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AI Resume Analyzer

This project is an AI-powered Resume Analyzer that uses Hugging Face’s Named Entity Recognition (NER) pipeline to extract and categorize key information from resumes. It simplifies the recruitment process by identifying skills, certifications, experiences, and more from uploaded documents.

Features

  • Key Information Extraction: Identifies entities such as skills, organizations, locations, and certifications.
  • Multiple File Formats: Supports PDF and plain text resumes.
  • Organized Results: Categorizes extracted data for a clean and structured display.

Tech Stack

Backend

  • Python with Flask for serving the API.
  • Hugging Face Transformers for Named Entity Recognition.

AI Model

  • Hugging Face’s dslim/bert-base-NER for entity extraction.

Installation and Setup

Clone the Repository

Start by cloning the repository from GitHub:

git clone https://github.com/allanninal/ai-resume-analyzer.git
cd ai-resume-analyzer

Backend Setup

  1. Create and activate a virtual environment:

    python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

  2. Install the dependencies from requirements.txt:

    pip install -r requirements.txt

  3. Run the Flask server:

    python app.py

Usage

  1. Upload Resume: Upload resumes in PDF or plain text format.
  2. Analyze Results: View categorized and extracted information such as skills, organizations, and certifications.
  3. Streamline Processes: Use the structured data for recruitment or resume optimization.

How It Works

  1. The uploaded resume text is extracted (if PDF) or processed directly (if plain text).
  2. Hugging Face’s dslim/bert-base-NER model identifies and categorizes key entities in the text.
  3. Results are displayed in a structured and clean format.

What’s Next?

  1. Custom Models: Fine-tune NER models for resume-specific data.
  2. Job Description Matching: Analyze resumes against job descriptions to identify gaps.
  3. Summarization: Generate concise summaries of resumes focusing on achievements.
  4. Multi-Language Support: Extend functionality to support multilingual resumes.
  5. Export Options: Allow users to export analysis results in JSON, PDF, or Excel.

License

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

Support

If you find this project helpful, consider supporting me on Ko-fi:
ko-fi.com/allanninal