AI Recruiter Pro

AI-powered recruitment platform that intelligently matches candidates with job opportunities through advanced natural language processing and AI algorithms.

🚀 Features

• AI-Powered Matching: Uses OpenAI embedding models to match candidates with suitable job openings
• Smart Resume Processing: Automatically extracts skills, experience, and qualifications from resumes
• Candidate Persona Generation: Creates detailed candidate profiles using AI
• Recruiter Collaboration: Share candidates and jobs with team members
• Advanced Role-Based Access Control: Granular permission system with administrator, senior recruiter, and recruiter roles
• Cloud Storage Integration: Store candidate resumes in Google Cloud Storage
• Responsive Design: Modern Material Design-inspired UI
• Bulk Resume Upload: Process multiple candidate resumes at once
• Job Token System: Intelligently groups similar job postings for improved analytics
• Candidate Rating System: Collect and manage recruiter feedback on candidates

📸 Screenshots

🔄 Data Sharing System

The platform includes a sophisticated data sharing system that allows recruiters to collaborate effectively:

• Sharing During Invitation: Administrators and Senior Recruiters can enable data sharing when inviting new team members
• Granular Permissions: Choose to share job listings, candidates, or both
• Visual Indicators: Shared resources display "Shared by [Name]" to clearly indicate ownership
• Filtering Options: Dedicated tabs for filtering between owned and shared resources
• Dynamic Updates: Changes to shared resources are instantly visible to all team members with access

🛠️ Installation

Prerequisites

• Python 3.9+
• PostgreSQL database
• Google Cloud Storage account (optional, falls back to local storage)
• OpenAI API key

Quick Start

1. Clone the repository

   git clone https://github.com/yourusername/ai-recruiter-pro.git
   cd ai-recruiter-pro

2. Set up a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt  # For exact versions
   # OR
   pip install -r requirements-app.txt  # For minimum versions

4. Configure environment variables

   cp .env.example .env
   # Edit .env with your configuration

5. Set up the database

   python migrations.py

6. Run the application

   python main.py

7. Access the application Open your browser and go to: http://localhost:5000

⚙️ Configuration

Environment Variables

See .env.example for a complete list of configuration options. The most important ones are:

Variable	Description
DATABASE_URL	PostgreSQL database connection string
OPENAI_API_KEY	Your OpenAI API key for AI features
GOOGLE_APPLICATION_CREDENTIALS	Path to GCP service account credentials file
GCS_BUCKET_NAME	Google Cloud Storage bucket name for resume storage
SECRET_KEY	Secret key for session encryption
DEMO_PASSWORD	Password for the demo admin account (improves security)

🔒 Google Cloud Storage Setup

This application can use Google Cloud Storage to store candidate resumes, making them accessible from anywhere and reducing local storage needs:

1. Create a Google Cloud Project
2. Create a service account with Storage Admin permissions
3. Download the service account credentials JSON file
4. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of this file
5. Create a bucket and set the GCS_BUCKET_NAME environment variable

The application will automatically fall back to local storage if GCS credentials are not provided.

📋 Admin Guide

Initial Setup

After installation, create an admin account:

python create_admin.py --email [email protected] --password SecurePassword123

Adding Sample Data

You can add sample data to test the application:

python add_test_data.py

🔐 Security Best Practices

• Keep your .env file secure and never commit it to version control
• Store the Google Cloud credentials file securely
• Regularly rotate your API keys and credentials
• Use strong passwords for database and admin accounts
• Always set the DEMO_PASSWORD environment variable to a secure value
• Never expose login credentials in UI elements or public-facing pages
• Set up proper HTTPS in production

Role-Based Access Control System

The application implements a three-tier role hierarchy:

• Administrator: Full system access with user management capabilities
• Senior Recruiter: Enhanced permissions for team management, with ability to invite users and share data
• Recruiter: Standard permissions for candidate and job management

Each role has specific permissions:

Feature	Recruiter	Senior Recruiter	Administrator
View Candidates	✓	✓	✓
Rate Candidates	✓	✓	✓
Manage Jobs	✓	✓	✓
Bulk Upload	✗	✓	✓
Invite Users	✗	✓	✓
Manage Teams	✗	✓	✓
System Config	✗	✗	✓
Data Analytics	✗	✓	✓

Demo Admin Account Security

The application uses a special security mechanism for the demo admin account:

• The password for the demo admin account ([email protected]) is controlled by the DEMO_PASSWORD environment variable
• This variable is loaded from the .env file at startup
• The application automatically synchronizes the demo account password with this environment variable
• Regular user accounts (non-demo) continue to use standard password management
• This approach ensures that demo credentials can be centrally managed without hardcoding
• The demo account password is automatically reset to match the environment variable upon application startup

🚀 Deployment

For production deployment:

1. Set FLASK_ENV=production and DEBUG=False
2. Use a production WSGI server like Gunicorn:

   gunicorn --bind 0.0.0.0:5000 main:app

3. Set up proper HTTPS with a valid SSL certificate
4. Configure database connection pooling
5. Consider using a reverse proxy like Nginx

📝 License

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

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.