Smart Application Tracker

A modern web-based job application tracking platform built with Flask that helps users organize, monitor, and analyze their job search process.

Track applications across multiple platforms, manage interviews and follow-ups, visualize application trends through analytics dashboards, and streamline your entire job search workflow from one place.

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🚀 Features

🔐 Authentication & Security

• Secure user registration and login
• Google OAuth authentication
• Password hashing and secure session management
• Password reset via email
• Terms & Conditions and Privacy Policy pages

📋 Application Management

• Add, edit, and manage job applications

• Support for:

  • Jobs
  • Internships
  • Contracts
  • Freelance opportunities
  • Programs

• Track application status throughout the hiring process

• Archive applications without permanent deletion

• Store recruiter and contact information

• Manage salary details, notes, office locations, and application links

📊 Analytics Dashboard

• Application status distribution
• Platform-wise application tracking
• Work type analytics
• Application type analytics
• Response rate tracking
• Interview rate tracking
• Offer rate tracking
• Application timeline visualization

🔄 Pipeline View

• Kanban-style application pipeline
• Drag-and-drop workflow visualization
• Quickly identify application bottlenecks
• Track progress from application to offer

🔍 Productivity Features

• Search and filter applications
• Priority management (High / Medium / Low)
• Follow-up date tracking
• Days remaining calculations
• Status-based application management
• Responsive user interface

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📸 Screenshots

Login Page

Registration Page

Dashboard Overview

Analytics Dashboard

Application Timeline Analytics

Pipeline View

Application Details

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🏗️ Project Architecture

The application follows a layered architecture for maintainability and scalability.

Presentation Layer
│
├── HTML Templates
├── CSS
├── JavaScript
│
Business Logic Layer
│
├── Flask Routes
├── Application Manager
│
Data Layer
│
├── SQLAlchemy Models
├── SQLite Database
│
Authentication Layer
│
├── Flask-Login
├── Google OAuth
├── Email Authentication

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📁 Project Structure

smart-application-tracker/
│
├── models/
│   ├── user.py
│   └── job_application.py
│
├── services/
│   └── application_manager.py
│
├── static/
│   ├── css/
│   │   └── styles.css
│   │
│   └── images/
│       ├── favicon.png
│       └── sat_logo.png
│
├── templates/
│   ├── login.html
│   ├── register.html
│   ├── analytics.html
│   ├── pipeline.html
│   └── ...
│
├── screenshots/
│   ├── dashboard-overview.png
│   ├── analytics-overview.png
│   ├── application-details.png
│   └── ...
│
├── app.py
├── forms.py
├── oauth.py
├── extensions.py
├── utils.py
├── requirements.txt
├── .gitignore
└── README.md

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🛠️ Tech Stack

Backend

• Python
• Flask
• SQLAlchemy
• Flask-WTF / WTForms
• Flask-Login
• Flask-Mail
• Authlib (Google OAuth)
• Jinja2

Frontend

• HTML5
• CSS3
• Bootstrap 5
• JavaScript
• jQuery
• AJAX
• DataTables
• Chart.js
• Choices.js

Database

• SQLite
• SQLAlchemy ORM

Authentication

• Email & Password Authentication
• Google OAuth 2.0

Deployment

• Ready for deployment on Render / Railway / PythonAnywhere

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⚙️ Installation

Clone the Repository

git clone https://github.com/RawPhoenix/smart-application-tracker.git

cd smart-application-tracker

Create Virtual Environment

python -m venv .venv

Activate it:

Windows

.venv\Scripts\activate

Linux / macOS

source .venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file:

SECRET_KEY=your_secret_key

MAIL_USERNAME=your_email
MAIL_PASSWORD=your_password

GOOGLE_CLIENT_ID=your_google_client_id
GOOGLE_CLIENT_SECRET=your_google_client_secret

Run the Application

python app.py

Open:

http://127.0.0.1:5000

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📊 Supported Application Statuses

• Applied
• Interview
• Offer
• Rejected
• Withdrawn

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💡 Key Design Decisions

Archive Instead of Delete

Applications are archived rather than permanently removed, reducing accidental data loss.

User-Centric Analytics

Analytics are calculated per user, providing personalized insights into application performance.

Service Layer Separation

Business logic is separated from route handling to improve maintainability and scalability.

Responsive Design

The interface is optimized for desktop and tablet devices while maintaining usability across screen sizes.

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🚀 Future Improvements

• Resume upload and management
• Resume parsing and automated skill extraction
• AI-powered Resume–Job Description Matching System
• Semantic similarity scoring using Transformer-based NLP models
• Skill-gap analysis and personalized improvement suggestions
• Job recommendation engine based on resume skills and application history
• Interview scheduling and calendar integration
• Email reminders for upcoming follow-ups and deadlines
• Export applications and analytics reports as PDF/CSV
• PostgreSQL migration and cloud-scale deployment

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👨‍💻 Author

Jithesh Shetty

B.Tech Computer Science Engineering (AI & ML)

Passionate about building practical software solutions and exploring AI/ML technologies.

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⭐ Feedback

If you found this project useful, consider giving the repository a star ⭐

Suggestions, improvements, and contributions are always welcome.