AI Resume Analyzer

A Streamlit web app that analyzes resumes, estimates experience and resume quality, matches skills against a job description, and recommends learning paths based on the target role.

---

Features

• Upload a PDF resume and preview it inside the app.
• Parse key fields (name, email, phone, skills, education, companies, raw text) using pyresparser.
• Estimate total work experience in years with robust date‑range parsing.
• Compute an overall resume quality score with a category‑wise breakdown.
• Match extracted skills with a pasted job description and highlight missing skills.
• Recommend courses and learning paths based on target role and detected skills.
• Basic location info lookup using IP (best‑effort, optional).

---

Project Structure

AI-RESUME-ANALYZER/ ├─ .venv/ # Local virtual environment (not committed) ├─ pyresparser_local_backup/ # Backup of experimental pyresparser code (not used in app) │ ├─ init.py │ ├─ resume_parser.py │ └─ utils.py ├─ resume_utils/ # All custom helper logic actually used by the app │ ├─ init.py │ └─ helpers.py ├─ Uploaded_Resumes/ # Sample resumes / user uploads (safe to delete in repo) │ ├─ Aditya Singh - Resume.pdf │ └─ Harsh Sharma - Resume.pdf ├─ App.py # Main Streamlit application ├─ Courses.py # Course lists used for recommendations ├─ requirements.txt # Python dependencies └─ README.md

text

The running app imports helper functions only from resume_utils/helpers.py and uses the pip‑installed pyresparser for ResumeParser. The pyresparser_local_backup folder is kept only as reference and is not imported by App.py.

---

Tech Stack

• Language: Python
• Web UI: Streamlit
• NLP & Parsing: pyresparser, spaCy, NLTK, pdfminer.six, docx2txt
• Data: pandas, numpy
• Others: plotly, geocoder

---

Setup & Installation

1. Clone the repository

git clone https://github.com//AI-Resume-Analyzer.git cd AI-Resume-Analyzer

text

2. Create and activate a virtual environment (recommended)

python -m venv .venv

Windows .venv\Scripts\activate

Linux / macOS source .venv/bin/activate

text

3. Install dependencies

pip install -r requirements.txt

text

4. Download NLP models / data required by pyresparser

python -m spacy download en_core_web_sm python -m nltk.downloader words

text

5. Run the Streamlit app

streamlit run App.py

text

Then open the local URL shown in the terminal (typically http://localhost:8501).

---

Usage

• On the main page, upload a PDF resume.
• Optionally paste a job description and choose a target role from the sidebar.
• View:
  • Extracted profile information (name, contact, degrees, institutes).
  • Total experience, skills, companies, degrees, and other metrics.
  • Overall resume score and textual feedback.
  • JD match percentage, matched skills, and missing skills.
  • Recommended courses for the selected role.

Sample resumes are provided in the Uploaded_Resumes/ folder for testing; you can delete them in your public repo if they contain real personal data.

---

Notes & Credits

• Resume parsing is powered by pyresparser and its dependencies (spaCy, NLTK, pdfminer).
• The pyresparser_local_backup/ directory contains experimental/local copies of parser code kept only for reference and is not used by App.py at runtime.
• Feel free to extend the scoring logic, add more role‑specific recommendations, or connect this app to a database for admin/analytics features.