Projects

GitHub

A showcase of my personal and technical projects.

Project Gallery

ClearBill.AI: Explaining Medical Bills with AI and RAG
ClearBill.AI: Explaining Medical Bills with AI and RAG
July 2025Completed
GitHubDocs
An AI-powered chatbot that uses Retrieval-Augmented Generation (RAG) with Astra DB, LangChain, and Hugging Face’s Llama-3.1-8B-Instruct to help users understand medical bills with clear, context-aware responses.
  • Built with Next.js and TypeScript, featuring a sleek, responsive chat UI for seamless user interaction
  • Retrieves answers using semantic search over a vector database populated with scraped healthcare resources
  • Uses @xenova/transformers for local embedding and LangChain for intelligent text processing
  • Runs LLM inference with Llama-3.1-8B-Instruct via Hugging Face API with context injection
  • Includes a custom script to scrape, embed, and load structured healthcare content into Astra DB
  • CI/CD pipeline using GitHub Actions for automatic linting and builds on push
📚RAG🤖LLM🧠LangChain📦Next.js🔷TypeScript🔎Vector Search🗄️Astra DB🩺Healthcare AI🕸️Web Scraping🎭Puppeteer🤗Hugging Face🌬️Mistral🦙Llama-3
SentiSync: Real-Time YouTube Sentiment Analysis with MLOps
SentiSync: Real-Time YouTube Sentiment Analysis with MLOps
July 2025Completed
GitHubDocs
A full MLOps pipeline and Chrome extension for real-time sentiment analysis on YouTube comments, using Flask, MLflow, Docker, and AWS.
  • Chrome Extension scrapes YouTube comments and fetches real-time sentiment insights from the backend
  • End-to-end MLOps workflow using DVC for reproducibility and MLflow for experiment tracking
  • Flask backend deployed via Docker on AWS EC2, with GitHub Actions-based CI/CD pipeline
  • Supports sentiment prediction, word cloud generation, and trend visualizations through API endpoints
  • Includes a suite of Jupyter notebooks for model experimentation, evaluation, and ensemble learning
⚙️MLOps📊MLflow🔁DVC🐳Docker☁️AWS🍶Flask🌐Chrome Extension💬Sentiment Analysis📝NLP🌲LightGBM
IntelliCrawl: Autonomous AI Research Agent for Developer Tools
IntelliCrawl: Autonomous AI Research Agent for Developer Tools
July 2025Completed
GitHubDocs
An advanced autonomous research agent that uses LLMs and web crawling to analyze and compare developer tools. Built with LangGraph, LangChain, and Firecrawl for scalable, multi-step analysis workflows.
  • Supports batch, single, and interactive CLI modes with customizable output (text, markdown, JSON, CSV)
  • Uses LangGraph for async, multi-step workflows and LangChain for LLM orchestration
  • Scrapes and analyzes developer tool websites using Firecrawl and GPT-4o-mini
  • Implements persistent caching with diskcache and .env-configured API keys
  • Includes a secondary lightweight agent using Firecrawl MCP and GPT-4.1-nano
🤖LLM🧠LangChain🧩LangGraph🌐OpenAI🔥Firecrawl💻CLI🕵️‍♂️AI Agents
LaTeX Resume Template
LaTeX Resume Template
July 2025Completed
GitHubDocs
A clean, ATS-friendly, and modern LaTeX resume template built for students and developers applying to technical roles. Features minimalist design, modular macros, and PDF outputs optimized for readability and machine parsing.
  • Modular LaTeX macros for easy customization and consistency across sections
  • Single-page layout with bolded technologies and quantifiable bullet points
  • Fully ATS-compatible PDF output using Unicode embedding and semantic structure
  • Includes Overleaf support, documentation, and contribution guidelines
  • Open-source under MIT License, ready to fork and adapt
📄LaTeX🧾Resume✅ATS🖨️PDF👐Open Source
FaceTrack: Smart Face Recognition Attendance System
FaceTrack: Smart Face Recognition Attendance System
July 2025Completed
GitHub
A full-stack web application that automates attendance tracking using facial recognition. Built with FastAPI, React, and SQLite, it supports real-time webcam input and batch image uploads for seamless attendance logging.
  • Real-time face recognition via webcam and batch image uploads
  • FastAPI backend with REST API endpoints and SQLite integration
  • React frontend with live feedback, image previews, and attendance viewer
  • OpenCV and face_recognition for robust face encoding and matching
  • Interactive database-backed attendance logs with export support
👁️Computer Vision📷OpenCV⚡FastAPI⚛️React🧬face_recognition💾SQLite
CodePrep.AI: AI-Powered Coding Interview Prep with LLMs
CodePrep.AI: AI-Powered Coding Interview Prep with LLMs
July 2025Completed
GitHubArchitecture
An interactive full-stack web application that generates multiple-choice coding challenges using Meta-Llama-3-8B-Instruct. Designed for interview practice, daily challenge tracking, and progress feedback with real-time explanations.
  • Dynamic challenge generation using Meta-Llama-3-8B-Instruct from Hugging Face
  • Clerk authentication with quota tracking and personalized history view
  • Full-stack system with React (Vite), FastAPI, and SQLite
  • Support for challenge explanation, user feedback, and difficulty selection
🤖LLM📈AI Interview Prep⚡FastAPI⚛️React🤗Hugging Face💾SQLite
DDPG: Deep Deterministic Policy Gradient Reimplementation in TensorFlow 2.x
DDPG: Deep Deterministic Policy Gradient Reimplementation in TensorFlow 2.x
July 2025Completed
GitHubArchitecture DiagramOriginal Paper
A robust, modular, and extensible reimplementation of the DDPG reinforcement learning algorithm (Lillicrap et al., 2015) using TensorFlow 2.x. Built for reproducibility, research comparison, and interactive experimentation with Gym environments.
  • Modular architecture: separate agent, networks, noise, buffer, wrappers
  • Hyperparameter sweeps, YAML config system, and TensorBoard logging
  • Includes full unit test suite and reproducibility controls (seed, version logging)
  • Supports Gym ≥ 0.26, Apple Silicon acceleration (tensorflow-macos, -metal)
🎯Reinforcement Learning🧠DDPG🔢TensorFlow🏋️OpenAI Gym
ACME10-HE-RAGApp: RAG with Vector Search, Knowledge Graphs, and LLMs
ACME10-HE-RAGApp: RAG with Vector Search, Knowledge Graphs, and LLMs
May 2025Completed
GitHubFinal Report
A full-stack Retrieval-Augmented Generation (RAG) application developed for HackerEarth that integrates vector search (FAISS), knowledge graphs (DBpedia), and OpenAI’s LLM to generate traceable, context-rich answers from a Wikipedia-based knowledge base.
  • Developed for HackerEarth using 10,000+ Wikipedia articles
  • FAISS-based vector search for semantic retrieval
  • Knowledge graph context enhancement with DBpedia and SPARQL
  • OpenAI LLM for coherent and natural language answers
  • Dockerized deployment with local dev option
🤖LLM📚RAG🔎Vector Search🗺️Knowledge Graph🛠️Full-Stack📝NLP