ATS — AI Resume Architect

Intelligent resume analysis powered by Google Gemini 2.5 Flash

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📋 Table of Contents

• Overview
• Features
• Architecture
• Tech Stack
• Getting Started
  • Prerequisites
  • Installation
  • Configuration
  • Running the App
• API Reference
• Project Structure
• Screenshots
• Contributing
• License

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🔍 Overview

ATS (AI Resume Architect) is a full-stack web application that leverages Google's Gemini 2.5 Flash AI model to intelligently analyze resumes against job descriptions. It acts as a smart Applicant Tracking System that provides candidates with actionable insights to optimize their resumes for better job matching.

Upload your resume, paste a job description, and instantly receive a detailed compatibility report — complete with match percentage, skill gap analysis, strengths, and personalized improvement suggestions.

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✨ Features

Feature	Description
🤖 AI-Powered Parsing	Uses Gemini 2.5 Flash to intelligently extract skills, experience, education, and tools from resumes
📄 PDF Resume Upload	Drag-and-drop or click-to-upload PDF resume support
🎯 ATS Match Scoring	Generates a 0–100 match percentage between resume and job description
✅ Skill Gap Analysis	Identifies matching skills and highlights missing ones
💪 Strengths Report	Highlights candidate strengths relative to the role
🚀 Improvement Suggestions	Provides actionable recommendations to improve resume fit
🎨 Cinematic UI	Stunning dark-themed interface with particle animations, glassmorphism, and smooth transitions
📱 Responsive Design	Fully responsive layout that works across desktop, tablet, and mobile

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🏗 Architecture

┌─────────────────────────────────────────────────────────┐
│                    CLIENT (Browser)                     │
│                                                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │  PDF Upload   │  │  JD Input    │  │  Results UI  │  │
│  └──────┬───────┘  └──────┬───────┘  └──────▲───────┘  │
│         │                 │                  │          │
│         └────────┬────────┘                  │          │
│                  │ POST /analyze             │          │
└──────────────────┼───────────────────────────┼──────────┘
                   │                           │
                   ▼                           │
┌──────────────────────────────────────────────┤──────────┐
│                 FLASK SERVER                 │          │
│                                              │          │
│  ┌────────────┐  ┌────────────┐  ┌──────────┴───────┐  │
│  │ PDF Parser │  │ Resume     │  │  JSON Response    │  │
│  │ (PyPDF2)   │──│ Parser     │──│  Builder          │  │
│  └────────────┘  │ (Gemini)   │  └──────────────────┘  │
│                  └─────┬──────┘                         │
│                        │                                │
│  ┌─────────────┐  ┌────┴──────┐                        │
│  │ JD Parser   │──│ ATS Match │                        │
│  │ (Gemini)    │  │ Engine    │                        │
│  └─────────────┘  │ (Gemini)  │                        │
│                   └───────────┘                        │
└────────────────────────────────────────────────────────┘
                        │
                        ▼
            ┌───────────────────────┐
            │   Google Gemini API   │
            │   (2.5 Flash Model)   │
            └───────────────────────┘

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🛠 Tech Stack

Layer	Technology	Purpose
Backend	Python, Flask	REST API server, request handling, file management
AI Engine	Google Gemini 2.5 Flash	Resume parsing, JD analysis, ATS matching
PDF Processing	PyPDF2	Text extraction from uploaded PDF resumes
Frontend	HTML5, Tailwind CSS, JavaScript	Responsive UI with cinematic design
Icons	Lucide Icons	Beautiful, consistent iconography
Markdown	Marked.js	Rendering AI-generated markdown responses

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🚀 Getting Started

Prerequisites

• Python 3.8+ — Download
• pip — Python package manager (included with Python)
• Google Gemini API Key — Get your API key

Installation

1. Clone the repository

   git clone https://github.com/your-username/ATS.git
   cd ATS

2. Create a virtual environment

   python -m venv .venv

3. Activate the virtual environment

   • Windows:

     .venv\Scripts\activate

   • macOS/Linux:

     source .venv/bin/activate

4. Install dependencies

   pip install flask google-genai PyPDF2

Configuration

Open main.py and replace the API key with your own Google Gemini API key:

client = genai.Client(api_key="YOUR_GEMINI_API_KEY_HERE")

Running the App

python main.py

The application will start on http://localhost:8080

 * Running on http://127.0.0.1:8080
 * Debug mode: on

Open your browser and navigate to http://localhost:8080 to start analyzing resumes! 🎉

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📡 API Reference

POST /analyze

Analyzes a resume against a job description using AI.

Request:

Parameter	Type	Location	Required	Description
resume	File (PDF)	Form Data	✅	The candidate's resume in PDF format
job_description	String	Form Data	✅	The target job description text

Response:

{
  "parsed_resume": "• Skills: Python, Flask, ...\n• Experience: ...",
  "parsed_job_description": "• Required Skills: ...\n• Responsibilities: ...",
  "ats_result": "### Match Percentage: 78%\n### Matching Skills: ...\n### Missing Skills: ..."
}

Error Responses:

Status	Message
400	Resume PDF is required
400	Job description is required

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📁 Project Structure

ATS/
├── main.py                 # Flask application & API routes
├── README.md               # Project documentation
├── templates/
│   ├── index.html          # Frontend template (alternate)
│   └── index1.html         # Main frontend template (active)
├── uploads/                # Uploaded PDF resumes (auto-created)
└── .venv/                  # Python virtual environment

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🖼 Screenshots

🏠 Landing Page — Click to expand

A cinematic dark-themed landing page with particle animation background, glassmorphism panels, and a clean two-column layout for resume upload and job description input.

📊 Analysis Results — Click to expand

Three-column results dashboard showing parsed resume profile, ATS scorecard with match percentage, and detailed job analysis — all rendered with rich markdown formatting and color-coded sections.

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create your feature branch

   git checkout -b feature/amazing-feature

3. Commit your changes

   git commit -m "feat: add amazing feature"

4. Push to the branch

   git push origin feature/amazing-feature

5. Open a Pull Request

Development Guidelines

• Follow PEP 8 for Python code
• Use meaningful commit messages following Conventional Commits
• Add docstrings to new functions
• Test your changes locally before submitting

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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_{Built with ❤️ and powered by Google Gemini AI}

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