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| This File is a summary of AthulKrishna's Projects. | |
| Project 1: | |
| name:System Metrics Dashboard | |
| A sleek, interactive web dashboard built with **React** and **Recharts** for monitoring real-time system performance metrics like CPU usage, memory stats, and custom metric tracking. Designed to be modular, responsive, and easily integrable with various backend data sources. | |
| --- | |
| Features | |
| - 📊 **Live Charts**: Bar, Line, and Gauge charts powered by Recharts. | |
| - 🧠 **Context API**: Global state management for metrics using `MetricsContext`. | |
| - 🧩 **Modular Components**: Drop-in-ready visual components for easy scaling. | |
| - 🎯 **Overview Navigation**: One-click access to the main dashboard. | |
| - ⚙️ **Custom Hook Support**: `useSystemMetrics()` for reactive metric consumption. | |
| --- | |
| 🛠️ Tech Stack | |
| | Layer | Tech | | |
| |-------------|-------------------------| | |
| | Frontend | React.js, CSS | | |
| | Charts | Recharts | | |
| | State Mgmt | React Context API | | |
| | Backend | GoLang, MongoDB | | |
| --- | |
| Project 2: | |
| name: Trip Planner Agent Workflow | |
| Built with CrewAI Plan your perfect getaway with the power of AI agents working in synergy! This intelligent workflow features specialized agents that collaborate to design a smart, data-driven, and personalized 7-day travel itinerary. | |
| Project Overview | |
| This project leverages multiple autonomous agents to plan a trip from scratch. Each agent has a clearly defined role and utilizes real-time tools to gather information and make decisions. | |
| Agents in Action | |
| Expert Travel Agent Designs a detailed 7-day travel itinerary including: | |
| Per-day plans | |
| Estimated budget | |
| Packing suggestions | |
| Safety tips Powered by GPT-4 + Tools (Search, Calculator) | |
| City Selection Expert Analyzes travel trends, weather, seasonality, pricing, and preferences to select the best city for travel. Powered by GPT-4 + Web Search | |
| Local Tour Guide Acts as your on-ground AI guide with detailed insights into the chosen city’s: | |
| Attractions | |
| Local customs | |
| Hidden gems Powered by GPT-4 + Web Search | |
| Tech Stack | |
| CrewAI — Multi-agent framework | |
| OpenAI GPT-3.5 & GPT-4 — Natural language agents | |
| SearchTools — To fetch real-time web data | |
| CalculatorTools — To calculate trip budgets | |
| Python 3.10+ | |
| Project 3: | |
| Name: GeoRisk AI | |
| Overview | |
| This project is a demonstrating a multi-agent system that leverages IBM watsonx.ai and Retrieval-Augmented Generation (RAG) to provide climate-aware risk analysis for business risk. | |
| It enables businesses to make informed decisions by assessing weather risks and historical climate data relevant to key supply chain points. | |
| Features | |
| Multi-agent architecture with specialized agents for: | |
| Location intelligence | |
| Climate data retrieval | |
| Business risk analysis | |
| Orchestrator agent to coordinate tasks | |
| IBM watsonx.ai for advanced AI-driven chat and recommendations | |
| RAG (Retrieval-Augmented Generation) to use historical climate and location data | |
| Interactive web interface: | |
| Chat panel for weather queries and supply chain risk advice | |
| Map component for selecting global locations | |
| Backend API to manage communication between frontend and agents | |
| Tech Stack | |
| Frontend: React.js | |
| Backend: Flask | |
| AI & NLP: IBM watsonx.ai | |
| Data Retrieval: RAG framework | |
| Map Integration: React map component with location picker | |
| HTTP Client: Axios | |
| project 4: | |
| Name: Ai-Conversation | |
| 🤖 GPT vs LLaMA: The Chatbot Showdown | |
| A fun Python project where GPT-4o-mini (snarky and argumentative) and LLaMA 3 via Groq (polite and diplomatic) engage in a back-and-forth conversation. Think of it as a polite librarian chatting with an overly caffeinated debate club member. | |
| project 5: | |
| name: Webscraping using BeautifulSoup | |
| A simple yet powerful Python project that demonstrates web scraping using BeautifulSoup. This project showcases how to extract useful information from websites like quotes, news, products, or custom data — turning unstructured web content into structured data. | |
| Features: | |
| Scrapes data from static web pages | |
| Parses HTML content using BeautifulSoup | |
| Supports CSV/JSON export of scraped data | |
| Clean and modular codebase | |
| Customizable scraping logic | |
| Requirements: | |
| Python 3.7+ | |
| requests | |
| beautifulsoup4 | |
| Project 6: | |
| name: Mandt - E-commerce Perfume Store | |
| Welcome to Mandt, a full-stack e-commerce application designed for perfume enthusiasts! This website allows users to browse and purchase a variety of perfumes for men, women, and unisex. Built with React.js for the frontend and Node.js with Express.js for the backend, Mandt leverages MySQL for efficient database storage. | |
| Features | |
| User-Friendly Interface: Browse through an extensive collection of perfumes with ease. | |
| Product Selection: Choose from a variety of options tailored for men, women, and unisex fragrances. | |
| Secure Ordering: Place your orders securely through our streamlined checkout process. | |
| Responsive Design: Fully responsive design ensures a seamless experience on all devices. | |
| Tech Stack | |
| Frontend: React.js | |
| Backend: Node.js, Express.js | |
| Database: MySQL | |
| project 7: | |
| name:Verdicta - AI Legal Assistant | |
| Verdicta is an advanced AI-powered legal assistant designed to assist with document analysis, contract review, legal research, and compliance checking. It integrates NLP, machine learning, and deep learning techniques to provide customized legal insights. 🚀 | |
| Features: | |
| Chatbot Interface: Ask legal questions and receive AI-generated responses. | |
| PDF & Image Upload: Extracts and analyzes text from legal documents. | |
| RAG-based Search: Retrieves relevant legal documents for context-aware answers. | |
| Emotion Recognition: Understands user emotions for better interaction. | |
| Local & Secure: Runs LLaMA 3 locally on a Lenovo Legion laptop for privacy. | |
| Tech Stack | |
| Frontend: React, Vite, React Markdown | |
| Backend: Python, Flask, LLaMA 3, ChromaDB | |
| Libraries: pdf.js, Tesseract.js, OpenAI API (Groq) | |
| Installation | |
| Prerequisites | |
| Node.js & npm | |
| Python 3.x | |
| Setup Instructions | |
| Clone the repository | |
| Install frontend dependencies | |
| Install backend dependencies | |
| Start the backend server | |
| Start the frontend server | |
| Access the app at http://localhost:3000 | |
| API Endpoints | |
| POST /query | |
| Description: Processes user questions and returns AI-generated legal responses. | |
| Request: JSON containing the user query. | |
| Response: AI-generated legal answer. | |
| Contributing | |
| We welcome contributions! Feel free to fork, submit pull requests, and open issues. | |
| License | |
| MIT License | |
| Authors | |
| Athul - RAG Specialist & Project Lead |