metadata

title: TalenttrackAI
emoji: 📄
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.30.0
app_file: app.py
pinned: false

Talent Track AI

Overview

"Talent Track AI" leverages the power of LLM as an advanced Streamlit application, specializing in thorough resume analysis. It excels at summarizing the resume, evaluating strengths, identifying weaknesses, and offering personalized improvement suggestions, while also recommending the perfect job titles. Additionally, it seamlessly employs Selenium to extract vital LinkedIn data, encompassing company names, job titles, locations, job URLs, and detailed job descriptions. In essence, Resume Analyzer AI simplifies the job-seeking journey by equipping users with comprehensive insights to elevate their career opportunities.

Features

Resume Analysis
LinkedIn Job Scraping
Career Guidance
Local LLM Processing (No API keys needed)

System Requirements

Python 3.8+
8GB+ RAM
10GB+ free disk space
Multi-core CPU recommended

Setup Instructions

Clone the repository:

git clone https://github.com/yourusername/TalentTrackAI.git
cd TalentTrackAI

Install required packages:

pip install -r requirements.txt

Download the model:

python setup_model.py

Run the application:

streamlit run app.py

Key Components

Local LLM Integration

Uses Llama 2 7B Chat model (quantized version)
Processes all requests locally
No API keys or internet required for analysis
Complete privacy and data security

Resume Analysis Pipeline

PDF text extraction
Chunk-based processing
Vector embeddings using HuggingFace
FAISS for efficient similarity search
Local LLM for analysis generation

LinkedIn Integration

Automated job search
Real-time data extraction
Customizable search parameters
Detailed job information retrieval

Usage Guide

Resume Analysis
- Upload your resume (PDF format)
- Get instant analysis including:
  - Detailed summary
  - Key strengths
  - Areas for improvement
  - Job title suggestions
Job Search
- Enter desired job title
- Specify location (optional)
- View matching LinkedIn listings
- Export results if needed

Technical Details

Model Specifications

Model: Llama 2 7B Chat
Format: GGUF (quantized)
Context Window: 2048 tokens
Memory Usage: ~4GB
Processing: CPU-based

Key Libraries

Streamlit
LangChain
FAISS
PyPDF2
Selenium
HuggingFace Transformers

Performance Notes

First run may take longer due to model loading
Subsequent analyses are faster
Processing time varies based on resume length
RAM usage depends on concurrent operations

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Table of Contents

Key Technologies and Skills
Installation
Usage
Features
Contributing
License
Contact

Key Technologies and Skills

Python
NumPy
Pandas
LangChain
LLM
Selenium
Streamlit
Hugging Face
AWS

Usage

To use this project, follow these steps:

Clone the repository:

git clone https://github.com/yourusername/TalentTrackAI.git
cd TalentTrackAI

Install the required packages: pip install -r requirements.txt
Run the Streamlit app: streamlit run app.py
Access the app in your browser at http://localhost:8501

Features

Easy User Experience:

Resume Analyzer AI makes it easy for users. You can upload your resume without any hassle. The application is designed to be user-friendly so that anyone can use its powerful resume analysis features.
It also uses the PyPDF2 library to quickly extract text from your uploaded resume, which is the first step in doing a thorough analysis.

Smart Text Analysis with Langchain:

What makes it special is how it analyzes text. It uses a smart method called the Langchain library to break long sections of text from resumes into smaller chunks, making them more meaningful.
This clever technique improves the accuracy of the resume analysis, and it gives users practical advice on how to enhance their job prospects.

Enhanced LLM Integration with FAISS:

The application uses local LLM processing for all analysis tasks, ensuring privacy and eliminating the need for API keys.
It uses the FAISS(Facebook AI Similarity Search) library to convert both the text chunks and query text data into numerical vectors, simplifying the analysis process and enabling the retrieval of pertinent information.

Intelligent Chunk Selection and LLM:

Utilizing similarity search, Resume Analyzer AI compares the query and chunks, enabling the selection of the top 'K' most similar chunks based on their similarity scores.
The application processes all requests using a local LLM model, ensuring complete privacy and data security.

Robust Question-Answering Pipeline:

This integration establishes a robust question-answering (QA) pipeline, making use of the load_qa_chain function, which encompasses multiple components, including the language model.
The QA chain efficiently handles lists of input documents (docs) and a list of questions (chunks), with the response variable capturing the results, such as answers to the questions derived from the content within the input documents.

Comprehensive Resume Analysis:

Summary: Resume Analyzer AI provides a quick, comprehensive overview of resumes, emphasizing qualifications, key experience, skills, projects, and achievements. Users can swiftly grasp profiles, enhancing review efficiency and insight.
Strength: Effortlessly conducting a comprehensive resume review, it analyzes qualifications, experience, and accomplishments. It subsequently highlights strengths, providing job seekers with a competitive edge.
Weakness: AI conducts thorough analysis to pinpoint weaknesses and offers tailored solutions for transforming them into strengths, empowering job seekers.
Suggestion: AI provides personalized job title recommendations that align closely with the user's qualifications and resume content, facilitating an optimized job search experience.

Selenium-Powered LinkedIn Data Scraping:

Utilizing Selenium and a Webdriver automated test tool, this feature enables users to input job titles, automating the data scraping process from LinkedIn. The scraped data includes crucial details such as company names, job titles, locations, URLs, and comprehensive job descriptions.
This streamlined process enables users to easily review scraped job details and apply for positions, simplifying their job search and application experience.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.

License

This project is licensed under the MIT License. Please review the LICENSE file for more details.

Contact

📧 Email: [email protected]

For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.