AI Interview Wizard (interviewi)

An intelligent, multi-page technical interview simulator and ATS resume scanner powered by Google Gemini AI.

This application provides two main features:

1. Interview Simulator: Generate customized interview questions based on your target role, industry, and skills/experience (optionally grounded in a job description). Answer them one at a time in a wizard, with background AI evaluation and comprehensive feedback.
2. ATS Resume Scanner: Upload a PDF resume and paste a job description to get an ATS compatibility score, keyword analysis, and actionable improvement suggestions.

🌟 Features

Interview Simulator

• AI-Powered Question Generation: Generates relevant technical questions using Google Gemini (default gemini-2.5-flash, configurable via GEMINI_MODEL).
• Interactive Multi-Page Wizard: Focused, one-question-at-a-time user experience with progress tracking.
• Background Evaluation: Non-blocking architecture evaluates answers asynchronously as the user progresses.
• Rich Results Dashboard: Animated circular score charts and detailed feedback (strengths and areas for improvement).

ATS Resume Scanner

• PDF Resume Upload: Drag-and-drop or click-to-upload with validation (2MB max, 50-page limit).
• ATS Compatibility Scoring: Overall score (0-100) with category breakdowns (keywords, skills, experience, format).
• Keyword Analysis: Shows matched and missing keywords from the job description.
• Improvement Suggestions: AI-powered actionable recommendations to improve resume-JD match.

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🏗️ Architecture

The project is structured as a mono-repo divided into two main applications:

• interviewi-api (Backend): Python Flask API utilizing SQLAlchemy (SQLite/PostgreSQL) and LangChain for AI integration.
• web (Frontend): Angular 16 Single Page Application with custom CSS, glassmorphism UI, and RxJS state management.

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 18+ & npm
• Google Gemini API Key

1. Backend Setup (interviewi-api)

cd interviewi-api
python -m venv venv
# Windows: .\venv\Scripts\Activate
# Mac/Linux: source venv/bin/activate

pip install -r requirements-dev.txt   # runtime + dev deps (pytest, ruff)

# Create your environment file
cp .env.example .env
# Edit .env and add your GOOGLE_API_KEY (GEMINI_MODEL is optional)

# Start the Flask server (runs on http://localhost:5000)
python app.py

2. Frontend Setup (web)

cd web
npm install

# Start the Angular development server (runs on http://localhost:4200)
npm start

3. Tests & lint

# Backend (from interviewi-api/)
python -m pytest -q        # 104 tests
python -m ruff check .

# Frontend (from web/)
npm run test:ci             # Karma headless
npm run lint
npm run build               # production build

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📖 Documentation

Detailed documentation about the architecture and implementation flows can be found in the root directory:

• IMPLEMENTATION_SUMMARY.md: Overview of the wizard flow refactor and ATS scanner.
• WIZARD_FLOW_DOCUMENTATION.md: Deep dive into the multi-page logic.
• FEATURE_DOCUMENTATION.md: Feature specs for answer submission and ATS scanner.
• QUICK_START.md: Additional commands and routing information.