AI Enabled Candidate Resume Screening Using Spacy Entity Recognition

Link to access: the website: https://ai-resume-parsing.onrender.com/

This repository contains the code for an AI-enabled candidate resume screening system (web based using python-flask). It allows you to automatically screen resumes based on job requirements and skills.

Refer this Repo directly downloading and running the code in your python IDE(make sure to take help from GPTs to setup and run the code, also refer this readme for understanding of code): https://github.com/jeeban101/AI-Resume-Screening

Prerequisites

Before running the project, ensure you have the following dependencies installed:

• Python 3.7 or higher
• All the packages listed in the requirements.txt file.

Getting Started

1. Clone this repository to your local machine.
2. Install the required dependencies by running the following command:
3. Update the necessary configurations in the config.py file.
4. Place your resume files in the designated folder.
5. Run the Flask application by executing the following command:
6. Open your web browser and navigate to http://127.0.0.1:5000/ to access the application.

Folder Structure

• app.py: Main Flask application file.
• Resume_parser.py: Resume parsing module.
• templates/: HTML templates for the web application.
• static/: Static files (CSS, images, etc.) for the web application.
• Assignments-AI-20BIT0441/: Folder containing assignment notebooks and datasets.
• AI Enabled Candidate Resume Screening/: Folder contains all the modules and files for AI Enabled Candidate Resume Screening Using Spacy Entity Recognition.
• Project Report AI Resume Screening.pdf: Detailed Report for the AI Enabled Candidate Resume Screening Using Spacy Entity Recognition.
• Assignment Datasets Link.txt: Contains Link for datasets of the assignment.
• Demo Video Link & Assignment Datasets Link.txt: Contains demo video link of project explaining workflow and working of code with demo.
• requirements.txt: Contains the python libraries and dependencies required for project.

Additional Resources

• Demo Video: Watch a demonstration of the project in action.
• Project Report: Read the report documenting the assignments.
• Drive Folder: Access the assignments and datasets on Google Drive.

Contributors

• Jeeban Bhagat (https://github.com/jeeban101/AI-Enabled-Candidate-Resume-Screening-) (20BIT0441, VIT,Vellore)

Requirements

• Python 3.x
• Flask
• Pyresparser
• NLTK
• scikit-learn
• Werkzeug
• smtplib

Installation

1. Clone the repository:

   git clone https://github.com/jeeban101/AI-Enabled-Candidate-Resume-Screening-
   1.Navigate to the project directory:
    cd AI-Enabled-Candidate-Resume-Screening-
   2.Install the required Python packages:
    pip install -r requirements.txt

Usage 1.Run the Flask web application: python app.py The application will be accessible at http://127.0.0.1:5000/.(may differ by flask version)

2.Access the web application in your web browser and fill in the required information, including the job position and resume file.

3.Upon submission, the system will extract skills from the resume, compare them with the job requirements, and send an email notification to the candidate regarding the screening outcome.

if Error: OSError: [E053] Could not read config.cfg from .....\venv\lib\site-packages\pyresparser\config.cfg #46 then,do the following:

• pip install nltk
• pip install spacy==2.3.5
• pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
• pip install pyresparser

does the trick. Also try different spacy versions and models, because they produce different results. Haven't tested any further myself. Hope this helps :) TO RESOLVE THE nltk error "python3 -m nltk.downloader stopwords" after pip install nltk

Assignments(https://drive.google.com/drive/folders/1Sxp0-cwl0nEDT5cbkbB3b8RUhwZSfi9h?usp=sharing) The "Assignments-AI-20BIT0441" folder contains assignments in Jupyter Notebook format. Each assignment is a separate .ipynb file that demonstrates different concepts and techniques related to AI.

Additional Files Report: The project report is available in the repository. It provides detailed information about the system architecture, algorithms used, and evaluation results.

Demo Video: A demo video showcasing the project functionality is available here.

Data Sets(for assignments)(Link:https://drive.google.com/drive/folders/1Sxp0-cwl0nEDT5cbkbB3b8RUhwZSfi9h?usp=sharing): The project utilizes specific data sets for training and evaluation purposes. These data sets can be accessed via the Google Drive link provided in the repository.

Contributing Contributions to this project are welcome. If you have any suggestions or improvements, feel free to open an issue or submit a pull request.