hm-badhon/Professional-Resume-Scanner-Application

Professional Resume Scanner Application

Project Overview

This Flask application is designed to classify resumes into various job categories using a machine learning model. The application allows users to upload their resumes, which are then processed and classified into a predefined category using a trained model.

Features

• Resume Upload: Users can upload resumes in text format.
• Resume Cleaning: The application preprocesses and cleans the resume text.
• Resume Classification: The cleaned text is classified into one of the predefined job categories.
• User-Friendly Interface: The application features a simple and modern web interface for ease of use.

Requirements

• Python 3.x
• Flask
• scikit-learn
• nltk

Installation

1. Clone the Repository

   git clone https://github.com/hm-badhon/Professional-Resume-Scanner-Application.git
   cd your-repo-name

2. Set Up 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

   If requirements.txt is not available, you can manually install the necessary packages:

   pip install flask scikit-learn nltk

4. Download NLTK Data

   Ensure you have the necessary NLTK data:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')

5. Download or Train Models

   Ensure you have the pre-trained models clf.pkl and tfidf.pkl. Place these files in the root directory of your project.

Usage

1. Run the Flask Application

   python app.py

   The application will start and be accessible at http://127.0.0.1:5000.

2. Access the Application

   Open a web browser and navigate to http://127.0.0.1:5000. You will see a form to upload resumes.

3. Upload a Resume

   Use the form to upload a resume file. The application will process the resume and display the predicted job category.

Project Structure

your_project/
│
├── static/
│   └── style.css       # CSS file for styling
│
├── templates/
│   └── index.html       # HTML template for the main page
│
├── app.py              # Main application file
├── clf.pkl             # Trained model for classification
├── tfidf.pkl           # Trained TF-IDF vectorizer
├── requirements.txt    # List of required Python packages
└── README.md           # This file

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request with your changes. Make sure to follow the coding style and include tests for your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Flask: A micro web framework for Python.
• scikit-learn: A machine learning library for Python.
• nltk: A library for natural language processing.