Automated AI Resume Screening

Automated AI Resume Screening is a web application that allows users to upload resumes and job descriptions in PDF format. The system processes these documents using AI and NLP techniques to extract skills, education, experience, and match resumes with job descriptions based on similarity scores.

Features

• Upload resumes and job descriptions (PDF format)
• Extract and analyze skills, education, and experience
• Predict the most relevant job category for a resume
• Compute resume-job matching scores based on skills and similarity
• Store and retrieve screening results from the database
• Built using FastAPI (backend) and React TypeScript (frontend)

Technologies Used

Backend:

• FastAPI (Python)
• SQLAlchemy (Database ORM)
• PostgreSQL (Database)
• PyPDF2 (PDF processing)
• Flair (Named Entity Recognition for extracting names)
• Sentence Transformers (Semantic text similarity)
• Scikit-learn (Machine learning models)

Frontend:

• React.js (TypeScript)
• Bootstrap & Chart.js (Visualization)

Installation & Setup

Backend Setup

1. Clone the repository:

   git clone https://github.com/alihassanml/Automated-AI-Resume-Screening.git
   cd Automated-AI-Resume-Screening/backend

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Run the FastAPI server:

   uvicorn main:app --reload

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start the React development server:

   npm run dev

API Endpoints

Upload Resume & Job Description

• Endpoint: POST /upload
• Description: Uploads a resume and job description for processing
• Request Body: Multipart form with resume and job files (PDF)
• Response: JSON containing processing results and database entry ID

Get Resume Screening Result

• Endpoint: GET /results/{id}
• Description: Fetches the screening result for a given ID
• Response: JSON containing extracted details, matching scores, and resume ranking

How It Works

1. User uploads a resume and job description
2. System extracts skills, education, experience using NLP
3. Computes skill and text similarity scores
4. Predicts the job category for the resume
5. Stores the results in a database
6. User retrieves the results via the API or frontend

Future Enhancements

• Improve job matching using deep learning models (e.g., BERT)
• Support for additional file formats (e.g., DOCX)
• Expand the frontend UI for better visualization
• Integration with job portals for automated screening

License

This project is licensed under the MIT License.

Contact

For any queries, reach out to Ali Hassan.