Applicant Tracking System (ATS)

Table of Contents

• Introduction
• Features
• Algorithm Description
• Installation
• Usage
• Project Structure

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Introduction

The Applicant Tracking System (ATS) is a web application designed to streamline the recruitment process by automatically ranking candidate CVs based on their relevance to a given job description. By leveraging natural language processing (NLP) and machine learning techniques, the system identifies the top N CVs that best match the job requirements.

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Features

• Upload Multiple CVs: Support for uploading multiple CVs in PDF or TXT format.
• Job Description Parsing: Ability to upload a job description in PDF or TXT format.
• Similarity Scoring: Uses advanced NLP models to calculate the similarity between CVs and the job description.
• Top N CVs Selection: Allows users to specify the number of top CVs to retrieve.
• Download CVs: Provides download links for the top-ranked CVs.
• Dark Mode: Option to toggle between light and dark themes.
• Security Measures: Sanitizes file uploads to prevent malicious files from being processed.

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Algorithm Description

The algorithm used in this application scores the similarity between candidates' CVs (resumes) and the job description by comparing them. This process works through the following steps:

1. File Upload:

   • Users upload multiple CVs and a job description file. These files can be in PDF or TXT format.

2. Text Extraction:

   • Extracting Text from CVs: If a CV is a PDF file, text is extracted from each page using the pdfplumber library. If it's a TXT file, it is read directly.
   • Extracting Text from Job Description: The job description file is processed similarly to extract text.

3. Preprocessing:

   • Converting to Lowercase: All text is converted to lowercase to eliminate differences due to capitalization.
   • Tokenization and Lemmatization:
     • The text is tokenized using the English language model (en_core_web_sm) from spaCy.
     • Stopwords (like "the," "is," "at") and punctuation are removed.
     • The remaining words are converted to their base form (lemmas).

4. Generating Vector Embeddings:

   • Using the preprocessed text, sentence embeddings are generated with the SentenceTransformer model (all-MiniLM-L6-v2).
   • These embeddings are high-dimensional numerical vectors that capture the semantic meaning of the text.

5. Calculating Similarity Scores:

   • Cosine similarity (cosine_similarity) is calculated between the embedding of the job description and each CV's embedding.
   • Cosine similarity ranges from 0 to 1, where 1 indicates perfect similarity and 0 indicates no similarity.

6. Ranking and Selection:

   • All CVs are sorted in descending order based on their similarity scores.
   • The top N CVs, as specified by the user, are selected.

7. Storing Results:

   • The results of the selected CVs—including rank, filename, full filename (for download), and similarity score—are stored in a global dictionary (global_results) associated with the user's session ID (sid).

8. Displaying Results:

   • Upon completion of processing, the server emits a processing_complete event to the client associated with the session ID.
   • The client then redirects to the /results/<sid> path, where the stored results are displayed using a server-side template (results.html).
   • In the results.html template, Flask's url_for function is used to generate download links for each CV.

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Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository

git clone https://github.com/muhammadasad149/Applicant-Tracking-System--ATS-.git
cd Applicant-Tracking-System--ATS-

2. Create a Virtual Environment

Create and activate a virtual environment to manage project dependencies.

On Windows:

python -m venv venv
venv\Scripts\activate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

Install the required Python packages using pip:

pip install -r requirements.txt

4. Download NLP Models

Download the spaCy English language model:

python -m spacy download en_core_web_sm

5. .env File Setup

To generate a Flask secret key and set it in your .env file, follow these steps:

1. Generate a Secret Key Using Python:

   Open a Python shell or create a simple Python script, and execute the following commands:

   import uuid
   secret_key = uuid.uuid4().hex
   print(secret_key)

   This code uses Python's uuid module to generate a random 32-character hexadecimal string, which is suitable for use as a secret key. For example, it might output:

   3d6f45a5fc12445dbac2f59c3b6c7cb1
   

2. Set the Secret Key in the .env File:

   Open your .env file and add the following line, replacing the placeholder with the secret key you just generated:

   FLASK_SECRET_KEY='3d6f45a5fc12445dbac2f59c3b6c7cb1'
   

   Note: Ensure there are no spaces around the = sign and that the key is enclosed in single quotes.

3. Verify the Secret Key in Your Flask Application:

   In your app.py or Flask application code, make sure you're loading the secret key from the environment variable:

   import os
   from dotenv import load_dotenv
   
   load_dotenv()
   
   app = Flask(__name__)
   app.secret_key = os.environ.get('FLASK_SECRET_KEY')

   This ensures that your Flask app uses the secret key you've set in the .env file.

By following these steps, you have securely generated and set a Flask secret key in your .env file.

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Usage

1. Running the Application

Start the Flask application using the following command:

python app.py

The application will be accessible at http://localhost:5000.

2. Using the Application

• Upload CVs and Job Description: Navigate to the home page and use the form to upload multiple CVs and a job description.
• Specify Top N CVs: Enter the number of top CVs you wish to retrieve.
• Submit the Form: Click the "Submit" button to start the processing.
• View Progress: A progress bar and spinner will display the processing status.
• View Results: Once processing is complete, you will be redirected to a results page showing the top CVs.
• Download CVs: Click the "Download" button next to each CV to download it.

3. Dark Mode

Use the toggle switch in the navigation bar to switch between light and dark modes.

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Project Structure

Applicant-Tracking-System--ATS-/
│
├── app.py
├── requirements.txt
├── templates/
│   ├── index.html
│   └── results.html
├── static/
│   ├── css/
│   │   └── styles.css
│   ├── js/
│   │   └── scripts.js
│   └── images/
│       └── logo.png
└── README.md

• app.py: Main Flask application file.
• templates/: Contains HTML templates for the application.
• static/: Contains static files like CSS, JavaScript, and images.
• requirements.txt: Lists all the Python dependencies.
• README.md: Project documentation.

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