Resume Screening App

Welcome to the Resume Screening App, a machine learning-powered tool designed to automatically categorize resumes and provide relevant responsibilities and skills associated with the detected job role. This app is built using Python, Streamlit, and various machine learning libraries.

Features

• Upload Resumes: Supports PDF or text format.
• Text Cleaning: Automatic resume text cleaning and pre-processing.
• Job Category Prediction: Predicts the job category based on resume content.
• Responsibilities and Skills: Provides a list of expected responsibilities and relevant skills for the predicted category.
• Gemini API: Utilizes Gemini API for additional resume insights.

How to Use

1. Upload a Resume: Upload a resume file in either PDF or text format.
2. Category Prediction: The app will process the resume, predict its job category, and display it.
3. Responsibilities and Skills: You will receive a list of responsibilities and relevant skills associated with the predicted category.

Installation

Prerequisites

• Python 3.8 or above
• pip package manager

Dependencies

Install the required packages using the following command:

pip install -r requirements.txt

Run the App

1. Clone this repository:

git clone https://github.com/pfrimpong11/resume-screening-app.git

2. Navigate into the directory:

cd resume-screening-app

3. Set up your environment variables in a .env file. You will need an API key for Google Gemini: Contents of .env:

API_KEY=your_gemini_api_key

4. Run the app using Streamlit:

streamlit run app.py

5. Open your browser and navigate to the URL provided by Streamlit, typically http://localhost:8501

Folder Structure

resume-screening-app/
│
├── clf.pkl                   # Pre-trained classifier model (machine learning)
├── tfidf.pkl                 # TF-IDF vectorizer model
├── app.py                    # Main Streamlit application script
├── requirements.txt          # Python dependencies
├── README.md                 # This file
├── .env                      # Environment variables (API key for Gemini)
└── data/                     # Folder for sample resumes (optional)

How it Works

1. Text Preprocessing: The app cleans resume text by removing URLs, special characters, and unnecessary spaces.
2. Prediction Model: A machine learning classifier predicts the job category based on the text content of the resume. The model is pre-trained using TF-IDF vectorization.
3. Responsibilities & Skills: The app calls the Gemini API to generate a JSON response, which includes:

• Predicted job category
• Relevant responsibilities
• Relevant skills

4. Results Display: The app displays the predicted category, responsibilities, and relevant skills directly in the Streamlit interface.

Tech Stack

• Backend: Python, scikit-learn, Google Gemini API
• Frontend: Streamlit
• Text Processing: NLTK
• Machine Learning: TF-IDF, Pre-trained Classifier

Contributing

If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome. 1.

Fork the repository (https://github.com/pfrimpong11/resume-screening-app.git)

2. Create a feature branch:

git checkout -b feature/your-feature

3. Commit your changes:

git commit -am 'Add new feature'

4. Push to the branch:

git push origin feature/your-feature

5. Create a new Pull Request