Chat-GPT/Gemini - Node.js Application for PDF-based Resume Data Extraction with AI Integration

AI-powered text extraction from PDFs & documents. Supports resumes, Drive links. Free for text files, Vision API for PDFs. Fast & reliable.

Node.js + Express backend for ResuMind, an AI-powered resume intelligence platform. Handles resume and job description uploads, text extraction (PDF/DOCX), AI-driven scoring, resume–JD matching, and structured feedback APIs.

A secure RESTful API powering AI-based resume analysis workflows. The backend handles resume parsing (PDF/Text), AI-driven skill extraction, structured job requirement processing, match score computation, skill gap detection, and persistent analysis tracking. The system integrates Groq AI for structured data transformation, enforces JWT

AI-powered PDF data extraction tool using Ollama & Streamlit. Extract structured information from resumes, invoices, contracts & research papers locally with no API costs. Features include smart extraction, Q&A capability, JSON/CSV export, and complete privacy. Built with Python, LangChain & Llama 3.2.

Build an AI tool that reviews a resume and gives suggestions to improve clarity, formatting, and keywords. Features: PDF upload Skill extraction ATS scoring rewrite suggestions Tech: OpenAI API / Claude / Next.js + Tailwind

A backend-driven AI-powered web application that analyzes job descriptions, evaluates resume alignment, and provides ATS optimization insights. The system integrates external AI services via REST APIs and implements server-side processing for keyword extraction, scoring logic, and PDF parsing.

A full-stack app that streamlines technical interviews. Upload resumes (PDF/DOCX) for text extraction, generate AI-powered questions, record timed answers, batch transcribe & score responses, and track performance through an interviewer dashboard. ( API DISABLED TEMPORARILY )

Upload your resume (PDF/DOCX/Image) and get a brutally funny, dark-humor roast powered by AI. Built with Next.js, Prisma, TypeScript, PostgreSQL, Groq API, Tesseract OCR, and pdf.js. Includes temperature control, quick text copy, and automatic text extraction from multiple file types.

AI-powered CV extraction API built with FastAPI and Google Gemini. Upload any PDF resume and instantly get clean, structured JSON containing name, education, experience, skills, and more. Designed for recruitment platforms, student portals, and automation systems.

Automated system that continuously fetches resumes from Google Drive. It uses OCR/text extraction to read PDFs, queues them for processing, and employs the Gemini AI model to analyze and score each resume against a submitted Job Description (JD). The results are saved to a database for API access

Developed a Python-based AI Resume Critiquer using Streamlit and OpenAI APIs. Built document ingestion and text extraction pipelines for PDF, DOCX, RTF, TXT, and HTML using PyPDF2, python-docx, and BeautifulSoup. Implemented prompt-based NLP analysis, resume validation, scoring, and skill-gap recommendations with curated learning resources.

This AI chatbot, built with Groq API and Streamlit, delivers real-time streaming responses, stores and resumes chats via SQLite, and supports PDF text extraction and summarization, combining conversational AI, persistent storage, and document analysis in a fast, efficient, and user-friendly full-stack application.

This end-to-end AI project converts resumes (PDF/DOCX) into a personalized portfolio website. It uses Streamlit, Python, LangChain, and the Gemini API. Automates parsing, data extraction, and front-end code generation (HTML/CSS/JS) into a ZIP file.

Built an AI-based resume parsing and job matching system using NLP & ML • Implemented PDF resume ingestion, skill extraction, and similarity-based ranking • Deployed Flask REST application on cloud with real-time processing • Live Demo: https://your-app.onrender.com Tech: Python, NLP, Machine Learning, Flask, MongoDB, REST APIs, Git