https://github.com/sliday/resume-job-matcher
AI-powered Python script for automated resume-job matching with scoring, PDF generation, and personalized email responses.
https://github.com/sliday/resume-job-matcher
Last synced: 5 months ago
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AI-powered Python script for automated resume-job matching with scoring, PDF generation, and personalized email responses.
- Host: GitHub
- URL: https://github.com/sliday/resume-job-matcher
- Owner: sliday
- Created: 2024-09-23T20:44:27.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-11-21T17:11:59.000Z (over 1 year ago)
- Last Synced: 2024-11-21T18:23:10.495Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 70.3 KB
- Stars: 151
- Watchers: 6
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Resume Job Matcher
## Overview
**Resume Job Matcher** is a Python script that automates the process of matching resumes to a job description using AI. It leverages the Anthropic Claude API or OpenAI's GPT API to analyze resumes and provide a match score along with personalized email responses for candidates.
## Features
- 🔥 **Comprehensive Resume Processing**
- Multiple outputs: PDF and Markdown generation
- Standardization for fair evaluation
- Font customization (sans-serif, serif, monospace)
- Command-line options for flexibility
- 🧠 **Advanced AI-Powered Analysis**
- Resume-job comparison using Claude/GPT API
- Dual AI support with runtime selection
- Efficient model interaction
- Structured data handling with Pydantic
- 📊 **In-depth Evaluation & Scoring**
- Smart parsing with PyPDF2
- Multi-factor assessment: skills, experience, education, certifications
- Visual and content-based quality assessment
- 🚩 Red flag detection in critical areas
- Detailed scoring with emoji and color-coded results
- 📈 **Comprehensive Analytics & Reporting**
- Statistical insights: top, average, median, standard deviation scores
- Candidate distribution summary
- Match analysis with improvement suggestions
- Job description optimization recommendations
- 🌐 **Enhanced Candidate Profiling**
- Website integration for improved matching
- Personalized email generation
- 🛠️ **Robust System Management**
- Advanced logging and error handling
- Improved user feedback and reliability
## Usage
To run the script with the new features:
```bash
python resume_matcher.py [--sans-serif|--serif|--mono] [--pdf] [job_desc_file] [pdf_folder]
```
- Use `--sans-serif`, `--serif`, or `--mono` to select a font preset.
- Use `--pdf` to generate PDF versions of unified resumes.
- Optionally specify custom paths for the job description file and PDF folder.
## Customization
### Adjust Logging Level
Modify the logging level at the beginning of the script:
```python
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
```
Available levels: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`.
### Change Scoring Model
To change the AI model used, update the `model` parameter in the `match_resume_to_job` function:
```python
message = client.messages.create(
model="claude-3-5-sonnet-20240620",
...
)
```
### Modify AI Provider
To switch between Anthropic and OpenAI APIs, modify the `choose_api` function call at the beginning of the script:
```python
def choose_api():
global chosen_api
prompt = "Use OpenAI API instead of Anthropic? [y/N]: "
choice = input(colored(prompt, "cyan")).strip().lower()
if choice in ["y", "yes"]:
chosen_api = "openai"
else:
chosen_api = "anthropic"
```
### Adjust AI Model
To change the AI model used, update the `model` parameter in the `talk_fast` function:
```python
response = client.chat.completions.create(
model="gpt-4o", # Change this to the desired model
...
)
```
## Score Calculation
The final score for each resume is calculated using a combination of two factors:
1. **AI-Generated Match Score (75% weight)**: This score is based on how well the resume matches the job description, considering factors such as skills, experience, education, and other relevant criteria.
2. **Resume Quality Score (25% weight)**: This score assesses the visual appeal and clarity of the resume itself, including formatting, layout, and overall presentation.
The calculation process is as follows:
1. The AI-generated match score and the resume quality score are both normalized to a 0-100 scale.
2. A weighted average is calculated:
`(AI_Score * 0.75 + Quality_Score * 0.25)`
3. The result is clamped to ensure it falls within the 0-100 range.
This combined approach ensures that both the content relevance and the presentation quality of the resume are taken into account in the final score.
### Modify Scoring Criteria
Adjust the scoring logic in the `match_resume_to_job` function's prompt as needed to better fit your specific requirements.
## Troubleshooting
### Common Issues
- **No Resumes Found**: Ensure that resume PDFs are placed in the correct directory (`src` by default).
- **Job Description Not Found**: Confirm that `job_description.txt` exists in the script's directory or provide the correct path.
- **API Key Errors**: Verify that the `CLAUDE_API_KEY` environment variable is set correctly.
- **Dependency Errors**: Install all required Python packages using `pip`.
### Adjusting Timeouts and Retries
If you experience network-related errors when fetching personal websites, you may adjust the `timeout` parameter in the `check_website` function.
```python
response = requests.get(url, timeout=10)
```
## Best Practices
- **Data Privacy**: Ensure that all candidate data is handled in compliance with relevant data protection laws and regulations.
- **API Usage**: Be mindful of API rate limits and usage policies when using the Anthropic Claude API.
## Contributing
We welcome contributions! Please follow these steps:
1. **Fork the Repository**: Create your own fork on GitHub.
2. **Create a Feature Branch**: Work on your feature or fix in a new branch.
3. **Submit a Pull Request**: Once your changes are ready, submit a pull request for review.
## Acknowledgments
- **Anthropic Claude API**: For providing advanced AI capabilities.
---
Enjoy using the Resume Job Matcher script to streamline your recruitment process!
## Python Packages
The following Python packages are required for this project:
- PyPDF2: For extracting text from PDF resumes
- anthropic: To interact with the Anthropic Claude API for AI-powered analysis
- tqdm: For displaying progress bars during processing
- termcolor: To add colored output in the console
- json5: For parsing JSON-like data with added flexibility
- requests: To make HTTP requests for fetching website content
- beautifulsoup4: For parsing HTML content from personal websites
- openai: To interact with the OpenAI API for AI-powered analysis
- pydantic: For data validation and settings management using Python type annotations
To install these packages, you can use pip:
```bash
pip install PyPDF2 anthropic openai tqdm termcolor json5 requests beautifulsoup4 pydantic
```
## Star History
