AI-Powered Resume Analyzer & Interview Preparation System

An intelligent web application that analyzes resumes using a Retrieval-Augmented Generation (RAG) approach and generates personalized technical and behavioral interview questions. This project helps users prepare efficiently for interviews by combining LLM-based reasoning with real-world interview question sources.

Problem Statement:

Interview preparation is often generic and time-consuming. Candidates struggle to identify which skills to focus on and which questions are most relevant to their profiles.

This system solves the problem by:

Automatically analyzing resumes

Extracting relevant skills using AI

Generating tailored interview questions

Providing an interactive chatbot for practice and feedback

Features

Resume Upload & Parsing

Supports PDF and DOCX resumes

Automatic text extraction

AI-Based Skill Extraction

Uses Google Gemini LLM

Identifies top technical and soft skills

Retrieval-Augmented Generation (RAG):

Combines LLM output with real interview questions

Scrapes data from trusted platforms

Multi-Source Question Retrieval

Technical questions from:

GeeksforGeeks

InterviewBit

Behavioral questions from:

The Muse

Context-Aware Interview Chatbot

Remembers extracted skills

Generates follow-up questions

Provides feedback on answers

User-Friendly Web Interface

Built with Streamlit

Session-based interaction handling

Tech Stack

Programming Language: Python

Frontend / UI: Streamlit

Large Language Model (LLM): Google Gemini (gemini-2.0-flash)

AI Architecture: Retrieval-Augmented Generation (RAG)

Resume Parsing: PyPDF, python-docx

Web Scraping & Retrieval: BeautifulSoup, Requests

Prompt Engineering: Skill extraction and context-aware querying

Session Management: Streamlit Session State

Environment Management: python-dotenv

APIs: Google Generative AI API

System Workflow

User uploads resume (PDF/DOCX)

Resume text is extracted

Gemini LLM analyzes resume and extracts key skills

Relevant interview questions are retrieved from multiple sources

Questions are aggregated using RAG

Context-aware chatbot is initialized

User interacts with AI for interview preparation

Architecture Diagram

flowchart TD
    A[User uploads resume] --> B[Resume text extraction]
    B --> C[Skill extraction using Gemini LLM]
    C --> D[Identified skills]

    D --> E[Technical question retrieval]
    D --> F[Behavioral question retrieval]

    E --> G[RAG aggregation layer]
    F --> G

    G --> H[Context aware chatbot]
    H --> I[Streamlit web interface]

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Installation & Setup

Clone the Repository: git clone https://github.com/MS123-D/AI-ResumeAnalyzer.git cd ai-resume-analyzer

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file in the project root:

GOOGLE_API_KEY=your_google_gemini_api_key

Run the Application

streamlit run app.py

Usage

Launch the Streamlit app

Upload your resume (PDF or DOCX)

Click Analyze Resume

View extracted skills and initial interview questions

Use the chatbot to:

Ask more questions

Practice answers

Get feedback

Future Enhancements

Answer evaluation with scoring

Resume improvement suggestions

Skill gap analysis

Offline question datasets

Multi-language resume support

Acknowledgements

Google Gemini API

GeeksforGeeks

InterviewBit

The Muse