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🧠 InView

An AI-powered interview assistant to help you ace your next opportunity — built in 36 hours at WildHacks 2025 by Northwestern University.

🔗 Devpost Submission


💡 Overview

InView is a smart, real-time interview assistant that simulates technical interviews, evaluates your answers, and provides emotion-based feedback. It's designed to help job seekers build confidence, improve communication, and get actionable insights into their performance.


🎯 Features

  • AI-Generated Interview Questions
    Automatically tailored to the job description or role (e.g., React Developer, Data Scientist).

  • Real-time Answer Evaluation (Gemini API)
    Evaluates responses with detailed scoring, feedback, and suggestions.

  • Live Emotion Detection via Webcam (FER)

    • Analyzes facial expressions frame-by-frame.
    • Tracks stress level, confusion, confidence, focus, and presence.
  • Post-Interview Summary Dashboard

    • Emotion metrics (stress/confidence/confusion/focus).
    • AI-based technical evaluation.
    • Replay session and review answers.
  • Custom JD Input + Role Selector Choose predefined job roles or paste your own job description.

  • Privacy-First Approach
    All video processing is local unless the user chooses to upload.


🔧 Tech Stack

Layer Technology
Frontend React.js, MUI (Material UI)
Backend FastAPI (Python)
AI/ML APIs Google Gemini API, FER Library
Video/Emotion WebRTC, MediaRecorder API, OpenCV
Database (Planned) MongoDb Atlas

🛠 How It Works

  1. User selects a job role or enters a custom JD
  2. LLM (Gemini) generates tailored technical questions
  3. User answers in text while webcam records live video
  4. Emotion snapshots captured every second via FER
  5. After interview ends:
    • Answers are evaluated by Gemini
    • Emotion data analyzed
    • Final feedback is displayed with insights and scores

👨‍💻 Team InView

  • Mohammed Saalim Kartapillai
  • Derick Johnson
  • Eric Somogyi
  • Salvador Ortiz

🚀 Try It Locally

# Frontend
cd interview-ai-frontend
npm install
npm start

# Backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload

Backend runs on http://localhost:8000
Frontend runs on http://localhost:3000

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