kushaalrajiv/hackathon

E-Recruitment Platform

Overview

The E-Recruitment Platform is a sophisticated tool designed to streamline the hiring process by integrating various technologies such as OCR, TF-IDF, PyResparser, Supabase Database, sentiment analysis, and automated email/calendar invite generation for parsed resumes.

Features

• OCR (Optical Character Recognition):

  • Extracts text data from scanned or image-based resumes.

• TF-IDF (Term Frequency-Inverse Document Frequency):

  • Utilizes TF-IDF for analyzing the importance of words in resumes, aiding in efficient candidate shortlisting.

• PyResparser:

  • Extracts relevant information from resumes using PyResparser, facilitating easy categorization of candidate details.

• Supabase Database:

  • Leverages Supabase as the database for secure and scalable storage of candidate information.

• Sentiment Analysis:

  • Performs sentiment analysis on resumes to gain insights into the emotional tone of candidate applications.

• Automated Email and Calendar Invites:

  • Automates the process of sending emails and calendar invites to candidates, enhancing communication efficiency.

Getting Started

1. Installation:

   • Clone this repository: git clone https://github.com/your-username/e-recruitment-platform.git
   • Navigate to the project directory: cd e-recruitment-platform
   • Install dependencies: pip install -r requirements.txt

2. Configuration:

   • Set up Supabase credentials and configure the database connection in config.py.
   • Ensure proper API keys for email and calendar integration.

3. Running the Application:

   • Execute the application: python app.py
   • Access the platform at http://localhost:5000 in your web browser.

Usage

1. Upload Resumes:

   • Use the provided interface to upload resumes in various formats, including scanned documents.

2. Resume Processing:

   • The platform automatically processes resumes using OCR, TF-IDF, and PyResparser.

3. Database Management:

   • Candidate details are stored securely in the Supabase database, allowing for easy retrieval and analysis.

4. Sentiment Analysis:

   • View sentiment analysis results to understand the emotional tone of each candidate's application.

5. Automated Communication:

   • The platform automatically sends personalized emails and calendar invites to shortlisted candidates.

Dependencies

• PyResparser
• Supabase
• NLTK
• Flask
• [Other dependencies listed in requirements.txt]

Contributing

Contributions are welcome! Please follow the guidelines in CONTRIBUTING.md.

License

This project is licensed under the MIT License.