🧠 IntelliView – AI-Powered Interview Prep & Resume Analyzer

IntelliView is a backend-heavy, AI-powered job preparation platform that enables users to simulate mock interviews, get real-time feedback, and evaluate resumes through ATS scoring. Built with a robust Python backend using Flask and integrated with Gemini AI (Google Generative AI), IntelliView empowers users to improve their interview performance and optimize resumes for applicant tracking systems.

---

🚀 Live Deployment

Hosted on AWS EC2 with persistent server processes managed via tmux.
🔗 https://3d50-16-16-183-86.ngrok-free.app

---

🎯 Core Features

• 🎤 Mock Interview Simulator
  Users can take AI-driven interviews with real-time guidance and feedback.

• 📄 ATS Resume Analyzer
  Upload a resume in PDF format to receive an ATS compatibility score with actionable suggestions.

• 📊 Performance History
  Track interview performance history with detailed breakdowns.

• 🌐 Responsive UI
  Fully responsive, minimalistic frontend that works across all devices.

---

🧠 AI & NLP Capabilities

• Gemini AI (Google Generative AI) is used to:

  • Generate mock interview questions
  • Evaluate user answers
  • Suggest improvements

• PDF Parsing & Resume Scoring:

  • Extracts skills and content using PyMuPDF
  • Checks format, keyword match, and ATS compliance

---

📁 Project Structure

IntelliView/
├── web_app/
│   ├── static/
│   │   ├── assets/                # Images and icons
│   │   └── css/                   # Custom stylesheets
│   ├── templates/                # Rendered HTML templates via Flask
│   │   ├── ats_score.html
│   │   ├── history.html
│   │   ├── history_list.html
│   │   ├── index.html
│   │   ├── interview.html
│   │   ├── profile.html
│   │   ├── settings.html
│   │   └── take-interview.html
│   ├── .env                      # Environment configuration
│   ├── .dockerignore
│   ├── .gitignore
│   ├── main.py                   # Flask backend server (entry point)
│   ├── requirements.txt          # Python dependencies
│   ├── run.sh                    # Shell script to start the app
│   ├── setup.sh                  # Setup script for deployment
│   ├── runtime.txt

---

⚙️ Technologies Used

🔧 Backend

• Python 3
• Flask – RESTful web framework
• Gemini AI (Google Generative AI) – for generating interview feedback and questions
• PyMuPDF – to extract text and structure from uploaded PDF resumes
• dotenv – to manage secret keys and environment variables
• Werkzeug – for request handling and file upload security

🌐 Frontend

• HTML5, CSS3, Vanilla JavaScript
• Dynamic rendering via Flask templating with .html files inside /templates
• Clean, responsive layout with mobile support

🖥️ Deployment & DevOps

• AWS EC2 – app hosting
• tmux – background session management for running persistent backend
• Shell scripting (run.sh, setup.sh) – automation for setup and server start
• Git + GitHub – version control

---

🛠️ Local Development Setup

✅ Prerequisites

• Python 3.8+
• Git
• pip
• Virtualenv (recommended)

📦 Installation

# Clone the repo
git clone https://github.com/algo-aryan/intelliview.git
cd intelliview/project/web_app

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
./setup.sh

# Set up environment variables
cp .env.example .env   # Then add Gemini API key and other secrets

# Run the Flask app
python3 web_app/main.py

---

🖥️ Deployment Guide (AWS EC2 + tmux)

# SSH into EC2
ssh ubuntu@<your-ec2-ip>

# Navigate to project directory
cd /home/ubuntu/intelliview/project/web_app

# Pull latest changes
git checkout fresh-start
git pull origin fresh-start

# Activate virtualenv
source .venv/bin/activate

# Install/update dependencies
./setup.sh

# Start app inside tmux
tmux new -s intelliview
python3 web_app/main.py

# Detach safely (CTRL + B, then D)

---

🧪 Sample Environment Variables (.env)

GOOGLE_API_KEY=your_gemini_api_key_here
FLASK_SECRET_KEY=your_flask_secret
UPLOAD_FOLDER=uploads/

---

🧼 Shell Scripts

• setup.sh: Initial setup, installs Python packages, sets up venv, and prepares environment.
• run.sh: Executes main.py inside activated venv, used for starting the server post-deployment.

---

📬 Contact

For queries or contributions, reach out via:

📧 aryan1509bansal@gmail.com 🔗 LinkedIn

---

👥 Team Members

• Tanisha Khanna 🔗 GitHub 🔗 LinkedIn

• Arnav Bansal 🔗 GitHub 🔗 LinkedIn

• Tushti Gupta 🔗 GitHub 🔗 LinkedIn

---

📃 License

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