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https://github.com/kishyassin/resume-filtering-with-python
This project streamlines the recruitment process by automating the analysis and selection of candidates. It combines Python-based automation, advanced text extraction, and language model processing to deliver a comprehensive dashboard for candidate evaluation.
https://github.com/kishyassin/resume-filtering-with-python
Last synced: 1 day ago
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This project streamlines the recruitment process by automating the analysis and selection of candidates. It combines Python-based automation, advanced text extraction, and language model processing to deliver a comprehensive dashboard for candidate evaluation.
- Host: GitHub
- URL: https://github.com/kishyassin/resume-filtering-with-python
- Owner: kishyassin
- License: mit
- Created: 2024-12-26T21:21:44.000Z (19 days ago)
- Default Branch: main
- Last Pushed: 2024-12-26T23:20:42.000Z (19 days ago)
- Last Synced: 2024-12-26T23:22:38.469Z (19 days ago)
- Language: Python
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Resume-Filtering-with-python
[](https://resume-filter.streamlit.app)
This project streamlines the recruitment process by automating the analysis and selection of candidates. It combines Python-based automation, advanced text extraction, and language model processing to deliver a comprehensive dashboard for candidate evaluation.
## Features
- Extract text from PDF resumes
- Convert extracted text to structured JSON format
- Filter candidates by tags
- Generate PDF reports of selected candidates
- Send recruitment messages to selected candidates
## Prerequisites
- Python 3.12
- Required Python packages (listed in `requirements.txt`)
## Installation
1. Clone the repository:
```sh
git clone https://github.com/kishyassin/Resume-Filtering-with-python.git
cd Resume-Filtering-with-python
```
2. Install the required packages:
```sh
pip install -r requirements.txt
```
## Configuration
1. Open the [configuration.py](http://_vscodecontentref_/0) file and add your API key, email, and password:
```python
api_key = "your_grok_ai_api_key"
email = "[email protected]"
password = "your_password"
```
## Usage
1. Place your CVs in PDF format in the [Banque-CV](http://_vscodecontentref_/1) folder.
2. Run the [pdf-to-text-to-json.py](http://_vscodecontentref_/2) script to extract text from the PDFs and convert it to JSON format:
```sh
python pdf-to-text-to-json.py
```
3. Run the [dashboard.py](http://_vscodecontentref_/3) script to start the Streamlit dashboard:
```sh
streamlit run dashboard.py
```
## Project Structure
- [Banque-CV](http://_vscodecontentref_/4): Folder to store CVs in PDF format.
- [banque-cv.json](http://_vscodecontentref_/5): JSON file containing extracted and structured CV data.
- [configuration.py](http://_vscodecontentref_/6): Configuration file for API key, email, and password.
- [dashboard.py](http://_vscodecontentref_/7): Streamlit dashboard for managing and filtering CVs.
- [functions_logic](http://_vscodecontentref_/8): Folder containing logic for various functions.
- [extract_json_from_text.py](http://_vscodecontentref_/9): Extracts JSON data from text using Groq AI.
- [extract_text_from_pdf.py](http://_vscodecontentref_/10): Extracts text from PDF files.
- [filter_by_tags.py](http://_vscodecontentref_/11): Filters CVs by tags.
- [generate_pdf.py](http://_vscodecontentref_/12): Generates PDF reports of selected CVs.
- [send_recruitment_message.py](http://_vscodecontentref_/13): Sends recruitment messages via email.
- [LICENSE](http://_vscodecontentref_/14): License file.
- [pdf-to-text-to-json.py](http://_vscodecontentref_/15): Script to process CVs from PDF to JSON.
- [README.md](http://_vscodecontentref_/16): This file.
- [requirements.txt](http://_vscodecontentref_/17): List of required Python packages.
## License
This project is licensed under the MIT License - see the [LICENSE](http://_vscodecontentref_/18) file for details.
## Acknowledgments
- Groq AI for text extraction and language model processing.
- Streamlit for the interactive dashboard.
- PyPDF2 for PDF text extraction.
- FPDF for PDF generation.