Here's a high-level project script for generating a website on an AI-Powered Resume Screening Tool:

Project Overview The AI-Powered Resume Screening Tool is a web application designed to help recruiters and hiring managers streamline their candidate selection process. The tool uses natural language processing (NLP) and machine learning algorithms to analyze resumes and identify top candidates based on the job requirements.

Features

1. Resume Upload: Allow users to upload resumes in various formats (e.g., PDF, Word, Text).
2. Job Description Input: Enable users to input job descriptions and requirements.
3. AI-Powered Screening: Use NLP and machine learning algorithms to analyze resumes and identify top candidates.
4. Candidate Ranking: Display a ranked list of candidates based on their resume match with the job requirements.
5. Candidate Profile View: Allow users to view individual candidate profiles, including their resume, contact information, and matching score.
6. User Authentication: Implement user authentication to ensure only authorized users can access the tool.

Technologies

1. Frontend: Build the user interface using HTML, CSS, and JavaScript (React or Angular).
2. Backend: Use a Python framework (Flask or Django) to handle API requests, integrate with NLP libraries, and manage user data.
3. NLP Library: Utilize a library like NLTK, spaCy, or Stanford CoreNLP for NLP tasks.
4. Machine Learning Library: Employ a library like scikit-learn or TensorFlow for machine learning tasks.
5. Database: Choose a database management system like MySQL, PostgreSQL, or MongoDB to store user data and resumes.

Project Structure

1. Frontend:
   • index.html: Main entry point for the application.
   • styles.css: CSS styles for the application.
   • script.js: JavaScript code for the application.
2. Backend:
   • app.py: Main application file (Flask or Django).
   • models.py: Defines database models for user data and resumes.
   • views.py: Handles API requests and integrates with NLP libraries.
   • nlp.py: Contains NLP-related functions and classes.
   • ml.py: Contains machine learning-related functions and classes.
3. Database:
   • database.db: Database file (e.g., SQLite) or database schema (e.g., MySQL).

API Endpoints

1. POST /upload-resume: Handles resume uploads.
2. POST /input-job-description: Handles job description input.
3. GET /get-candidate-rankings: Returns a ranked list of candidates.
4. GET /get-candidate-profile: Returns an individual candidate profile.

Example Use Cases

1. A recruiter uploads a job description and several resumes.
2. The AI-Powered Resume Screening Tool analyzes the resumes and returns a ranked list of candidates.
3. The recruiter views individual candidate profiles and contacts the top candidates for an interview.

Future Development

1. Integrate with popular applicant tracking systems (ATS).
2. Expand NLP capabilities to handle more languages and resume formats.
3. Implement additional machine learning algorithms to improve candidate ranking accuracy.

This project script provides a solid foundation for building an AI-Powered Resume Screening Tool. However, please note that this is a high-level overview, and you'll need to dive deeper into each component to implement the project successfully.