📄 AI Powered Resume Analyzer Using BERT - Transformer

This project is an AI-powered resume analyzer that uses BERT (Bidirectional Encoder Representations from Transformers) to compare a candidate's resume with a job description and provide a similarity score. The application is built using Streamlit for a user-friendly interface.

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🚀 Features

• 📤 Upload your resume in PDF format
• 📝 Enter the job description text
• 🧠 Extracts and processes text from the resume
• 🔍 Uses BERT to generate embeddings for both resume and job description
• 📊 Computes similarity score based on cosine similarity
• 📈 Displays a matching percentage to help candidates assess their suitability

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🛠️ Installation

1️⃣ Clone the repository

git clone https://github.com/Ananthakrishnan12/Resume-Analyzer-Using-BERT.git
cd Resume-Analyzer-Using-BERT

2️⃣ Create a virtual environment (Optional but recommended)

python -m venv CV_parser
source CV_parser/bin/activate   # On macOS/Linux
CV_parser\Scripts\activate      # On Windows

3️⃣ Install dependencies

pip install -r requirements.txt

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📌 How to Run

Run the Streamlit app:

streamlit run main.py

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🏗️ Tech Stack

• Python 🐍
• Streamlit 🎨 (for UI)
• PyMuPDF (fitz) 📄 (for PDF text extraction)
• Transformers 🤗 (BERT-based embeddings)
• Torch (PyTorch) 🔥 (for embedding computations)

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🔍 How It Works

1. Resume Text Extraction

   • The uploaded PDF file is processed using PyMuPDF (fitz) to extract text.

2. BERT Embedding Generation

   • The extracted resume text and job description are tokenized and converted into embeddings using BERT.

3. Cosine Similarity Computation

   • The similarity score between resume and job description embeddings is calculated using cosine similarity.

4. Result Display

   • The application displays the extracted resume text (preview), job description, and matching score as a percentage.

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📷 DEMO (Wait few seconds for loading....)

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