based suggestions

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Contributing

We welcome contributions! Here's how to get started:

1. Fork this repository and clone your fork
2. Create a new branch for your feature or bugfix
3. Make your changes (backend or frontend)
4. Test thoroughly (run both backend and frontend locally)
5. Submit a pull request with a clear description

Ideas for Contribution

• Improve autosuggest ranking algorithms
• Add more filters (brand, category, price range)
• Enhance product detail pages
• Implement user authentication and order history
• Optimize backend for larger datasets
• Add Hindi-English translation for queries
• Implement real-time inventory updates
• Add recommendation system based on user behavior

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Troubleshooting

Issue	Solution
Backend slow to start	Reduce dataset size in convert_csv_to_json.py
Unicode errors	Ensure all files use encoding='utf-8'
Dependency errors	Check Python and Node.js versions
Frontend not connecting to backend	Verify NEXT_PUBLIC_BACKEND_URL in .env

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Data Setup (Optional)

The backend uses backend/data/products.json as its database. To use a new dataset:

1. Place your CSV as dataset.csv in the project root
2. Run the conversion script:

   python convert_csv_to_json.py

3. This generates backend/data/products.json in the correct format

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License

MIT License - feel free to use this project for learning and development.

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Credits

• Data Scraping: EcommerceAPI.io
• Product Dataset: Kaggle Flipkart Product Dataset
• Built with: FastAPI, Next.js, and open-source libraries
• Created by: Grid 7.0 Team

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*Happy Searching!