{"id":48448562,"url":"https://github.com/ryomendev/vouchery","last_synced_at":"2026-04-06T19:02:35.873Z","repository":{"id":290387707,"uuid":"974011116","full_name":"RyomenDev/Vouchery","owner":"RyomenDev","description":null,"archived":false,"fork":false,"pushed_at":"2025-04-28T14:56:48.000Z","size":22922,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-28T15:39:20.640Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://vouchery-sigma.vercel.app","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RyomenDev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-28T06:02:03.000Z","updated_at":"2025-04-28T14:56:52.000Z","dependencies_parsed_at":"2025-04-28T15:39:34.085Z","dependency_job_id":"ab147b73-738d-4452-a466-a8bc899f6c67","html_url":"https://github.com/RyomenDev/Vouchery","commit_stats":null,"previous_names":["ryomendev/vouchery"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RyomenDev/Vouchery","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RyomenDev%2FVouchery","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RyomenDev%2FVouchery/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RyomenDev%2FVouchery/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RyomenDev%2FVouchery/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RyomenDev","download_url":"https://codeload.github.com/RyomenDev/Vouchery/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RyomenDev%2FVouchery/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31485516,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-06T17:22:55.647Z","status":"ssl_error","status_checked_at":"2026-04-06T17:22:54.741Z","response_time":112,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-04-06T19:02:28.376Z","updated_at":"2026-04-06T19:02:35.867Z","avatar_url":"https://github.com/RyomenDev.png","language":"JavaScript","readme":"\n\u003c!-- ![Description](./Scrape/Wroking.gif)\n\n\u003cp align=\"center\" style=\"display: flex; justify-content: center;\"\u003e\n    \u003cimg src=\"https://github.com/user-attachments/assets/e2168b85-915b-41b1-9411-e1485d46fafd\" style=\"width: 45%; height: auto; margin-right: 10px;\"\u003e\n    \u003cimg src=\"https://github.com/user-attachments/assets/a9c6d359-88e2-435e-b7e6-aa5317a3a2b1\" style=\"width: 45%; height: auto;\"\u003e\n\u003c/p\u003e\n\n![Image](https://github.com/user-attachments/assets/bfbfff03-4354-4011-876a-3d3f51077b12) --\u003e\n\n# Vouchery\n\nVouchery is a **MERN-based** Amazon Smart TV scraper that extracts product details, pricing, offers, and AI-generated review summaries. Built with **Node.js, Puppeteer, Express, React.js, and MongoDB**, it also features **Socket.IO**-based real-time coupon distribution using a round-robin algorithm. The project provides an interactive UI for seamless product data retrieval, display, and storage.\n\n\n### 1. Backend (Node.js + Express.js)\n\n- Use **Puppeteer** (with Stealth Plugin) for scraping Amazon product details, even from JavaScript-rendered pages.\n- Socket.IO implementation for real-time coupon code distribution among connected clients using a round-robin mechanism.\n- Store the extracted data in **MongoDB**.\n- Implement an API endpoint (`/scrape`) to trigger the scraper.\n\n### 2. Frontend (React.js)\n\n- Create a UI to input the Amazon product link.\n- Display scraped product details (name, price, images, offers, etc.).\n- Show an AI-generated summary of customer reviews using **Gemini API**, instead of OpenAI, as **OpenAI does not offer a free tier**.\n- Display distributed coupon codes received in real-time via WebSockets.\n\n### 3. Database (MongoDB)\n\nStore scraped product data for future reference.\n\nTech Stack:\n\n- **Backend:** Node.js, Express.js, Puppeteer, Socket.IO\n- **Frontend:** React.js, Tailwind CSS\n- **Database:** MongoDB (to store scraped data)\n- **AI Summary:** Gemini API for review summarization\n- **Api \\\u0026 Documentation:** Swagger (OPENAPI)\n\n## 🚀 Features\n\n✅ Extracts product name, price, ratings, and discount information  \n✅ Fetches bank offers, \"About This Item\" section, and product specifications  \n✅ Scrapes product images and manufacturer details  \n✅ AI-generated customer review summary (**Gemini API**)  \n✅ Interactive UI for entering and displaying scraped product details  \n✅ Stores scraped data in MongoDB for easy retrieval\n✅ Real-time coupon code distribution via Socket.IO using a round-robin algorithm\n\n### 🔍 How It Works\n\n- Enter an Amazon Smart TV product link in the UI.\n- Click Scrape to fetch product details.\n- View structured product data with AI-generated review insights.\n- Data is stored in MongoDB for future reference.\n\n## Implementation\n\n### 1. **Scraper Utility**\n\n- The `scraper` function extracts product details from a given URL.\n- Extracted data includes:\n  - Name\n  - Rating \u0026 Number of Ratings\n  - Price \u0026 Discount\n  - Bank Offers\n  - About Information\n  - Product Specifications\n  - Images \u0026 Manufacturer Images\n  - Customer Reviews\n\n### 2. **Generating Review Summary with OpenAI**\n\n- The extracted reviews are processed using OpenAI's API to generate a concise review summary.\n- Uses **Gemini** `(generateReviewSummaryGemini)` instead of OpenAI, as **OpenAI does not offer a free tier**.\n- The generated summary provides a quick insight into customer opinions.\n\n### 3. **Saving Product Data**\n\n- After scraping and processing the reviews, the product details (including the generated summary) are stored in the database using MongoDB.\n- A new product instance is created and saved asynchronously.\n\n---\n\n## HOW TO RUN\n\n### 1️⃣ Create .env File\n\nDefine your environment variables:\n\n### :clubs: A. Client\n\n```ini\nVITE_SERVER_URL=\"http://localhost:5000\"\n```\n\n### :clubs: B. Server\n\n```sh\nVITE_SERVER_URL=http://localhost:5000\nMONGO_URI=mongodb://mongodb_container:27017/scrapesmart # if using Container image\n# MONGO_URI=mongodb+srv://yourUsername:yourEncodedPassword@cluster0.mongodb.net/yourDatabase # if using cloud Db\n# # MONGO_URI=\"mongodb://localhost:27017/\" # If use locally setup DB\nPORT=5000\nSERVER_URL=\"http://localhost:5000\"\nOPENAI_API_KEY=\"sk-proj-\" # get from [Google AI for Developers](https://ai.google.dev/)\nGEMINI_API_KEY=\"AIz..\"\n\n```\n\n### 2️⃣ Run Everything\n\n```sh\ndocker-compose up --build\n```\n\n```sh\ndocker-compose down # – Stops and removes all containers, networks, and volumes defined in the docker-compose.yml file.\n```\n\n### TryOut Links\n\n- [Samsung Smartphone](https://www.amazon.in/Samsung-Smartphone-Titanium-Storage-Included/dp/B0DSKMKJV5?th=1) - https://www.amazon.in/Samsung-Smartphone-Titanium-Storage-Included/dp/B0DSKMKJV5?th=1\n- [MSI NVIDIA GeForce Ventus Graphic Card](https://www.amazon.in/MSI-NVIDIA-GeForce-Ventus-Graphic/dp/B0B5V8NT52) - https://www.amazon.in/MSI-NVIDIA-GeForce-Ventus-Graphic/dp/B0B5V8NT52\n- [ASUS Laptop (i7-13650HX)](https://www.amazon.in/ASUS-Battery-i7-13650HX-Windows-G614JU-N3200WS/dp/B0C4TVHMR9) -https://www.amazon.in/ASUS-Battery-i7-13650HX-Windows-G614JU-N3200WS/dp/B0C4TVHMR9\n- [MSI G274QPX 27-inch Gaming Monitor](https://www.amazon.in/MSI-G274QPX-Inch-Gaming-Monitor/dp/B0BSLJ9ZR7) - https://www.amazon.in/MSI-G274QPX-Inch-Gaming-Monitor/dp/B0BSLJ9ZR7\n- [Sony PlayStation 5 Slim](https://www.amazon.in/Sony-CFI-2008A01X-PlayStation%C2%AE5-Console-slim/dp/B0CY5HVDS2) - https://www.amazon.in/Sony-CFI-2008A01X-PlayStation%C2%AE5-Console-slim/dp/B0CY5HVDS2\n\n---\n\n### References\n\n- [GeeksForGeeks](https://www.geeksforgeeks.org/scraping-amazon-product-information-using-beautiful-soup/)\n- [OpenAI's website](https://platform.openai.com/)\n- [Google AI for Developers](https://ai.google.dev/)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryomendev%2Fvouchery","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fryomendev%2Fvouchery","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryomendev%2Fvouchery/lists"}