https://github.com/srikarveluvali/searchly
Searchly is a sophisticated AI-powered product recommendation system that leverages cutting-edge technologies to provide personalized shopping experiences. Users can interact with an AI chatbot for tailored product suggestions, save their favorite items, and explore curated recommendations.
https://github.com/srikarveluvali/searchly
agent-based-modeling e-commerce flask generative-ai groq reactjs web-scraping
Last synced: 2 months ago
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Searchly is a sophisticated AI-powered product recommendation system that leverages cutting-edge technologies to provide personalized shopping experiences. Users can interact with an AI chatbot for tailored product suggestions, save their favorite items, and explore curated recommendations.
- Host: GitHub
- URL: https://github.com/srikarveluvali/searchly
- Owner: SrikarVeluvali
- Created: 2024-11-22T02:55:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-02T02:16:36.000Z (over 1 year ago)
- Last Synced: 2025-01-23T04:41:52.999Z (over 1 year ago)
- Topics: agent-based-modeling, e-commerce, flask, generative-ai, groq, reactjs, web-scraping
- Language: Python
- Homepage:
- Size: 299 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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.
---
## Table of Contents
1. [Features](#features)
2. [Project Structure](#project-structure)
3. [Architecture Diagram](#architecture)
4. [Tech Stack](#tech-stack)
5. [Workflow Diagram](#workflow)
6. [Setup Instructions](#setup-instructions)
7. [Detailed Component Overview](#detailed-component-overview)
- [Backend](#backend)
- [Frontend](#frontend)
8. [Usage](#usage)
9. [API Endpoints](#api-endpoints)
10. [Screenshots](#screenshots)
11. [Contributing](#contributing)
---
## 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.
---
## 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
```
---
## Architecture Diagram
---
## 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
---
## Workflow Diagram
---
## 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**
---
### Backend Setup:
1. Clone the repository:
```bash
git clone https://github.com/SrikarVeluvali/Searchly
cd Searchly/server
```
2. Create a virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # For Windows: venv\Scripts\activate
```
3. Install the dependencies:
```bash
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:
```bash
python app.py
```
6. The server will run on `http://localhost:5000`.
---
### Frontend Setup:
1. Navigate to the client folder:
```bash
cd Searchly/client
```
2. Install dependencies:
```bash
npm install
```
3. Run the development server:
```bash
npm start
```
4. Access the application at `http://localhost:3000`.
---
### MongoDB Configuration:
- Ensure MongoDB is running locally or connect to a hosted instance.
- Update the database connection string in `app.py` if necessary.
---
## 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`.
---
### 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.
---
## 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.
---
## 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.
---
### 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.
---
### 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.
---
### Tag and Recommendation Retrieval Endpoints
- **`/get_recommendations`** (POST): Retrieves product recommendations based on a user's stored tags. Requires `email` in the request body.
---
### 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`.
---
## Screenshots
### 1. Landing Page
### 2. AI Chatbot
### 3. Product Recommendations
### 4. Favorites Management
---
## Contributing
1. Fork the repository.
2. Create a feature branch:
```bash
git checkout -b feature-name
```
3. Commit changes and push to your fork:
```bash
git push origin feature-name
```
4. Open a pull request.
---