CVScore AI - Intelligent Resume Analysis & Ranking System

CVScore AI is an intelligent resume analysis tool designed to speed up and optimize recruitment processes. Using AI (Google Gemini), it analyzes uploaded resumes (in PDF or image format) according to specified job descriptions and custom criteria, scores them, ranks them, and provides a detailed report about the candidate.

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✨ Key Features

• Multi-File Support: Analyze multiple resumes (PDF, PNG, JPG) simultaneously.
• Dynamic Criteria: Add custom titles, descriptions, and advanced criteria (must-have, nice-to-have, etc.) for each job position.
• AI-Powered Analysis: Deep analysis of each candidate's resume using the Google Gemini 1.5 Flash model.
• Smart Scoring: Assigns a fair and context-aware score between 0-100 based on a predefined scoring guide.
• Strengths & Weaknesses: Automatically lists the candidate's advantages and missing points for the position.
• Custom Interview Questions: Generates specific interview questions targeting the candidate’s missing qualifications.
• Candidate Ranking: Sorts all candidates from highest to lowest score.
• User-Friendly Interface: Built with Streamlit, offering a simple, modern, and interactive web UI.

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🛠️ Technologies Used

• Frontend: Streamlit
• AI & Language Model: Google Gemini 2.5 Flash
• AI Orchestration: LangChain
• PDF Processing: PyMuPDF (fitz)
• Image Processing (OCR): Pillow & Google Tesseract
• Language: Python 3.9+

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⚙️ Installation & Setup

Prerequisites

• Python 3.9 or higher: Download Python
• Tesseract OCR Engine: Required to extract text from image-based resumes.
  Tesseract Installation Guide

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Step-by-Step Installation

1. Clone the Project:

git clone https://github.com/your-username/cv-ai-analyzer.git
cd cv-ai-analyzer

Create and Activate Virtual Environment (Recommended):

Windows

python -m venv venv
.\venv\Scripts\activate

macOS / Linux

python3 -m venv venv
source venv/bin/activate
Install Dependencies:

pip install -r requirements.txt

Note: If requirements.txt does not exist, create it with pip freeze > requirements.txt.

Set Up the API Key:

Create a .env file in the project root directory and add your Google AI Studio Gemini API key:

GOOGLE_API_KEY="PASTE_YOUR_API_KEY_HERE"

IMPORTANT: .env contains sensitive information; never commit it to Git!

Run the Application:

streamlit run app.py

Open the local web address in your browser to start using the application.

📁 Project Structure

cv-ai-analyzer/
│
├── lib/
│   ├── __init__.py
│   ├── cv_parser.py        # Extracts raw text from PDF and image resumes
│   └── ai_handler.py       # Manages AI analysis with LangChain and Gemini
│
├── app.py                  # Streamlit UI code
├── requirements.txt        # Python dependencies
├── .env                    # Stores API key (excluded from Git)
└── README.md               # This file

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.