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📄 JobFit: AI Resume Analyzer

An intelligent resume analyzer built with Python, Streamlit, and Google's Gemini AI. It evaluates your resume against a job description to provide a detailed review and an ATS-style match percentage report.

🚀 Features

✅ AI-Powered Analysis using Google Gemini ✅ Dual Analysis Modes: In-depth review and ATS match percentage ✅ PDF Resume Parsing (analyzes the first page) ✅ Interactive UI built with Streamlit for a seamless user experience ✅ Instant Resume Preview upon upload

🛠️ Tech Stack

  • Frontend: Streamlit
  • Backend: Python
  • AI Model: Google Gemini (gemini-1.5-flash-latest)
  • Libraries: google-generativeai, streamlit, PyMuPDF, Pillow

🧠 How It Works

Streamlit App (app.py)

  • The user uploads their PDF resume and pastes a job description into the text area.
  • The backend (input_pdf_setup) converts the first page of the PDF into a JPEG image.
  • This image, along with the job description and a specialized prompt, is sent to the Gemini API.
  • Two distinct prompts guide the AI: one for a detailed HR review and another for an ATS match report.
  • The AI-generated analysis is then displayed clearly in the user interface.

📦 Installation

1️⃣ Clone the Repository

git clone [https://github.com/your-username/jobfit-resume-analyzer.git](https://github.com/your-username/jobfit-resume-analyzer.git)
cd jobfit-resume-analyzer
### 2️⃣ Create a Virtual Environment (optional but recommended)

```bash
python -m venv env
source env/bin/activate  # On Windows use `env\Scripts\activate`

3️⃣ Install Dependencies

Create a requirements.txt file with the following content:

streamlit
google-generativeai
PyMuPDF
Pillow

Then run the installation command:

pip install -r requirements.txt

4️⃣ Add Your Google Gemini API Key

Create a folder named .streamlit in your project directory. Inside it, create a file named secrets.toml.

.
├── .streamlit/
│   └── secrets.toml
├── app.py
└── requirements.txt

Add your API key to secrets.toml:

# .streamlit/secrets.toml
GEMINI_API_KEY = "YOUR_API_KEY_HERE"

Finally, update app.py to use this secret:

# In app.py, change this line:
genai.configure(api_key=st.secrets["GEMINI_API_KEY"])

🔑 Get your API key here: Google AI Studio

📌 Usage

To run the Streamlit app:

streamlit run app.py

A web interface will open in your browser. Paste a job description, upload your PDF resume, and choose an analysis type.

📁 File Structure

.
├── app.py              # Main Streamlit application
├── requirements.txt    # Python dependencies
├── .streamlit/
│   └── secrets.toml    # API keys and secrets
└── README.md           # Project documentation

✨ Future Ideas

  • Process all pages of the resume, not just the first.
  • Add support for more file types like .docx and .txt.
  • Provide actionable suggestions to improve the resume based on the analysis.
  • Incorporate a database to save and track analysis history.
  • Deploy on Streamlit Community Cloud or Hugging Face Spaces.

🧑‍💻 Author

📜 License

This project is licensed under the MIT License.

⭐ Contribute & Support

If you liked this project, consider giving it a star ⭐ on GitHub! Pull requests, issues, and feature suggestions are always welcome.