Boost your hiring process with this AI Recruitment System powered by Google Gemini AI and built with Streamlit.
This system automates CV analysis, transcribes and evaluates interview audio, manages candidates, and generates professional reports.

✔️ CV Analysis – Upload PDF resumes and let AI score and summarize candidates based on job requirements.
✔️ Interview Analysis – Upload MP3 interviews, transcribe automatically, and get AI-powered suitability scores with salary recommendations.

The best fits for a role, this system ensures you find top talent efficiently and effectively.

The clean, modern UI built with Streamlit makes the application easy to navigate, and the included documentation ensures a smooth setup and user experience.

• Intelligent CV Analysis: Upload a candidate's PDF resume and get an instant suitability score, a detailed summary, and a hiring recommendation.
• AI Interview Analysis: Transcribe and analyze audio interviews (MP3 files) to get a full transcript and a comprehensive AI summary, including an updated suitability score.
• Candidate Management: A centralized dashboard to view, filter, and manage all candidates based on their progress through the hiring funnel.
• Hiring Prioritization: Use the AI to generate a prioritized list of top candidates for a specific job, helping you make final decisions faster.
• Automated Notifications: Send direct interview invitations and hiring decisions to candidates via email right from the app.
• Professional Reporting: Generate and download detailed PDF reports for CV and interview analyses.

• Python 3.10+
• An SMTP Server for email notifications
• A Gemini API Key
• Microsoft ODBC Driver 17 for SQL Server
• SQL Server (database)

1. Clone or extract the project files.
2. Create a .env file and add your Gemini API key and SMTP credentials for email sending.
3. Update your database credentials in main.py inside the get_connection() function.
4. Import the provided SQL schema (recruitment_system_schema.sql) into your SQL Server database.
5. Install dependencies: pip install -r requirements.txt
6. Run the application: streamlit run main.py
7. Open your browser at http://localhost:8501.