Searchly: AI-Powered Product Recommendation System

Welcome to Searchly, your go-to AI-powered e-commerce assistant! Searchly is a cutting-edge product recommendation system designed to deliver highly personalized shopping experiences. Powered by AI-driven algorithms, real-time data scraping, and a modern frontend, Searchly bridges the gap between user intent and the perfect product match.

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Table of Contents

1. Features
2. Project Structure
3. Architecture Diagram
4. Tech Stack
5. Workflow Diagram
6. Setup Instructions
7. Detailed Component Overview
   • Backend
   • Frontend
8. Usage
9. API Endpoints
10. Screenshots
11. Contributing

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Features

• AI Chat Assistant: "Aivy" guides users through product discovery with natural language queries.
• Personalized Recommendations: Provides tailored suggestions based on user preferences.
• Favorites Management: Add and remove products from a personal favorites list.
• Search Filters: Sort and filter products by price, ratings, and relevance.
• Real-Time Product Scraping: Fetch live product data for up-to-date recommendations.
• Secure Authentication: Secure user registration and login with JWT tokens.
• Responsive Design: Works seamlessly across devices.

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Project Structure

📁 Searchly
├── 📁 client (Frontend)
│   ├── 📁 public
│   ├── 📁 src
│   │   ├── 📁 components
│   │   ├── 📁 data
│   │   ├── App.js
│   │   ├── index.js
│   │   └── Tailwind CSS config files
│   └── package.json
├── 📁 server (Backend)
│   ├── app.py
│   ├── scrape_web.py
│   ├── 📁 .env (Configuration variables)
│   └── requirements.txt
├── README.md

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Architecture Diagram

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Tech Stack

Backend:

• Python (Flask)
• MongoDB for data persistence
• Pinecone for vector similarity search
• HuggingFace Embeddings for query-product matching
• Groq API for AI-based query understanding
• BeautifulSoup/Requests for web scraping

Frontend:

• React for UI
• Framer Motion for animations
• Tailwind CSS for styling
• Lucide Icons for vector icons

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Workflow Diagram

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Setup Instructions

Prerequisites:

1. Node.js (v14+)
2. Python (v3.9+)
3. MongoDB installed locally or hosted
4. Pinecone API Key
5. Groq API Key

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Backend Setup:

1. Clone the repository:

   git clone https://github.com/SrikarVeluvali/Searchly
   cd Searchly/server

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # For Windows: venv\Scripts\activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Create a .env file with the following variables:

   GROQ_API_KEY=your_groq_api_key
   PINECONE_API_KEY=your_pinecone_api_key
   

5. Run the server:

   python app.py

6. The server will run on http://localhost:5000.

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Frontend Setup:

1. Navigate to the client folder:

   cd Searchly/client

2. Install dependencies:

   npm install

3. Run the development server:

   npm start

4. Access the application at http://localhost:3000.

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MongoDB Configuration:

• Ensure MongoDB is running locally or connect to a hosted instance.
• Update the database connection string in app.py if necessary.

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Detailed Component Overview

Backend

Key Features:

1. AI Recommendations:

   • Processes user queries with the Groq API.
   • Embeds and indexes product data using HuggingFace and Pinecone.

2. Data Management:

   • MongoDB manages user data, tags, and favorites.
   • Handles CRUD operations for favorites and tags.

3. Web Scraping:

   • Dynamically fetches product details using scrape_web.py.

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Frontend

Pages:

1. Landing Page:

   • Introduces users to Searchly's capabilities.

2. Authentication:

   • Secure login and registration via React.

3. AI Chatbot:

   • Interactive UI to converse with "Aivy" for product suggestions.

4. Product Recommendations:

   • Displays personalized product lists with sorting/filtering.

5. Favorites:

   • Displays saved products for quick access.

Components:

• AuthScreen:
  • Handles login and registration flows.
• ChatbotPage:
  • Implements the AI assistant.
• FavouritesPage:
  • Manages user favorites.
• Products:
  • Renders the recommendation grid with search and filters.

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Usage

1. Launch both the backend (http://localhost:5000) and frontend (http://localhost:3000).
2. Navigate to http://localhost:3000 to use the application.
3. Key User Actions:
   • Register/Login to create an account.
   • Interact with the chatbot to get product suggestions.
   • Add/remove products to/from your favorites.

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API Endpoints

Here is a comprehensive list of all API endpoints in the provided code:

Authentication Endpoints

• /userregister (POST): Registers a new user. Requires name, email, and password in the request body.
• /userlogin (POST): Logs in an existing user. Requires email and password in the request body.

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Recommendation Endpoints

• /recommend (POST): Handles user queries to generate AI-based product recommendations using Groq. Requires query and email in the request body.
• /recommend_from_db (POST): Similar to /recommend but focuses on finding recommendations based on previously indexed products. Requires query and email in the request body.

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Favorites Management Endpoints

• /add_favourite (POST): Adds a product to the user's favorites list. Requires email and product (product details) in the request body.
• /get_favourites (POST): Retrieves the list of favorited products for a user. Requires email in the request body.
• /remove_favourite (POST): Removes a product from the user's favorites list. Requires email and product_url (unique product URL) in the request body.

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Tag and Recommendation Retrieval Endpoints

• /get_recommendations (POST): Retrieves product recommendations based on a user's stored tags. Requires email in the request body.

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Utilities

• search_product(query) (Function): Helper function to scrape e-commerce websites (e.g., Amazon and Flipkart) for products based on a search query. This is not exposed as an endpoint but is used within /recommend and /recommend_from_db.

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Screenshots

1. Landing Page

2. AI Chatbot

3. Product Recommendations

4. Favorites Management

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Contributing

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit changes and push to your fork:

   git push origin feature-name

4. Open a pull request.

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