CS graduate, Applied AI and Python developer; likes working in the AI domain.

github

leetcode

M Lucky  • Peerlist

<p>This project is an AI-powered platform designed to deliver highly personalized financial advice using Retrieval-Augmented Generation (RAG). It integrates a Next.js client with a Python FastAPI server, leveraging Pinecone for vector search and a Neo4j knowledge graph. The system utilizes large language models, such as Google Gemini, to provide users with tailored and factual financial guidance based on their individual profiles and uploaded documents.</p><h3>Core Capabilities</h3><ul><li><strong>Hybrid Retrieval</strong>: Employs vector similarity search via Pinecone on chunked financial documents and structured fact lookup through a Neo4j knowledge graph.</li><li><strong>Personalized Responses</strong>: Tailors advice based on user profiles including risk tolerance, goals, and preferences, using LLM prompting.</li><li><strong>Document Ingestion</strong>: Supports PDF, CSV, and JSON file uploads, with automated chunking, embedding, and upserting to Pinecone.</li><li><strong>Knowledge Graph Visualization</strong>: Renders financial data relationships using NetworkX and Matplotlib.</li><li><strong>Comprehensive Evaluation</strong>: Utilizes DeepEval metrics for faithfulness, hallucination, and relevancy, with safeguards against API rate limits and duplicate vectors.</li></ul><h3>Client (Next.js) Features</h3><ul><li>User authentication is handled via Clerk.</li><li>Users can upload financial documents and generate reports.</li><li>Interactive charts and download options are available.</li><li>Includes documentation and best practices pages.</li></ul><h3>Server (Flask) Features</h3><ul><li>Provides a /advice endpoint for on-demand personalized advice.</li><li>Offers APIs for document processing (/process-document), user report generation (/generate-user-report), and organization report generation (/generate-organization-report).</li><li>Includes ingestion and evaluation scripts.</li></ul>

GitHub - Lucky110405/minor-2-final · GitHub

FaceMark AI is a smart classroom solution designed to automate attendance using Deep-Learning facial recognition technology. Built with Convolutional Neural Networks (CNN), the system efficiently identifies and marks student attendance in real-time using video input. By streamlining the attendance process, FaceMark AI reduces manual errors and saves valuable classroom time, paving the way for future advancements in smart education technology. This project has immense potential for expansion beyond automated attendance. Here are the envisioned features that I wish to integrate in near future: Student Behavior Analysis Personalized Learning Recommendations Scalability and Multi-Classroom Management

FaceMark AI (deep_learning_facial_recognition

What it is: PrecisionBake AI is an AI-powered web platform that makes baking easier, healthier, and more reliable. helps bakers get perfect results every time while ensuring their food meets health and taste needs. Website Link: https://precision-bake-ai-mvp-01.vercel.app/ What it provides: ➢ Precise Measurements– Converts volume-based ingredients (cups, teaspoons, etc.) into accurate grams for consistent results. ➢ Nutritional Insights– Shows calories and macros to support healthier choices. ➢ AI Baking Assistant– Helps validate and explore baking ideas using an LLM trained on baking science. Status: In active development; planned features include dietary customization and smart recipe generation, recipe personalization and image-based ingredient detection.