---
title: AI Interview Mentor
emoji: 🎯
colorFrom: indigo
colorTo: purple
sdk: docker
pinned: false
---

# AI Interview Mentor

**Live at → https://adesh01-ai-interview-mentor.hf.space**

An AI-powered mock interview platform for students and instructors. Instructors upload question banks, students take adaptive AI interviews and receive scored feedback.

## Demo Credentials

| Role | Email | Password |
|---|---|---|
| Instructor | adeshboudh16@gmail.com | Adesh@123 |
| Student | adesh@gmail.com | adesh@123 |

## Features

- **Instructor portal** — create batches, invite students via class code, manage topics, upload question banks via CSV, lock/unlock topics
- **Student interviews** — AI asks up to 5 questions per session, adapts with follow-up questions on shallow answers, randomised question order each time
- **Scored reports** — LLM judge evaluates the full Q&A transcript and produces a score (0–100), summary, and improvement tips
- **Gemini + Groq fallback** — Gemini 2.0 Flash as primary LLM, Groq Llama 3.3 70B as automatic fallback
- **Persistent sessions** — LangGraph + PostgreSQL checkpointing resumes interrupted interviews

## How It Works

1. Instructor signs up, creates a batch, and shares the class code
2. Instructor uploads a CSV question bank and unlocks topics for interviews
3. Student signs up with the class code and picks a topic to interview on
4. AI conducts up to 5 turns — asking, evaluating, and probing deeper on weak answers
5. Interview ends with an inline score and summary; full history saved to the dashboard

## Tech Stack

| Layer    | Technology                                                 |
| -------- | ---------------------------------------------------------- |
| Frontend | React 19, TypeScript, Tailwind CSS v3, Zustand             |
| Backend  | FastAPI, LangGraph, asyncpg                                |
| Database | NeonDB (PostgreSQL + JSONB)                                |
| LLM      | Gemini 2.0 Flash (primary) / Groq Llama 3.3 70B (fallback) |
| Auth     | Custom JWT (HS256, 15 min access + 7 day refresh)          |
| Deploy   | Docker on Hugging Face Spaces (port 7860)                  |

## Environment Variables

Set these as secrets in HF Spaces → Settings → Variables and secrets:

| Variable         | Description                                                                              |
| ---------------- | ---------------------------------------------------------------------------------------- |
| `NEON_DB_URL`    | NeonDB PostgreSQL connection string                                                      |
| `JWT_SECRET`     | Random secret, min 32 chars (`python -c "import secrets; print(secrets.token_hex(32))"`) |
| `GEMINI_API_KEY` | Google AI Studio API key                                                                 |
| `GROQ_API_KEY`   | Groq API key (fallback LLM)                                                              |
| `APP_URL`        | Deployed Space URL, e.g. `https://adesh01-ai-interview-mentor.hf.space`                  |

## Local Development

```bash
make install      # install all backend + frontend deps
make schema       # apply DB schema (run once on fresh DB)
make dev          # start backend on :7860 with auto-reload
make frontend     # start frontend dev server on :5173
make test         # run pytest suite
```

## Docker

```bash
make build        # build frontend + Docker image
make docker-run   # run container with .env on localhost:7860

# Reset DB and re-apply schema (wipes all data + checkpoints)
make clean-db
```

> **CSV upload** — use the Instructor portal → Upload page. Format: `question_text,difficulty` (difficulty: easy / medium / hard). A sample file is at `docs/question_bank/linear_regression.csv`.

---

## Future Scope

The current version is a working MVP. A full-scale production version would include:

### 1. Text-to-Speech & Speech-to-Text
Replace the text chat with a real voice interview experience. The AI interviewer speaks questions aloud (TTS via ElevenLabs or Google Cloud TTS), and students respond by speaking (STT via Whisper or Google Speech-to-Text). This makes the interview feel closer to an actual job interview and removes typing as a bottleneck for non-native speakers.

### 2. Camera Monitoring
Integrate webcam monitoring using computer vision (MediaPipe or AWS Rekognition) to detect:
- Eye movement and gaze direction (focus/distraction signals)
- Facial expressions (confidence, hesitation)
- Tab-switching or browser focus loss

Results feed into the report as a "behaviour score" alongside the content score.

### 3. Dynamic Report Generation at Scale
The current report is a single LLM call on a 5-turn transcript. At scale this would become:
- A multi-agent report pipeline — one agent scores content, one scores communication, one compares against role benchmarks
- Historical trend charts (score over multiple attempts)
- Peer percentile ranking within a batch
- Instructor analytics aggregated across hundreds of students

### 4. Code Editor Support
For technical interviews (DSA, system design), embed an in-browser code editor (Monaco Editor / CodeMirror) with:
- Live code execution (sandboxed via Piston API or Judge0)
- AI reviewing both the verbal explanation and the submitted code
- Support for 20+ languages with syntax highlighting and test case runner

### 5. Multi-Domain Scaling
The current question bank is CSV-uploaded per topic. At scale this becomes a curated, versioned question library across domains:
- **Software Engineering** — Java, Python, Go, DevOps, Kubernetes, AWS
- **Data Science** — ML, Statistics, SQL, Spark
- **Management** — Product Management, Business Analysis, Finance
- AI-generated question variations to prevent question leakage between students

---

## Scaling to Industry Level — Cost Estimate

| Component          | Current (MVP)           | Industry Scale (10k DAU)                                     |
| ------------------ | ----------------------- | ------------------------------------------------------------ |
| Hosting            | HF Spaces (free)        | AWS ECS / GCP Cloud Run — ~$300–800/mo                       |
| Database           | NeonDB free tier        | NeonDB Business / RDS PostgreSQL — ~$200–500/mo              |
| LLM (interviews)   | Gemini free / Groq free | Gemini Flash API at scale — ~$0.075/1M tokens → ~$150–400/mo |
| LLM (reports)      | Same                    | GPT-4o or Claude Sonnet for judge calls — ~$100–300/mo       |
| TTS/STT            | —                       | ElevenLabs + Whisper API — ~$200–600/mo                      |
| Camera AI          | —                       | AWS Rekognition or custom CV model — ~$100–300/mo            |
| CDN / Storage      | —                       | CloudFront + S3 for recordings — ~$50–150/mo                 |
| **Total estimate** | **~$0**                 | **~$1,100–3,050/mo**                                         |

**Key scaling decisions:**
- Move from a single Docker container to microservices — interview engine, report engine, and media processing run independently and scale separately
- Use a message queue (Redis / SQS) between the interview API and the LLM workers to handle burst traffic without dropping requests
- Separate read replicas for analytics queries so instructor dashboards don't compete with live interview sessions
- Rate-limit LLM calls per student to control cost — a hard budget cap per institution is essential at B2B scale