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A comprehensive web application built with Python and Streamlit to optimize your job search process. Track applications, analyze patterns, generate cover letters, and never miss a follow-up!

• 📊 Dashboard: Visual overview of your job search progress
• ➕ Application Management: Add, edit, and track job applications
• 📈 Analytics: Detailed insights into your application patterns
• ⏰ Follow-up Management: Never miss important follow-ups

• 🔗 Auto-Fill from URLs: Extract job information from posting URLs
• 📄 Cover Letter Generator: AI-powered cover letters with multiple templates
• 📧 Follow-up Email Templates: Professional follow-up emails
• 📊 Rejection Analysis: Learn from rejection patterns to improve

• 💾 SQLite Database: Local data storage for privacy
• 📥 Excel Export: Export your data for external analysis
• 📋 Filtering & Search: Find applications quickly
• 📱 Responsive Design: Works on desktop and mobile

• Python 3.7+
• pip

1. Clone or download this project

   git clone <repository-url>
   cd job-application-tracker

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   streamlit run app.py

4. Open your browser Navigate to http://localhost:8501

• Frontend: Streamlit (Python web framework)
• Database: SQLite (local file database)
• Visualization: Plotly (interactive charts)
• Data Processing: Pandas
• Web Scraping: BeautifulSoup + Requests
• Deployment: Streamlit Cloud (free tier)

job-application-tracker/
├── app.py                  # Main Streamlit application
├── database.py            # SQLite database operations
├── job_utils.py           # Job scraping and auto-fill utilities
├── requirements.txt       # Python dependencies
├── .streamlit/
│   └── config.toml        # Streamlit configuration
└── README.md             # This file

• Use "Add Application" from the sidebar
• Optionally paste a job URL to auto-extract information
• Fill in the form with job details
• Set follow-up reminders

• Job Info Extractor: Paste URLs from LinkedIn, Indeed, company sites
• Cover Letter Generator: Choose from templates (Software Developer, Data Analyst, etc.)
• Follow-up Emails: Generate professional follow-up messages

• Dashboard: Overview of your job search metrics
• Analytics: Detailed breakdowns by status, company, timeline
• Rejection Analysis: Learn from patterns to improve success rate

• View pending follow-ups on the dashboard
• Set custom reminders for each application
• Mark follow-ups as completed

1. Prepare your code

   • Ensure all files are in your project directory
   • Test locally with streamlit run app.py

2. Create a GitHub repository

   • Push your code to a public GitHub repository
   • Include all files: app.py, database.py, job_utils.py, requirements.txt

3. Deploy to Streamlit Cloud

   • Visit share.streamlit.io
   • Sign in with GitHub
   • Click "New app"
   • Select your repository and branch
   • Set main file path: app.py
   • Click "Deploy!"

4. Your app will be live

   • Get a public URL like https://your-app.streamlit.app
   • Share with potential employers!

If you add external APIs later, you can set secrets in Streamlit Cloud:

• Go to your app settings
• Add secrets in the "Secrets" tab
• Format as TOML

The application uses SQLite with three main tables:

• id: Primary key
• company_name: Company name
• job_title: Position title
• job_url: Link to job posting
• application_date: When you applied
• status: Current status (Applied, Interview, etc.)
• salary_range: Expected compensation
• location: Job location
• job_description: Full job description
• contact_person: Recruiter/HR contact
• notes: Your notes
• follow_up_date: Next follow-up date
• rejection_reason: If rejected, why
• source: Where you found the job

• Links to applications
• Tracks follow-up reminders
• Marks completion status

• Future enhancement for interview tracking
• Links to applications

• Local Data Storage: All data stored locally in SQLite
• No Cloud Dependencies: Works entirely offline
• Export Control: You own and control your data
• No Tracking: No analytics or tracking code

1. New Status Types: Edit the status dropdown in app.py
2. Custom Templates: Add templates in job_utils.py
3. New Metrics: Extend the analytics in database.py

• Modify CSS in app.py for custom colors
• Update .streamlit/config.toml for theme changes
• Add custom charts with Plotly

This project demonstrates:

• Python Development: Clean, modular code structure
• Web Development: Full-stack application with database
• Data Analysis: Pandas, SQL, data visualization
• UI/UX Design: User-friendly interface design
• DevOps: Deployment and configuration management

• Identified Pain Point: Job search tracking inefficiency
• Built Solution: Comprehensive tracking system
• Added Value: Analytics and automation features
• Real-world Usage: Immediately useful tool

"I built this Job Application Tracker to solve my own job search challenges. It demonstrates my ability to identify problems, architect solutions, and build production-ready applications. The tool has helped me stay organized and analyze my job search patterns for better results."

• Email Integration: Import applications from email
• Calendar Sync: Sync follow-ups with Google Calendar
• Resume Matching: Score resume match against job descriptions
• Salary Insights: Market rate analysis
• Interview Prep: Company research automation
• Application Templates: Save and reuse application data

• Success Prediction: ML model for application success
• Market Trends: Industry hiring pattern analysis
• Skill Gap Analysis: Identify missing qualifications
• Geographic Analysis: Location-based success rates

This is a personal portfolio project, but improvements are welcome!

1. Fork the repository
2. Create a feature branch
3. Make improvements
4. Test thoroughly
5. Submit a pull request

This project is open source. Feel free to use, modify, and adapt for your own needs.

Built by [Your Name] as part of a job search optimization project.

• Portfolio: [Your Portfolio URL]
• LinkedIn: [Your LinkedIn]
• GitHub: [Your GitHub]
• Email: [Your Email]

In today's competitive job market, organization and data-driven decision making are crucial for job search success. This application:

1. Eliminates Manual Tracking: No more spreadsheets or scattered notes
2. Provides Actionable Insights: Learn what works and what doesn't
3. Saves Time: Auto-fill tools and templates speed up applications
4. Improves Follow-up: Never miss important follow-up opportunities
5. Demonstrates Skills: Shows technical ability while solving real problems

The project showcases full-stack development skills while addressing a genuine professional need - making it a perfect portfolio piece that tells a compelling story about problem-solving and technical execution.

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