ResumeAIQ: AI-Powered Resume Analysis

ResumeAIQ is a full-stack application that leverages Google's Gemini AI to analyze resumes, provide ATS scoring, answer questions about the candidate, and perform job matching.

Features

• Resume Parsing: Supports PDF and DOCX formats.
• ATS Scoring: Evaluates resume structure, keywords, experience impact, and readability.
• AI Summary: Generates concise, HR-friendly summaries using Gemini AI.
• Q&A Chat: RAG-based chat interface to ask questions about the uploaded resume.
• Job Matching: Compares the resume against a provided job description and offers improvement recommendations.
• Modern UI: Glassmorphism-themed frontend built with React, Vite, and Tailwind-free custom CSS.

Tech Stack

• Backend: FastAPI, Python, LangChain (Concepts), FAISS, Google Generative AI SDK.
• Frontend: React, TypeScript, Vite, Redux Toolkit, Framer Motion, Axios.
• Containerization: Docker, Docker Compose, Nginx.

Prerequisites

• Docker and Docker Compose
• Node.js (for local frontend dev)
• Python 3.11+ (for local backend dev)
• Google Gemini API Key (Get one at Google AI Studio)

Getting Started

1. using Docker (Recommended)

Run the entire stack with a single command:

docker-compose up --build

• Frontend: http://localhost:3000
• Backend: http://localhost:8000/docs (Swagger UI)

2. Running Locally

Backend

1. Navigate to backend/:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\Activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the server:

   uvicorn app.main:app --reload

Frontend

1. Navigate to frontend/:

   cd frontend

2. Install dependencies:

   npm install

3. Run the dev server:

   npm run dev

Workflow & Linting

We have defined workflows for maintaining code quality.

To Lint the Project: Use the agent workflow command or manually run:

• Backend: flake8 .
• Frontend: npm run lint

See .agent/workflows/lint.md for details.