RecruitGenie – AI-Powered Resume Screening & Candidate Insights

An end-to-end full-stack recruitment intelligence platform built with FastAPI, Next.js, and AI-driven scoring engines. RecruitGenie automates resume parsing, scoring, shortlisting, interview question generation, and analytics.

Features

AI-Driven Resume Processing

•	Extracts candidate information (name, email, phone, skills).
•	Computes base score, skill score, penalty, and overall score.
•	Identifies missing skills and found skills.
•	Generates tailored interview questions automatically.

Resume Upload Portal

•	Upload .pdf, .docx, or .txt resumes.
•	Auto-processed and stored.
•	Results instantly displayed.

Candidate Management Dashboard

•	View all processed candidates.
•	Filter by job ID or status.
•	Update status (shortlisted, review, reject) directly.
•	Add notes per candidate.
•	Open raw JSON detail.

Analytics Dashboard

•	Total candidates
•	Average score
•	Distribution by status
•	Top missing skills
•	Data visualizations (frontend)

CSV-Based Persistence

•	All candidate data stored in backend/assets/candidate_data.csv
•	Auto-created if missing

Download Uploaded Resume

•	Backend endpoint to download original uploaded resume files

Tech Stack

Frontend — Next.js 16 (App Router) • React + TypeScript • Client-side fetching • Reusable pages: /upload, /candidates, /analytics • Environment-based API routing

Backend — FastAPI • Async endpoints • Multipart resume upload • CSV-based data layer • Resume parsing agent • Scoring engine • Interview question generator • CORS enabled for Next.js

AI / Agents • ResumeAgent • ScoringAgent • InterviewAgent • DataAgent (CSV writer)

Folder Structure

RecruitGenie/
│
├── backend/
│   ├── agents/
│   │   ├── resume_agent.py
│   │   ├── scoring_agent.py
│   │   ├── interview_agent.py
│   │   ├── data_agent.py
│   ├── assets/
│   │   ├── resumes/
│   │   ├── candidate_data.csv
│   │   └── job_description.txt
│   ├── main.py
│   ├── recruitgenie_app.py
│   ├── requirements.txt
│
├── recruitgenie-frontend/
│   ├── app/
│   │   ├── upload/page.tsx
│   │   ├── candidates/page.tsx
│   │   ├── analytics/page.tsx
│   │   ├── layout.tsx
│   ├── public/
│   ├── package.json
│   ├── next.config.js
│   └── .env
│
└── README.md

Backend Setup (FastAPI)

1️⃣ Create & activate virtual environment

python3 -m venv backend/venv
source backend/venv/bin/activate

2️⃣ Install dependencies

pip install -r backend/requirements.txt

3️⃣ Run FastAPI server

uvicorn backend.main:app --reload --port 8001

Frontend Setup (Next.js)

1️⃣ Navigate to frontend

cd recruitgenie-frontend

2️⃣ Install dependencies

npm install

3️⃣ Create .env file

NEXT_PUBLIC_API_BASE=http://localhost:8001

4️⃣ Run development server

npm run dev

Key API Endpoints

▶ Upload Resume

POST /upload_resume/?job_id=JOB_01

▶ Get All Candidates

GET /candidates/

▶ Update Status

PATCH /candidates/{id}/status

▶ Update Notes

PATCH /candidates/{id}/notes

▶ Analytics Summary

GET /analytics/summary

▶ Download Uploaded Resume

GET /resumes/file/{filename}

How Scoring Works

Each resume goes through: 1. ResumeAgent → Extract text + contact details 2. ScoringAgent → • base_score • skill_score • penalty • missing_skills • found_skills • total_score 3. InterviewAgent → Generates questions based on missing vs found skills 4. DataAgent → Writes a new row to CSV and assigns: shortlisted, review, or reject

Future Enhancements

•	OAuth2 login for recruiters
•	Multi-job descriptions
•	MongoDB/PostgreSQL persistence
•	AI-powered skill extraction
•	Resume similarity ranking
•	PDF previewer in frontend

Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss.

License

Licensed under the MIT License, allowing full flexibility to reuse, modify, distribute, and integrate this project into personal or commercial applications. Attribution is required.

Contact

If you have questions, suggestions, or collaboration ideas, feel free to reach out at: 📩 akashgpatil23.05@gmail.com