📄 Intelligent Resume Extractor

An AI-powered Resume Intelligence & Filtering system that converts unstructured resumes into structured, recruiter-ready data using LangChain, Google Gemini, and Streamlit.

Upload a ZIP file containing multiple resumes (PDF/DOCX), automatically extract key candidate details, and download the results as a CSV file for easy screening and analysis.

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🚀 Features

• 📦 Upload a ZIP file containing multiple resumes
• 📄 Supports PDF and DOCX resume formats
• 🤖 Uses Generative AI to understand resume content
• 📊 Extracts structured information:
  • Candidate Name
  • Professional Summary
  • Years of Experience
  • Skills
  • Important Links (GitHub, LinkedIn, Portfolio, etc.)
• 📥 Download extracted data as a CSV file
• 🎨 Modern and interactive UI built with Streamlit

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🧠 Why This Project?

Recruiters and HR teams often receive resumes in bulk, making manual screening:

• Time-consuming
• Inconsistent
• Error-prone

This project automates resume understanding and transforms unstructured documents into structured intelligence, enabling faster and smarter hiring decisions.

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🛠️ Tech Stack

• Python
• Streamlit – Web application UI
• LangChain – LLM orchestration
• Google Gemini (Generative AI) – Resume understanding
• TypedDict Structured Output – Schema-enforced extraction
• PyMuPDF – PDF text extraction
• Pandas – CSV generation
• Zipfile & OS – Bulk resume handling

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📂 Project Workflow

1. User uploads a ZIP file containing resumes
2. Each resume is read and converted into raw text
3. LLM extracts structured data using a fixed schema
4. All extracted data is aggregated into a CSV file
5. User downloads the CSV for filtering and analysis

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/ShubhamMohanty680/Intelligent_Resume_Extractor.git
cd intelligent-resume-extractor

2️⃣ Create Virtual Environment

python -m venv venv
venv\Scripts\activate  # On MAC: source venv/bin/activate 

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Environment Variables (Create a .env file and add)

GOOGLE_API_KEY=your_gemini_api_key

5️⃣ Run the Application

streamlit run app.py