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https://github.com/marx-wrld/resume-screener
A repo containing a resume screening software
https://github.com/marx-wrld/resume-screener
ai nlp-machine-learning
Last synced: 2 days ago
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A repo containing a resume screening software
- Host: GitHub
- URL: https://github.com/marx-wrld/resume-screener
- Owner: Marx-wrld
- License: gpl-3.0
- Created: 2023-10-24T16:25:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-19T16:04:21.000Z (11 months ago)
- Last Synced: 2023-12-20T19:04:08.639Z (11 months ago)
- Topics: ai, nlp-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 1.41 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Resume-screener
A repository containing a resume screening software build in Python & NLP.
- To run the app:-
```
streamlit run app.py
```
- Choosing the right people for your job is the biggest responsibility of every business, since, choosing the right set of people can accelerate business growth exponentially.
This is a simple web application built with Streamlit that serves as a "Resume Screener." The main functionality is to upload a resume file (either a text or PDF file) and use a pre-trained machine learning model to predict the category or job role associated with the content of the resume. The predictions are displayed in the web interface along with the predicted category name.
**Libraries Used:**
- streamlit: A Python library for creating web applications with minimal effort.
- pickle: Used for serializing and deserializing Python objects (here, it's used to load pre-trained machine learning models).
- re: The regular expression module for pattern matching and text manipulation.
- nltk: The Natural Language Toolkit for natural language processing tasks.
**Model Loading:**
- The code loads a pre-trained machine learning model (clf.pkl) and a TF-IDF vectorizer (tfidf.pkl). The model is a text classification model trained on resume data.
**Text Cleaning Function:**
- clean_resume(resume_text): This function takes a raw resume text and performs several cleaning steps, including removing URLs, mentions, special characters, non-ASCII characters, and extra whitespaces.
**Web Application Main Function:**
```
main(): This is the main function for the Streamlit web application.
```
- It displays a title "Resume Screener" and provides a file uploader widget (st.file_uploader) for users to upload their resumes.
- When a file is uploaded, it reads the contents of the file, cleans the text using the clean_resume function, and then uses the pre-trained model to make a prediction.
- The predicted category ID is displayed, and a mapping (category_mapping) is used to map the ID to a human-readable category name.
- The predicted category name is then displayed in the web application.
**Category Mapping:**
- category_mapping: A dictionary that maps category IDs to human-readable category names. This is used to interpret the model's predictions.
**Execution as a Web App:**
- The last part of the code checks whether the script is being run as the main program (if __name__ == '__main__':). If it is, it calls the main() function, starting the Streamlit web application.
