Smart Resume Analyzer & Optimizer 🚀

Transform your resume with AI-powered analysis and optimization. Get instant feedback leveraging multiple state-of-the-art language models.

✨ Key Features

• ATS Compatibility Score: Comprehensive analysis of your resume's compatibility with Applicant Tracking Systems
• Detailed Content Analysis: In-depth evaluation of structure, keywords, and content quality
• Tailored Recommendations: Specific suggestions to improve your resume's effectiveness
• Job Description Matching: Compare your resume against job requirements with precision scoring
• Interactive Resume Chat: Ask questions about your resume and get instant answers
• Multi-Model Support: Access to leading AI models including:
  • OpenAI (From GPT-3.5-turbo to GPT-4o-mini)
  • Mistral (medium and large)
  • Claude (3.5-sonnet, 3.5-haiku, 3 opus)
  • HuggingFace Inference API
  • Groq Models
  • Ollama Models

🛠️ Installation & Setup

Using Docker (Recommended)

Pull and run the pre-built image:

docker pull manthan07/resume_analyzer:main-latest
docker run -p 7860:7860 manthan07/resume_analyzer:main-latest

Alternative Docker Build

git clone https://github.com/manthan89-py/Smart-Resume-Analyzer-Optimizer
cd Smart-Resume-Analyzer-Optimizer
docker build -t localmachine/resume_analyzer:main-latest .
docker run -p 7860:7860 localmachine/resume_analyzer:main-latest

Local Installation

git clone https://github.com/manthan89-py/Smart-Resume-Analyzer-Optimizer
cd Smart-Resume-Analyzer-Optimizer
sh start.sh  # Requires Python 3.12+

🎯 Usage Guide

1. Access the application at https://localhost:7860
2. Upload your resume in PDF format
3. Select your preferred LLM Model
4. Provide API Key/Model Token if required
5. (Optional) Add specific questions or instructions
6. (Optional) Include a job description for comparison analysis

💡 Example Queries

• "What are the strengths and weaknesses of my resume?"
• "What interview questions might be asked for [JOB ROLE]?"
• "Highlight my main technical skills"
• "Provide a professional summary for my resume"

⚙️ Technical Details

Analysis Components

1. ATS Score Calculation

   • Keyword optimization (35%)
   • Structural formatting (25%)
   • Content quality (20%)
   • Professional narrative coherence (15%)
   • Additional contextual factors (5%)

2. Job Description Matching

   • Technical skill match
   • Experience relevance
   • Soft skill alignment
   • Professional narrative coherence

Preprocessing and Analysis

• Structural decomposition of resume sections
• Lexical optimization and keyword analysis
• Content sophistication evaluation
• Intelligent transformation methodology

⚠️ Current Limitations & Workarounds

• Model Parsing Issues: Implemented retry mechanism for LLM calls. Consider using Groq (limited usage) or Mistral models (currently free) as alternatives
• Markdown Formatting: Some inconsistencies in output formatting. Currently optimized for content analysis over formatting
• Processing Time: Check container/server logs for performance issues. Multiple model options available as alternatives

🚀 Future Enhancements

• Additional model support including Local Models API (LLMStudio)
• Enhanced ATS Score breakdown
• Advanced resume analysis features
• LinkedIn Profile integration and comparison
• Improved markdown formatting
• Direct update capability for source documents (Docx, PDF)
• Resume-LinkedIn profile comparison analysis

📝 Note on Ollama Models

For Ollama models, ensure the Ollama service is running. Check status with:

ollama ps

🔒 Prerequisites

• Python 3.12 or higher (for local installation)
• Docker (for containerized deployment)
• Relevant API keys for chosen language models

🤝 Contributing

Thanks for your interest in improving Smart Resume Analyzer & Optimizer! Here's how you can help:

Quick Start

1. Fork the repo and create your branch from main
2. Make your changes
3. Test your changes
4. Submit a pull request

Guidelines

• Bug Reports: Use GitHub's issue tracker

  • Include steps to reproduce
  • Provide example code when possible
  • Describe expected vs actual behavior

• Code Style:

  • Follow PEP 8
  • Use black formatter

• Pull Requests:

  • Update readme if needed
  • Update requirements.txt for new dependencies
  • Get approval from at least one maintainer