Advanced Resume Tracking System

Table of Contents 📋

• Overview
• Features
• Prerequisites
• Setup
  • Clone the repository
  • Navigate to the project folder
  • Install dependencies
  • Create a .env file
  • Run the application
  • Open the application in your browser
• Contents
• Environment Variables
• Large Language Models (LLM)
• GenAI Integration
• Demo
• Video Demo
• Contributing
• License
• Acknowledgments
• Contact

Overview 📜

This project is an advanced Resume Tracking System implemented using Large Language Models (LLM) in Python and GenAI. The system is deployed on Streamlit for a user-friendly interface. The project includes an app (app.py), a .env file for environment variables, and a requirements.txt file listing dependencies.

Features 📊

• Advanced resume parsing using LLM
• Streamlit-based user interface
• GenAI integration for enhanced functionality
• Easy deployment and setup

Prerequisites ✒️

Make sure you have the following installed:

• Python (version 3.11.4)
• Streamlit
• Other dependencies listed in requirements.txt

Setup 💻

1. Clone the repository:

   git clone https://github.com/neerajcodes888/Adavanced-Resume-Tracking-System.git

2. Navigate to the project folder:

   cd Adavanced-Resume-Tracking-System.git

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file

   GOOGLE_API_KEY=Your Google API Key  (https://makersuite.google.com/app/apikey)
   
   

5. Add the required environment variables:

   pip install -r requirements.txt

6. Run the application:

   streamlit run app.py

7. Open your browser and go to http://localhost:8501 to access the Resume Tracking System.

Large Language Models (LLM) 📂

This project utilizes Large Language Models, such as GPT-3.5, for advanced resume parsing. LLMs are powerful natural language processing models that can understand and generate human-like text. Ensure that you have access to the necessary LLM APIs and credentials.

GenAI Integration 🔌

GenAI is integrated into the system to provide additional functionality. GenAI may include features like automated content generation, semantic understanding, or other AI capabilities. Please refer to the GenAI documentation for specific details and integration instructions.

Environment Variables 📰

• GOOGLE_API_KEY: Description of the variable(Confidential).

Add any additional environment variables and their descriptions as needed.

Demo 📷

To see the magic of the Resume Tracking System, follow these steps:

1. Access the Deployed Application:

   • Open your web browser.
   • Navigate to the URL --> Deployed Link.

2. Upload a PDF Resume:

   • On the Streamlit web interface, look for the "Upload PDF" button.
   • Click the button, and a file upload dialog will appear.
   • Choose a resume in PDF format from your local machine.

3. Upload a Job Description (JD):

   • Continue by finding the "Upload JD" button on the Streamlit interface.
   • Click the button, and another file upload dialog will appear.
   • Choose a job description file, which should be text format.

4. See the Magic Happen:

   • After uploading both files, look for a "Process and Analyze" button on the Streamlit interface.
   • Click this button to initiate the analysis.
   • Witness the system's magic as it utilizes Large Language Models (LLM) and GenAI integration to parse and analyze the uploaded resume and job description.
   • Explore the displayed results on the Streamlit interface, showcasing the advanced capabilities of the system.

By following these steps, users will be able to experience the live demonstration of your Resume Tracking System directly on the Streamlit application. Make sure to include any specific instructions or guidance within the Streamlit interface for a seamless user experience.

VDemo 📺

Contributing 👯

Contributions are welcome! Feel free to open issues or submit pull requests.

License 📝

This project is licensed under the Apache-2.0 license.

Acknowledgments 🙏

• Special thanks to the developers and researchers at OpenAI for their contributions to Large Language Models.
• Appreciation to the GenAI development team for their innovative work in the field of artificial intelligence.

Contact 📞

For any inquiries or issues, please contact 💬.