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AI_Resume_Scanner

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🚀 MATCH.AI — AI Resume Screener & Feedback System

GitHub Repository

FastAPI

Google Gemini

Project 1 — AI & Generative AI Fellowship Program @ Zeppelin Lab

An enterprise-grade, deterministic AI Resume Screener & Structured Feedback System built for high-accuracy recruitment screening. Powered by Google Gemini Flash, strictly typed with Pydantic, served via FastAPI, and styled with a 3D Glassmorphism Executive Studio Dashboard.

📋 Project Overview

MATCH.AI is an AI-powered resume screening system that:

Accepts candidate resumes (PDF, DOCX, TXT) and job descriptions
Uses Google Gemini Flash AI to perform structured, deterministic evaluations
Returns validated JSON assessments with match scores, matched/missing requirements, and actionable suggestions
Includes an Evidence Grounding Verifier to prevent AI hallucinations
Features a premium 3D interactive web dashboard with dark/light themes

🛠️ Tech Stack

Layer	Technology
Language	Python 3.10+
Backend Framework	FastAPI + Uvicorn
AI/LLM Engine	Google Gemini Flash (REST API)
Schema Validation	Pydantic AI (Structured JSON Output)
Frontend	HTML5, CSS3, Vanilla JavaScript (3D Glassmorphism UI)
Gradio Interface	Gradio 5.12.0 (Hugging Face Spaces deployment)
PDF Extraction	PyPDF2
DOCX Extraction	python-docx
Testing	Pytest (5/5 automated tests)
CI/CD	GitHub Actions
Deployment	Hugging Face Spaces, Docker
Version Control	Git + GitHub (branching: main, dev, feature/*)

✨ Features & Architecture

mermaid

graph LR
    A[Candidate Resume PDF / DOCX / TXT] --> C[FastAPI Scanner /api/scan]
    B[Job Description Spec] --> C
    C --> D[PDF / DOCX / TXT Extractor Engine]
    D --> E[Gemini Flash temperature=0.0]
    E --> F[Pydantic Schema Validation]
    F --> G[Evidence Grounding Verifier]
    G --> H[3D Interactive Studio Dashboard]

Deterministic AI Scoring Engine (temperature = 0.0): Evaluates candidates against job descriptions without LLM hallucinations or variance.
Strict Pydantic Output Validation: Enforces valid JSON structure (Assessment schema) with scores (0–100), rationale, matched requirements, skill gaps, and suggestions.
Evidence Grounding Verifier: Token-based semantic overlap analysis to verify every AI claim against actual resume text.
3D Glassmorphism Executive Dashboard: Dual Aurora Light & Obsidian Dark Themes, 3D particle canvas, perspective mouse tilt effects.
Multi-Format Document Support: Supports .pdf, .docx, .doc, and .txt files.

🚀 Setup Steps

Prerequisites

Python 3.10 or higher
Git
A Google Gemini API Key (Get one free)

1. Clone the Repository

bash

git clone https://github.com/majidali1256/ai-resume-screener.git
cd ai-resume-screener

2. Create Virtual Environment & Install Dependencies

bash

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

3. Configure Environment Variables

bash

cp .env.example .env
# Edit .env and add your Gemini API Key:
# GEMINI_API_KEY=your_api_key_here
# AI_MODEL=gemini-2.0-flash

⚠️ Security Note: .env is excluded from version control via .gitignore. Never commit API keys.

4. Run the Server

uvicorn main:app --host 0.0.0.0 --port 8000

Open http://localhost:8000 to launch the 3D Executive Studio Dashboard!

5. Run Tests

pytest tests/

🔌 API Reference

`POST /api/scan`

Upload a candidate resume and job description to get a complete JSON assessment.

Request (Multipart Form Data):

resume_file: .pdf, .docx, .doc, or .txt file
jd_file (optional): Job Description file
jd_text (optional): Raw job description text

Response Schema:

json

{
  "match_score": 85,
  "score_rationale": "Strong alignment across Python, FastAPI, and React...",
  "matched_requirements": ["5+ years Python experience", "FastAPI expertise"],
  "missing_requirements": ["GraphQL experience"],
  "suggestions": ["Quantify accomplishments with specific metrics"],
  "limitations": []
}

`GET /api/health`

Returns service health status.

`GET /api/demo`

Runs a sample scan using built-in benchmark data.

📁 Project Structure

ai-resume-screener/
├── main.py                    # FastAPI server entry point
├── app.py                     # Gradio interface (Hugging Face Spaces)
├── resume_scanner/
│   ├── assessor.py            # Gemini Flash AI assessment engine
│   ├── extractor.py           # PDF/DOCX/TXT text extraction
│   ├── grounding.py           # Evidence grounding verifier
│   └── models.py              # Pydantic Assessment schema
├── static/
│   └── index.html             # 3D Glassmorphism Executive Dashboard
├── data/
│   ├── benchmark/             # Golden evaluation benchmark library
│   └── uploads/               # Temporary upload storage (gitignored)
├── tests/
│   └── test_screener.py       # Automated pytest suite
├── .github/
│   └── workflows/ci.yml       # GitHub Actions CI pipeline
├── requirements.txt           # Python dependencies
├── Dockerfile                 # Docker deployment config
├── .env.example               # Environment variable template
├── .gitignore                 # Security: excludes .env, uploads, caches
└── README.md                  # This file

🔒 Security & Privacy

API keys are stored in .env (never committed — excluded by .gitignore)
Uploaded resumes are processed in-memory and cleaned up immediately after scanning
No PII storage: The system does not persist any candidate personal data

🌐 Live Deployment

Platform	Link
Live Demo (Hugging Face Space)	AI-Resume-Screener-Studio
GitHub Repository	ai-resume-screener
Hugging Face Model Card	AI_Resume_Scanner

👨‍💻 Team & Roles

Role	Name	GitHub
Project Lead & Full-Stack Developer	Majid Ali	@majidali1256

Responsibilities:

AI/LLM Engine Development (Gemini Flash integration, structured prompting)
Backend Architecture (FastAPI REST API, Pydantic validation)
Frontend Development (3D Glassmorphism Dashboard, Gradio interface)
Evidence Grounding Verifier module
Testing & CI/CD (Pytest suite, GitHub Actions)
Deployment (Hugging Face Spaces, Docker)

📄 License

This project is licensed under the MIT License.

majidali1256/AI_Resume_Scanner

🚀 MATCH.AI — AI Resume Screener & Feedback System

📋 Project Overview

🛠️ Tech Stack

✨ Features & Architecture

🚀 Setup Steps

Prerequisites

1. Clone the Repository

2. Create Virtual Environment & Install Dependencies

3. Configure Environment Variables

4. Run the Server

5. Run Tests

🔌 API Reference

POST /api/scan

GET /api/health

GET /api/demo

📁 Project Structure

🔒 Security & Privacy

🌐 Live Deployment

👨‍💻 Team & Roles

📄 License

majidali1256/
AI_Resume_Scanner

`POST /api/scan`

`GET /api/health`

`GET /api/demo`