JobMatch: ATS Resume Evaluator 💼

Welcome to JobMatch, an ATS (Application Tracking System) Resume Evaluator that leverages Google's Gemini Generative AI to analyze how well your resume matches a given job description and provides suggestions for improvement.

Table of Contents

• Overview
• Installation
• Environment Setup
• Usage
• Code Walkthrough
  • Streamlit Setup
  • Google Generative AI Integration
  • PDF Text Extraction
  • App Interface
• Contributing
• License

Overview

JobMatch is a web-based tool built using Streamlit and integrated with Google's Gemini Generative AI. The app allows users to upload their resumes in PDF format, input a job description, and receive an AI-generated evaluation that includes:

• Percentage match between the resume and the job description.
• Missing keywords required to improve the resume.
• Profile summary aligned with the job description.

Installation

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/i-ninte/ATS.git
   cd ATS

2. Install the required dependencies:

   pip install -r requirements.txt

Environment Setup

The project uses environment variables to store sensitive information, such as API keys. Follow these steps to set up your environment:

1. Create a .env file in the root directory.
2. Add your Google API Key to the .env file:

   GOOGLE_API_KEY=your_google_api_key

Usage

To start the app locally, run:

streamlit run app.py

Once the app is running, you can:

1. Enter a job description.
2. Upload your resume as a PDF file.
3. Click the Submit button to get your resume evaluation.

Code Walkthrough

Streamlit Setup

The Streamlit framework powers the UI of the application. The app is configured with a page title and icon:

st.set_page_config(
    page_title="JobMatch: ATS Resume Evaluator", 
    page_icon="💼", 
    layout="centered"
)

Google Generative AI Integration

We use the Google Generative AI API to process the input and provide a detailed analysis. The API is configured using an API key stored in environment variables:

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

The get_gemini_response function sends the user's resume and job description to the Gemini API for evaluation:

def get_gemini_response(input_text):
    try:
        model = genai.GenerativeModel("gemini-pro")
        response = model.generate_content(input_text)
        return response.text
    except Exception as e:
        st.error(f"Error fetching response from Gemini API: {e}")
        return None

PDF Text Extraction

The input_pdf_text function extracts the text from an uploaded PDF file using the PyPDF2 library:

def input_pdf_text(uploaded_file):
    try:
        reader = pdf.PdfReader(uploaded_file)
        text = ""
        for page in reader.pages:
            text += page.extract_text() or ""
        return text
    except Exception as e:
        st.error(f"Error reading PDF file: {e}")
        return None

App Interface

The user interface allows users to upload a resume, input the job description, and submit the data for evaluation. The key elements include:

1. Job Description Input:

   job_description = st.text_area("Enter the Job Description", height=150)

2. Resume Upload:

   uploaded_file = st.file_uploader("Upload your resume as a PDF", type=["pdf"])

3. Submit Button: Once the user inputs both the job description and resume, they can click the Submit button to trigger the evaluation:

   if st.button("Submit"):
       resume_text = input_pdf_text(uploaded_file)
       input_prompt = f"..."  # Generate prompt for API
       response = get_gemini_response(input_prompt)
       st.json(response)  # Display the AI response in JSON format

The results include the matching percentage, missing keywords, and a profile summary.

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request.